Support `tuple` return type from model `pre` and update test to use this #890

oliverholworthy · 2022-11-16T16:21:37Z

Relates to #889

Goals ⚽

Support SequenceTransform layers passed to pre arg of model.evaluate and model.predict.

Implementation Details 🚧

Handles tuple return type from pre layer in model.evaluate and model.predict
- this is already supported in model.fit

Testing Details 🔍

Update test_transformer_with_causal_language_modeling to use pre arg instead of loader transform.
- while [BUG] Incorrect Input Signature of Saved Model with Ragged Features and Loader transform #889 remains an issue, this approach doesn't produce a model that is easy to serve.

nvidia-merlin-bot · 2022-11-16T16:53:01Z

Click to view CI Results

GitHub pull request #890 of commit 94b28550dec599d5cefe1670a7aa459772baf2ca, no merge conflicts.
Running as SYSTEM
Setting status of 94b28550dec599d5cefe1670a7aa459772baf2ca to PENDING with url http://merlin-infra1.nvidia.com:8080/job/merlin_models/1859/ and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
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Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
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Checking out Revision 94b28550dec599d5cefe1670a7aa459772baf2ca (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 94b28550dec599d5cefe1670a7aa459772baf2ca # timeout=10
Commit message: "Update CLM transformer test  to use `pre` instead of Loader `transform`"
 > git rev-list --no-walk 6867055bae82c7eb12c2791323b439742f8c9478 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins17149803288144795477.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/3/merlin-models-0.9.0+46.g94b28550.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.10,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.10,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.0.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.5.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==5.0.4,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-dataloader==0.0.2,merlin-models==0.9.0+46.g94b28550,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,nvtabular @ git+https://github.com/NVIDIA-Merlin/NVTabular.git@ba4c14159a8e858c8998d4158a4376e65a8fa266,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.3,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.9.1,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==2.5.0,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,tensorflow-ranking==0.5.1,tensorflow-serving-api==2.9.2,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='1492503373'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-_kg19kog
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  Resolved https://github.com/NVIDIA-Merlin/core.git to commit aac1dc33cf92cd19f5a065fa7f49cf9b7500cfb2
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Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.8.0+12.gaac1dc3-py3-none-any.whl size=118622 sha256=8a6fb6b05afac1701240cf8a6ac54574bd9b066f7dcef2f407defb557c5016bb
  Stored in directory: /tmp/pip-ephem-wheel-cache-s7as_5xv/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.8.0+12.gaac1dc3
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-xrf_ou9u
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-xrf_ou9u
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit ba4c14159a8e858c8998d4158a4376e65a8fa266
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: merlin-core>=0.2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from nvtabular==1.6.0+4.gba4c1415) (0.8.0+12.gaac1dc3)
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Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+4.gba4c1415) (1.0.1)
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Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+4.gba4c1415) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+4.gba4c1415) (4.0.0)
Building wheels for collected packages: nvtabular
  Building wheel for nvtabular (pyproject.toml): started
  Building wheel for nvtabular (pyproject.toml): finished with status 'done'
  Created wheel for nvtabular: filename=nvtabular-1.6.0+4.gba4c1415-cp38-cp38-linux_x86_64.whl size=257596 sha256=7e29562d0b45376a7dac28c04050d8737331854090cdf4b584d6f15f795e813f
  Stored in directory: /tmp/pip-ephem-wheel-cache-95z0xlg_/wheels/8f/d9/f9/30f2cdc5bf8787fae6fdfe55afd6e1b493e619ec32c32ec40b
Successfully built nvtabular
Installing collected packages: nvtabular
  Attempting uninstall: nvtabular
    Found existing installation: nvtabular 1.1.1
    Not uninstalling nvtabular at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'nvtabular'. No files were found to uninstall.
Successfully installed nvtabular-1.6.0+4.gba4c1415
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
collected 878 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss.s                            [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ......                             [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  3%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_loader.py ................                            [  5%]

tests/unit/tf/test_public_api.py .                                       [  6%]

tests/unit/tf/blocks/test_cross.py ...........                           [  7%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [  8%]

tests/unit/tf/blocks/test_interactions.py ...                            [  8%]

tests/unit/tf/blocks/test_mlp.py ....................................... [ 13%]

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tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

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tests/unit/tf/blocks/retrieval/test_base.py .                            [ 22%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 22%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ............            [ 24%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 24%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 24%]

tests/unit/tf/core/test_aggregation.py .........                         [ 25%]

tests/unit/tf/core/test_base.py ..                                       [ 25%]

tests/unit/tf/core/test_combinators.py s.....................            [ 28%]

tests/unit/tf/core/test_encoder.py ..                                    [ 28%]

tests/unit/tf/core/test_index.py ...                                     [ 28%]

tests/unit/tf/core/test_prediction.py ..                                 [ 29%]

tests/unit/tf/core/test_tabular.py ......                                [ 29%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 29%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 29%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 30%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 30%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 30%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 30%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 30%]

tests/unit/tf/examples/test_usecase_accelerate_training_by_lazyadam.py . [ 30%]

[ 30%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 30%]

tests/unit/tf/examples/test_usecase_incremental_training_layer_freezing.py . [ 30%]

[ 30%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 30%]

tests/unit/tf/examples/test_usecase_retrieval_with_hpo.py .              [ 31%]

tests/unit/tf/horovod/test_horovod.py ...                                [ 31%]

tests/unit/tf/inputs/test_base.py .                                      [ 31%]

tests/unit/tf/inputs/test_continuous.py ........                         [ 32%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 36%]

........                                                                 [ 37%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 39%]

tests/unit/tf/layers/test_queue.py ..............                        [ 40%]

tests/unit/tf/losses/test_losses.py .......................              [ 43%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 43%]

tests/unit/tf/metrics/test_metrics_topk.py .........................     [ 46%]

tests/unit/tf/models/test_base.py s.........................             [ 49%]

tests/unit/tf/models/test_benchmark.py ..                                [ 50%]

tests/unit/tf/models/test_ranking.py ..................................  [ 53%]

tests/unit/tf/models/test_retrieval.py ................................. [ 57%]

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tests/unit/tf/outputs/test_base.py ......                                [ 63%]

tests/unit/tf/outputs/test_classification.py ......                      [ 63%]

tests/unit/tf/outputs/test_contrastive.py ..............                 [ 65%]

tests/unit/tf/outputs/test_regression.py ..                              [ 65%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 66%]

tests/unit/tf/outputs/test_topk.py .                                     [ 66%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 66%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 68%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 68%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 69%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 69%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 70%]

tests/unit/tf/transformers/test_block.py ......................          [ 72%]

tests/unit/tf/transformers/test_transforms.py ..........                 [ 73%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 74%]

tests/unit/tf/transforms/test_features.py s............................. [ 77%]

.......................s......                                           [ 80%]

tests/unit/tf/transforms/test_negative_sampling.py .........             [ 82%]

tests/unit/tf/transforms/test_noise.py .....                             [ 82%]

tests/unit/tf/transforms/test_sequence.py ....................           [ 84%]

tests/unit/tf/transforms/test_tensor.py ...                              [ 85%]

tests/unit/tf/utils/test_batch.py ....                                   [ 85%]

tests/unit/tf/utils/test_dataset.py ..                                   [ 85%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 86%]

tests/unit/torch/test_dataset.py .........                               [ 87%]

tests/unit/torch/test_public_api.py .                                    [ 87%]

tests/unit/torch/block/test_base.py ....                                 [ 88%]

tests/unit/torch/block/test_mlp.py .                                     [ 88%]

tests/unit/torch/features/test_continuous.py ..                          [ 88%]

tests/unit/torch/features/test_embedding.py ..............               [ 89%]

tests/unit/torch/features/test_tabular.py ....                           [ 90%]

tests/unit/torch/model/test_head.py ............                         [ 91%]

tests/unit/torch/model/test_model.py ..                                  [ 92%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 92%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 93%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 94%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 120 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 86 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 55 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_file2xcp1e6b.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/.local/lib/python3.8/site-packages/distributed/node.py:180: UserWarning: Port 8787 is already in use.

Perhaps you already have a cluster running?

Hosting the HTTP server on port 46055 instead

warnings.warn(
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147     14    90%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              169     62    63%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          244     50    80%

merlin/models/tf/core/combinators.py                                   426     53    88%

merlin/models/tf/core/encoder.py                                       175     28    84%

merlin/models/tf/core/index.py                                         104     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     29    90%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         60     10    83%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     32    93%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        762    104    86%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59      6    90%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     36    92%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      4    94%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     42    80%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      2    94%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11581   2355    80%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========= 865 passed, 13 skipped, 1439 warnings in 1771.15s (0:29:31) ==========

___________________________________ summary ____________________________________

py38-gpu: commands succeeded

congratulations :)

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins14084128108323102164.sh

nvidia-merlin-bot · 2022-11-18T22:31:16Z

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GitHub pull request #890 of commit 03a063d601177d9234ecc4d8e235230df877a58d, no merge conflicts.
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Checking out Revision 03a063d601177d9234ecc4d8e235230df877a58d (detached)
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Commit message: "Merge branch 'main' into clm-test-use-model-pre"
 > git rev-list --no-walk d4f335738102de0c8bfa1dafb08efb180093a92d # timeout=10
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Requirement already satisfied: traitlets>=5.2.2 in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (5.4.0)
Requirement already satisfied: jupyter_core in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (4.11.1)
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Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (2.16.1)
Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (22.1.0)
Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (1.3.10)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (5.9.0)
Requirement already satisfied: entrypoints in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (0.4)
Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (24.0.0)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (2.8.2)
Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/3/merlin-models-0.9.0+50.g03a063d6.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.12,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.12,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.0.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.5.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==5.0.4,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-dataloader==0.0.2,merlin-models==0.9.0+50.g03a063d6,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,nvtabular @ git+https://github.com/NVIDIA-Merlin/NVTabular.git@21117cfc4c113b30036afcb97b6daa5f377996db,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.3,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.9.1,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==2.5.0,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='481560990'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-yxjwhzjl
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/core.git /tmp/pip-req-build-yxjwhzjl
  Resolved https://github.com/NVIDIA-Merlin/core.git to commit b298635ce3991007a4961896e21779f6a45348e0
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+14.gb298635) (0.55.1)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+14.gb298635) (2022.5.0)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+14.gb298635) (2022.3.0)
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+14.gb298635) (4.64.1)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+14.gb298635) (1.10.0)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+14.gb298635) (7.0.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+14.gb298635) (21.3)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+14.gb298635) (1.3.5)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+14.gb298635) (2022.3.0)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+14.gb298635) (1.2.5)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+14.gb298635) (3.19.5)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.8.0+14.gb298635) (1.2.0)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.8.0+14.gb298635) (0.4.3)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core==0.8.0+14.gb298635) (5.4.1)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.8.0+14.gb298635) (2.2.0)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.8.0+14.gb298635) (0.12.0)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core==0.8.0+14.gb298635) (1.2.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (8.1.3)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (1.0.4)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (2.4.0)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (5.8.0)
Requirement already satisfied: tblib>=1.6.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/tblib-1.7.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (1.7.0)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (3.1.2)
Requirement already satisfied: tornado>=6.0.3 in ./.tox/py38-gpu/lib/python3.8/site-packages (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (6.2)
Requirement already satisfied: zict>=0.1.3 in /var/jenkins_home/.local/lib/python3.8/site-packages/zict-2.0.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (2.0.0)
Requirement already satisfied: numpy<1.22,>=1.18 in /var/jenkins_home/.local/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.8.0+14.gb298635) (1.20.3)
Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.8.0+14.gb298635) (65.5.1)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.8.0+14.gb298635) (0.38.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->merlin-core==0.8.0+14.gb298635) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.8.0+14.gb298635) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.8.0+14.gb298635) (2022.2.1)
Requirement already satisfied: absl-py<2.0.0,>=0.9 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.8.0+14.gb298635) (1.2.0)
Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.8.0+14.gb298635) (1.52.0)
Requirement already satisfied: locket in /var/jenkins_home/.local/lib/python3.8/site-packages/locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask>=2022.3.0->merlin-core==0.8.0+14.gb298635) (0.2.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core==0.8.0+14.gb298635) (1.15.0)
Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (1.0.1)
Requirement already satisfied: h2<5,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.8.0+14.gb298635) (4.1.0)
Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.8.0+14.gb298635) (6.0.2)
Requirement already satisfied: MarkupSafe>=2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from jinja2->distributed>=2022.3.0->merlin-core==0.8.0+14.gb298635) (2.0.1)
Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.8.0+14.gb298635) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.8.0+14.gb298635) (4.0.0)
Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.8.0+14.gb298635-py3-none-any.whl size=118651 sha256=c82bc523398b60a99500f5424b56ca009c9cd35309da4ef14eeefac90bb8e36e
  Stored in directory: /tmp/pip-ephem-wheel-cache-op909hku/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.8.0+14.gb298635
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-xl1vg3rd
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-xl1vg3rd
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit 21117cfc4c113b30036afcb97b6daa5f377996db
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
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Building wheels for collected packages: nvtabular
  Building wheel for nvtabular (pyproject.toml): started
  Building wheel for nvtabular (pyproject.toml): finished with status 'done'
  Created wheel for nvtabular: filename=nvtabular-1.6.0+5.g21117cfc-cp38-cp38-linux_x86_64.whl size=257596 sha256=c15f9f0e5c542f6bdf142fc82d407d264293bd6d0e3bc5882531c97ce4040541
  Stored in directory: /tmp/pip-ephem-wheel-cache-t_hxn8dx/wheels/8f/d9/f9/30f2cdc5bf8787fae6fdfe55afd6e1b493e619ec32c32ec40b
Successfully built nvtabular
Installing collected packages: nvtabular
  Attempting uninstall: nvtabular
    Found existing installation: nvtabular 1.1.1
    Not uninstalling nvtabular at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'nvtabular'. No files were found to uninstall.
Successfully installed nvtabular-1.6.0+5.g21117cfc
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
collected 879 items
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tests/unit/xgb/test_xgboost.py ....................                      [100%]
=================================== FAILURES ===================================

__________________ test_example_03_exploring_different_models __________________
self = <testbook.client.TestbookNotebookClient object at 0x7f15997f8dc0>

cell = {'cell_type': 'markdown', 'id': 'e75faa3b', 'metadata': {'pycharm': {'name': '#%% md\n'}}, 'source': 'When we work on ...we define our input and output paths. We will use the parquet files in the test folder to validate our trained model.'}

kwargs = {}, cell_indexes = [0, 1, 2, 3, 4, 5, ...]

executed_cells = [{'cell_type': 'code', 'execution_count': 2, 'id': '5f49a48e', 'metadata': {'pycharm': {'name': '#%%\n'}, 'execution':...d': '2dd02301', 'metadata': {'pycharm': {'name': '#%% md\n'}}, 'source': '## Feature Engineering with NVTabular'}, ...]

idx = 7
def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:


          cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)


../../../.local/lib/python3.8/site-packages/testbook/client.py:133:

args = (<testbook.client.TestbookNotebookClient object at 0x7f15997f8dc0>, {'cell_type': 'code', 'execution_count': 4, 'id': ...to_parquet(os.path.join(DATA_FOLDER, "train"))\n    valid.to_ddf().to_parquet(os.path.join(DATA_FOLDER, "valid"))'}, 7)

kwargs = {}
def wrapped(*args, **kwargs):


  return just_run(coro(*args, **kwargs))


../../../.local/lib/python3.8/site-packages/nbclient/util.py:84:

coro = <coroutine object NotebookClient.async_execute_cell at 0x7f154af555c0>
def just_run(coro: Awaitable) -> Any:
    """Make the coroutine run, even if there is an event loop running (using nest_asyncio)"""
    # original from vaex/asyncio.py
    loop = asyncio._get_running_loop()
    if loop is None:
        had_running_loop = False
        try:
            loop = asyncio.get_event_loop()
        except RuntimeError:
            # we can still get 'There is no current event loop in ...'
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
    else:
        had_running_loop = True
    if had_running_loop:
        # if there is a running loop, we patch using nest_asyncio
        # to have reentrant event loops
        check_ipython()
        import nest_asyncio

        nest_asyncio.apply()
        check_patch_tornado()


  return loop.run_until_complete(coro)


../../../.local/lib/python3.8/site-packages/nbclient/util.py:62:

self = <_UnixSelectorEventLoop running=False closed=False debug=False>

future = <Task finished name='Task-243' coro=<NotebookClient.async_execute_cell() done, defined at /var/jenkins_home/.local/lib...RuntimeError: CUDA error encountered at: ../src/bitmask/null_mask.cu:93: 2 cudaErrorMemoryAllocation out of memory\n')>
def run_until_complete(self, future):
    """Run until the Future is done.

    If the argument is a coroutine, it is wrapped in a Task.

    WARNING: It would be disastrous to call run_until_complete()
    with the same coroutine twice -- it would wrap it in two
    different Tasks and that can't be good.

    Return the Future's result, or raise its exception.
    """
    self._check_closed()
    self._check_running()

    new_task = not futures.isfuture(future)
    future = tasks.ensure_future(future, loop=self)
    if new_task:
        # An exception is raised if the future didn't complete, so there
        # is no need to log the "destroy pending task" message
        future._log_destroy_pending = False

    future.add_done_callback(_run_until_complete_cb)
    try:
        self.run_forever()
    except:
        if new_task and future.done() and not future.cancelled():
            # The coroutine raised a BaseException. Consume the exception
            # to not log a warning, the caller doesn't have access to the
            # local task.
            future.exception()
        raise
    finally:
        future.remove_done_callback(_run_until_complete_cb)
    if not future.done():
        raise RuntimeError('Event loop stopped before Future completed.')


  return future.result()


/usr/lib/python3.8/asyncio/base_events.py:616:

self = <testbook.client.TestbookNotebookClient object at 0x7f15997f8dc0>

cell = {'cell_type': 'code', 'execution_count': 4, 'id': 'abdb2c78', 'metadata': {'pycharm': {'name': '#%%\n'}, 'execution': ...f().to_parquet(os.path.join(DATA_FOLDER, "train"))\n    valid.to_ddf().to_parquet(os.path.join(DATA_FOLDER, "valid"))'}

cell_index = 7, execution_count = None, store_history = True
async def async_execute_cell(
    self,
    cell: NotebookNode,
    cell_index: int,
    execution_count: t.Optional[int] = None,
    store_history: bool = True,
) -> NotebookNode:
    """
    Executes a single code cell.

    To execute all cells see :meth:`execute`.

    Parameters
    ----------
    cell : nbformat.NotebookNode
        The cell which is currently being processed.
    cell_index : int
        The position of the cell within the notebook object.
    execution_count : int
        The execution count to be assigned to the cell (default: Use kernel response)
    store_history : bool
        Determines if history should be stored in the kernel (default: False).
        Specific to ipython kernels, which can store command histories.

    Returns
    -------
    output : dict
        The execution output payload (or None for no output).

    Raises
    ------
    CellExecutionError
        If execution failed and should raise an exception, this will be raised
        with defaults about the failure.

    Returns
    -------
    cell : NotebookNode
        The cell which was just processed.
    """
    assert self.kc is not None

    await run_hook(self.on_cell_start, cell=cell, cell_index=cell_index)

    if cell.cell_type != 'code' or not cell.source.strip():
        self.log.debug("Skipping non-executing cell %s", cell_index)
        return cell

    if self.skip_cells_with_tag in cell.metadata.get("tags", []):
        self.log.debug("Skipping tagged cell %s", cell_index)
        return cell

    if self.record_timing:  # clear execution metadata prior to execution
        cell['metadata']['execution'] = {}

    self.log.debug("Executing cell:\n%s", cell.source)

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors or "raises-exception" in cell.metadata.get("tags", [])
    )

    await run_hook(self.on_cell_execute, cell=cell, cell_index=cell_index)
    parent_msg_id = await ensure_async(
        self.kc.execute(
            cell.source, store_history=store_history, stop_on_error=not cell_allows_errors
        )
    )
    await run_hook(self.on_cell_complete, cell=cell, cell_index=cell_index)
    # We launched a code cell to execute
    self.code_cells_executed += 1
    exec_timeout = self._get_timeout(cell)

    cell.outputs = []
    self.clear_before_next_output = False

    task_poll_kernel_alive = asyncio.ensure_future(self._async_poll_kernel_alive())
    task_poll_output_msg = asyncio.ensure_future(
        self._async_poll_output_msg(parent_msg_id, cell, cell_index)
    )
    self.task_poll_for_reply = asyncio.ensure_future(
        self._async_poll_for_reply(
            parent_msg_id, cell, exec_timeout, task_poll_output_msg, task_poll_kernel_alive
        )
    )
    try:
        exec_reply = await self.task_poll_for_reply
    except asyncio.CancelledError:
        # can only be cancelled by task_poll_kernel_alive when the kernel is dead
        task_poll_output_msg.cancel()
        raise DeadKernelError("Kernel died")
    except Exception as e:
        # Best effort to cancel request if it hasn't been resolved
        try:
            # Check if the task_poll_output is doing the raising for us
            if not isinstance(e, CellControlSignal):
                task_poll_output_msg.cancel()
        finally:
            raise

    if execution_count:
        cell['execution_count'] = execution_count


  await self._check_raise_for_error(cell, cell_index, exec_reply)


../../../.local/lib/python3.8/site-packages/nbclient/client.py:965:

self = <testbook.client.TestbookNotebookClient object at 0x7f15997f8dc0>

cell = {'cell_type': 'code', 'execution_count': 4, 'id': 'abdb2c78', 'metadata': {'pycharm': {'name': '#%%\n'}, 'execution': ...f().to_parquet(os.path.join(DATA_FOLDER, "train"))\n    valid.to_ddf().to_parquet(os.path.join(DATA_FOLDER, "valid"))'}

cell_index = 7

exec_reply = {'buffers': [], 'content': {'ename': 'RuntimeError', 'engine_info': {'engine_id': -1, 'engine_uuid': '129221f6-d595-49...e, 'engine': '129221f6-d595-49f0-9955-196689e293dd', 'started': '2022-11-18T22:07:08.840929Z', 'status': 'error'}, ...}
async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(self.on_cell_error, cell=cell, cell_index=cell_index)
    if not cell_allows_errors:


      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)


E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E           from merlin.datasets.synthetic import generate_data

E

E           DATA_FOLDER = os.environ.get("DATA_FOLDER", "/workspace/data/")

E

E           NUM_ROWS = os.environ.get("NUM_ROWS", 1000000)

E           SYNTHETIC_DATA = eval(os.environ.get("SYNTHETIC_DATA", "True"))

E

E           if SYNTHETIC_DATA:

E               train, valid = generate_data("aliccp-raw", int(NUM_ROWS), set_sizes=(0.7, 0.3))

E               # save the datasets as parquet files

E               train.to_ddf().to_parquet(os.path.join(DATA_FOLDER, "train"))

E               valid.to_ddf().to_parquet(os.path.join(DATA_FOLDER, "valid"))

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mRuntimeError�[0m                              Traceback (most recent call last)

E           Cell �[0;32mIn [4], line 9�[0m

E           �[1;32m      6�[0m SYNTHETIC_DATA �[38;5;241m=�[39m �[38;5;28meval�[39m(os�[38;5;241m.�[39menviron�[38;5;241m.�[39mget(�[38;5;124m"�[39m�[38;5;124mSYNTHETIC_DATA�[39m�[38;5;124m"�[39m, �[38;5;124m"�[39m�[38;5;124mTrue�[39m�[38;5;124m"�[39m))

E           �[1;32m      8�[0m �[38;5;28;01mif�[39;00m SYNTHETIC_DATA:

E           �[0;32m----> 9�[0m     train, valid �[38;5;241m=�[39m �[43mgenerate_data�[49m�[43m(�[49m�[38;5;124;43m"�[39;49m�[38;5;124;43maliccp-raw�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[38;5;28;43mint�[39;49m�[43m(�[49m�[43mNUM_ROWS�[49m�[43m)�[49m�[43m,�[49m�[43m �[49m�[43mset_sizes�[49m�[38;5;241;43m=�[39;49m�[43m(�[49m�[38;5;241;43m0.7�[39;49m�[43m,�[49m�[43m �[49m�[38;5;241;43m0.3�[39;49m�[43m)�[49m�[43m)�[49m

E           �[1;32m     10�[0m     �[38;5;66;03m# save the datasets as parquet files�[39;00m

E           �[1;32m     11�[0m     train�[38;5;241m.�[39mto_ddf()�[38;5;241m.�[39mto_parquet(os�[38;5;241m.�[39mpath�[38;5;241m.�[39mjoin(DATA_FOLDER, �[38;5;124m"�[39m�[38;5;124mtrain�[39m�[38;5;124m"�[39m))

E

E           File �[0;32m~/workspace/merlin_models/models/merlin/datasets/synthetic.py:134�[0m, in �[0;36mgenerate_data�[0;34m(input, num_rows, set_sizes, min_session_length, max_session_length, device)�[0m

E           �[1;32m    131�[0m         start_i �[38;5;241m=�[39m end_i

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E           �[0;32m--> 134�[0m     �[38;5;28;01mreturn�[39;00m �[38;5;28mtuple�[39m([merlin�[38;5;241m.�[39mio�[38;5;241m.�[39mDataset(d, schema�[38;5;241m=�[39mschema) �[38;5;28;01mfor�[39;00m d �[38;5;129;01min�[39;00m output_datasets])

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E

E           File �[0;32m~/workspace/merlin_models/models/merlin/datasets/synthetic.py:134�[0m, in �[0;36m�[0;34m(.0)�[0m

E           �[1;32m    131�[0m         start_i �[38;5;241m=�[39m end_i

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E           �[0;32m--> 134�[0m     �[38;5;28;01mreturn�[39;00m �[38;5;28mtuple�[39m([�[43mmerlin�[49m�[38;5;241;43m.�[39;49m�[43mio�[49m�[38;5;241;43m.�[39;49m�[43mDataset�[49m�[43m(�[49m�[43md�[49m�[43m,�[49m�[43m �[49m�[43mschema�[49m�[38;5;241;43m=�[39;49m�[43mschema�[49m�[43m)�[49m �[38;5;28;01mfor�[39;00m d �[38;5;129;01min�[39;00m output_datasets])

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E

E           File �[0;32m~/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/io/dataset.py:267�[0m, in �[0;36mDataset.__init__�[0;34m(self, path_or_source, engine, npartitions, part_size, part_mem_fraction, storage_options, dtypes, client, cpu, base_dataset, schema, **kwargs)�[0m

E           �[1;32m    261�[0m npartitions �[38;5;241m=�[39m npartitions �[38;5;129;01mor�[39;00m �[38;5;241m1�[39m

E           �[1;32m    262�[0m �[38;5;28;01mif�[39;00m �[38;5;28misinstance�[39m(path_or_source, dask�[38;5;241m.�[39mdataframe�[38;5;241m.�[39mDataFrame) �[38;5;129;01mor�[39;00m is_dataframe_object(

E           �[1;32m    263�[0m     path_or_source

E           �[1;32m    264�[0m ):

E           �[1;32m    265�[0m     �[38;5;66;03m# User is passing in a <dask.dataframe|cudf|pd>.DataFrame�[39;00m

E           �[1;32m    266�[0m     �[38;5;66;03m# Use DataFrameDatasetEngine�[39;00m

E           �[0;32m--> 267�[0m     _path_or_source �[38;5;241m=�[39m �[43mconvert_data�[49m�[43m(�[49m

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E           �[1;32m    269�[0m �[43m    �[49m�[43m)�[49m

E           �[1;32m    270�[0m     �[38;5;66;03m# Check if this is a collection that has now moved between host <-> device�[39;00m

E           �[1;32m    271�[0m     moved_collection �[38;5;241m=�[39m �[38;5;28misinstance�[39m(path_or_source, dask�[38;5;241m.�[39mdataframe�[38;5;241m.�[39mDataFrame) �[38;5;129;01mand�[39;00m (

E           �[1;32m    272�[0m         �[38;5;129;01mnot�[39;00m �[38;5;28misinstance�[39m(_path_or_source�[38;5;241m.�[39m_meta, �[38;5;28mtype�[39m(path_or_source�[38;5;241m.�[39m_meta))

E           �[1;32m    273�[0m     )

E

E           File �[0;32m~/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/core/dispatch.py:579�[0m, in �[0;36mconvert_data�[0;34m(x, cpu, to_collection, npartitions)�[0m

E           �[1;32m    577�[0m     _x �[38;5;241m=�[39m cudf�[38;5;241m.�[39mDataFrame�[38;5;241m.�[39mfrom_arrow(x)

E           �[1;32m    578�[0m �[38;5;28;01melif�[39;00m �[38;5;28misinstance�[39m(x, pd�[38;5;241m.�[39mDataFrame):

E           �[0;32m--> 579�[0m     _x �[38;5;241m=�[39m �[43mcudf�[49m�[38;5;241;43m.�[39;49m�[43mDataFrame�[49m�[38;5;241;43m.�[39;49m�[43mfrom_pandas�[49m�[43m(�[49m�[43mx�[49m�[43m)�[49m

E           �[1;32m    580�[0m �[38;5;66;03m# Output a collection if to_collection=True�[39;00m

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E           �[1;32m    585�[0m )

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/nvtx/nvtx.py:101�[0m, in �[0;36mannotate.call..inner�[0;34m(args, **kwargs)�[0m

E           �[1;32m     98�[0m �[38;5;129m@wraps�[39m(func)

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E           �[1;32m    100�[0m     libnvtx_push_range(�[38;5;28mself�[39m�[38;5;241m.�[39mattributes, �[38;5;28mself�[39m�[38;5;241m.�[39mdomain�[38;5;241m.�[39mhandle)

E           �[0;32m--> 101�[0m     result �[38;5;241m=�[39m �[43mfunc�[49m�[43m(�[49m�[38;5;241;43m�[39;49m�[43margs�[49m�[43m,�[49m�[43m �[49m�[38;5;241;43m�[39;49m�[38;5;241;43m*�[39;49m�[43mkwargs�[49m�[43m)�[49m

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E           �[1;32m    103�[0m     �[38;5;28;01mreturn�[39;00m result

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:4547�[0m, in �[0;36mDataFrame.from_pandas�[0;34m(cls, dataframe, nan_as_null)�[0m

E           �[1;32m   4543�[0m �[38;5;28;01mfor�[39;00m col_name, col_value �[38;5;129;01min�[39;00m dataframe�[38;5;241m.�[39mitems():

E           �[1;32m   4544�[0m     �[38;5;66;03m# necessary because multi-index can return multiple�[39;00m

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E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/cudf/core/column/column.py:1966�[0m, in �[0;36mas_column�[0;34m(arbitrary, nan_as_null, dtype, length)�[0m

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E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/cudf/core/column/column.py:1760�[0m, in �[0;36mas_column�[0;34m(arbitrary, nan_as_null, dtype, length)�[0m

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E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/cudf/core/column/column.py:297�[0m, in �[0;36mColumnBase.from_arrow�[0;34m(cls, array)�[0m

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E

E           File �[0;32mcudf/_lib/interop.pyx:150�[0m, in �[0;36mcudf._lib.interop.from_arrow�[0;34m()�[0m

E

E           �[0;31mRuntimeError�[0m: CUDA error encountered at: ../src/bitmask/null_mask.cu:93: 2 cudaErrorMemoryAllocation out of memory

E           RuntimeError: CUDA error encountered at: ../src/bitmask/null_mask.cu:93: 2 cudaErrorMemoryAllocation out of memory
../../../.local/lib/python3.8/site-packages/nbclient/client.py:862: CellExecutionError
During handling of the above exception, another exception occurred:
tb = <testbook.client.TestbookNotebookClient object at 0x7f15997f8dc0>

tmpdir = local('/tmp/pytest-of-jenkins/pytest-45/test_example_03_exploring_diff0')
@testbook(REPO_ROOT / "examples/03-Exploring-different-models.ipynb", execute=False)
def test_example_03_exploring_different_models(tb, tmpdir):
    tb.inject(
        f"""
        import os
        os.environ["DATA_FOLDER"] = "{tmpdir}"
        os.environ["NUM_ROWS"] = "999"
        """
    )
    NUM_OF_CELLS = len(tb.cells)


  tb.execute_cell(list(range(0, NUM_OF_CELLS - 5)))


tests/unit/tf/examples/test_03_exploring_different_models.py:18:

self = <testbook.client.TestbookNotebookClient object at 0x7f15997f8dc0>

cell = {'cell_type': 'markdown', 'id': 'e75faa3b', 'metadata': {'pycharm': {'name': '#%% md\n'}}, 'source': 'When we work on ...we define our input and output paths. We will use the parquet files in the test folder to validate our trained model.'}

kwargs = {}, cell_indexes = [0, 1, 2, 3, 4, 5, ...]

executed_cells = [{'cell_type': 'code', 'execution_count': 2, 'id': '5f49a48e', 'metadata': {'pycharm': {'name': '#%%\n'}, 'execution':...d': '2dd02301', 'metadata': {'pycharm': {'name': '#%% md\n'}}, 'source': '## Feature Engineering with NVTabular'}, ...]

idx = 7
def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:
            cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)
        except CellExecutionError as ce:


          raise TestbookRuntimeError(ce.evalue, ce, self._get_error_class(ce.ename))


E               testbook.exceptions.TestbookRuntimeError: An error occurred while executing the following cell:

E               ------------------

E               from merlin.datasets.synthetic import generate_data

E

E               DATA_FOLDER = os.environ.get("DATA_FOLDER", "/workspace/data/")

E

E               NUM_ROWS = os.environ.get("NUM_ROWS", 1000000)

E               SYNTHETIC_DATA = eval(os.environ.get("SYNTHETIC_DATA", "True"))

E

E               if SYNTHETIC_DATA:

E                   train, valid = generate_data("aliccp-raw", int(NUM_ROWS), set_sizes=(0.7, 0.3))

E                   # save the datasets as parquet files

E                   train.to_ddf().to_parquet(os.path.join(DATA_FOLDER, "train"))

E                   valid.to_ddf().to_parquet(os.path.join(DATA_FOLDER, "valid"))

E               ------------------

E

E               �[0;31m---------------------------------------------------------------------------�[0m

E               �[0;31mRuntimeError�[0m                              Traceback (most recent call last)

E               Cell �[0;32mIn [4], line 9�[0m

E               �[1;32m      6�[0m SYNTHETIC_DATA �[38;5;241m=�[39m �[38;5;28meval�[39m(os�[38;5;241m.�[39menviron�[38;5;241m.�[39mget(�[38;5;124m"�[39m�[38;5;124mSYNTHETIC_DATA�[39m�[38;5;124m"�[39m, �[38;5;124m"�[39m�[38;5;124mTrue�[39m�[38;5;124m"�[39m))

E               �[1;32m      8�[0m �[38;5;28;01mif�[39;00m SYNTHETIC_DATA:

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E               �[1;32m     10�[0m     �[38;5;66;03m# save the datasets as parquet files�[39;00m

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E

E               File �[0;32m~/workspace/merlin_models/models/merlin/datasets/synthetic.py:134�[0m, in �[0;36mgenerate_data�[0;34m(input, num_rows, set_sizes, min_session_length, max_session_length, device)�[0m

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E               File �[0;32m~/workspace/merlin_models/models/merlin/datasets/synthetic.py:134�[0m, in �[0;36m�[0;34m(.0)�[0m

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E               �[1;32m   1754�[0m �[38;5;28;01mif�[39;00m �[38;5;28misinstance�[39m(arbitrary, pa�[38;5;241m.�[39mlib�[38;5;241m.�[39mHalfFloatArray):

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E               �[1;32m   1756�[0m         �[38;5;124m"�[39m�[38;5;124mType casting from float16 to float32 is not �[39m�[38;5;124m"�[39m

E               �[1;32m   1757�[0m         �[38;5;124m"�[39m�[38;5;124myet supported in pyarrow, see: �[39m�[38;5;124m"�[39m

E               �[1;32m   1758�[0m         �[38;5;124m"�[39m�[38;5;124mhttps://issues.apache.org/jira/browse/ARROW-3802�[39m�[38;5;124m"�[39m

E               �[1;32m   1759�[0m     )

E               �[0;32m-> 1760�[0m col �[38;5;241m=�[39m �[43mColumnBase�[49m�[38;5;241;43m.�[39;49m�[43mfrom_arrow�[49m�[43m(�[49m�[43marbitrary�[49m�[43m)�[49m

E               �[1;32m   1762�[0m �[38;5;28;01mif�[39;00m �[38;5;28misinstance�[39m(arbitrary, pa�[38;5;241m.�[39mNullArray):

E               �[1;32m   1763�[0m     new_dtype �[38;5;241m=�[39m cudf�[38;5;241m.�[39mdtype(arbitrary�[38;5;241m.�[39mtype�[38;5;241m.�[39mto_pandas_dtype())

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/cudf/core/column/column.py:297�[0m, in �[0;36mColumnBase.from_arrow�[0;34m(cls, array)�[0m

E               �[1;32m    292�[0m �[38;5;28;01melif�[39;00m �[38;5;28misinstance�[39m(

E               �[1;32m    293�[0m     array�[38;5;241m.�[39mtype, pd�[38;5;241m.�[39mcore�[38;5;241m.�[39marrays�[38;5;241m.�[39m_arrow_utils�[38;5;241m.�[39mArrowIntervalType

E               �[1;32m    294�[0m ):

E               �[1;32m    295�[0m     �[38;5;28;01mreturn�[39;00m cudf�[38;5;241m.�[39mcore�[38;5;241m.�[39mcolumn�[38;5;241m.�[39mIntervalColumn�[38;5;241m.�[39mfrom_arrow(array)

E               �[0;32m--> 297�[0m result �[38;5;241m=�[39m �[43mlibcudf�[49m�[38;5;241;43m.�[39;49m�[43minterop�[49m�[38;5;241;43m.�[39;49m�[43mfrom_arrow�[49m�[43m(�[49m�[43mdata�[49m�[43m)�[49m[�[38;5;241m0�[39m]

E               �[1;32m    299�[0m �[38;5;28;01mreturn�[39;00m result�[38;5;241m.�[39m_with_type_metadata(cudf_dtype_from_pa_type(array�[38;5;241m.�[39mtype))

E

E               File �[0;32mcudf/_lib/interop.pyx:150�[0m, in �[0;36mcudf._lib.interop.from_arrow�[0;34m()�[0m

E

E               �[0;31mRuntimeError�[0m: CUDA error encountered at: ../src/bitmask/null_mask.cu:93: 2 cudaErrorMemoryAllocation out of memory

E               RuntimeError: CUDA error encountered at: ../src/bitmask/null_mask.cu:93: 2 cudaErrorMemoryAllocation out of memory
../../../.local/lib/python3.8/site-packages/testbook/client.py:135: TestbookRuntimeError

----------------------------- Captured stderr call -----------------------------

2022-11-18 22:07:03.736352: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-11-18 22:07:07.948900: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 0

2022-11-18 22:07:07.949004: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 8139 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-11-18 22:07:07.949787: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 1

2022-11-18 22:07:07.949849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 8360 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

2022-11-18 22:07:07.950553: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 2

2022-11-18 22:07:07.950605: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13875 MB memory:  -> device: 2, name: Tesla P100-DGXS-16GB, pci bus id: 0000:0e:00.0, compute capability: 6.0

2022-11-18 22:07:07.951169: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 3

2022-11-18 22:07:07.951221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13875 MB memory:  -> device: 3, name: Tesla P100-DGXS-16GB, pci bus id: 0000:0f:00.0, compute capability: 6.0

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

_________________ test_usecase_accelerate_training_by_lazyadam _________________
tb = <testbook.client.TestbookNotebookClient object at 0x7f15c0147c40>
@testbook(
    REPO_ROOT / p,
    timeout=180,
    execute=False,
)
def test_usecase_accelerate_training_by_lazyadam(tb):
    tb.inject(
        """
        import os
        os.environ["NUM_ROWS"] = "1000"
        """
    )


  tb.execute()


tests/unit/tf/examples/test_usecase_accelerate_training_by_lazyadam.py:22:

../../../.local/lib/python3.8/site-packages/testbook/client.py:147: in execute

super().execute_cell(cell, index)

../../../.local/lib/python3.8/site-packages/nbclient/util.py:84: in wrapped

return just_run(coro(*args, **kwargs))

../../../.local/lib/python3.8/site-packages/nbclient/util.py:62: in just_run

return loop.run_until_complete(coro)

/usr/lib/python3.8/asyncio/base_events.py:616: in run_until_complete

return future.result()

../../../.local/lib/python3.8/site-packages/nbclient/client.py:965: in async_execute_cell

await self._check_raise_for_error(cell, cell_index, exec_reply)

self = <testbook.client.TestbookNotebookClient object at 0x7f15c0147c40>

cell = {'cell_type': 'code', 'execution_count': 7, 'id': '0500ad25-29e0-40c8-85bc-6e3864107c6a', 'metadata': {'execution': {'...e_train_function_3861]']}], 'source': 'model1.compile(optimizer="adam")\nmodel1.fit(train, batch_size=1024, epochs=1)'}

cell_index = 12

exec_reply = {'buffers': [], 'content': {'ename': 'ResourceExhaustedError', 'engine_info': {'engine_id': -1, 'engine_uuid': 'd63008...e, 'engine': 'd63008b0-2d32-4df8-9cd0-3fcc1d4a6ebd', 'started': '2022-11-18T22:10:01.273671Z', 'status': 'error'}, ...}
async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(self.on_cell_error, cell=cell, cell_index=cell_index)
    if not cell_allows_errors:


      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)


E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E           model1.compile(optimizer="adam")

E           model1.fit(train, batch_size=1024, epochs=1)

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mResourceExhaustedError�[0m                    Traceback (most recent call last)

E           Cell �[0;32mIn [7], line 2�[0m

E           �[1;32m      1�[0m model1�[38;5;241m.�[39mcompile(optimizer�[38;5;241m=�[39m�[38;5;124m"�[39m�[38;5;124madam�[39m�[38;5;124m"�[39m)

E           �[0;32m----> 2�[0m �[43mmodel1�[49m�[38;5;241;43m.�[39;49m�[43mfit�[49m�[43m(�[49m�[43mtrain�[49m�[43m,�[49m�[43m �[49m�[43mbatch_size�[49m�[38;5;241;43m=�[39;49m�[38;5;241;43m1024�[39;49m�[43m,�[49m�[43m �[49m�[43mepochs�[49m�[38;5;241;43m=�[39;49m�[38;5;241;43m1�[39;49m�[43m)�[49m

E

E           File �[0;32m~/workspace/merlin_models/models/merlin/models/tf/models/base.py:920�[0m, in �[0;36mBaseModel.fit�[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing, train_metrics_steps, pre, **kwargs)�[0m

E           �[1;32m    917�[0m     �[38;5;28mself�[39m�[38;5;241m.�[39m_reset_compile_cache()

E           �[1;32m    918�[0m     �[38;5;28mself�[39m�[38;5;241m.�[39mtrain_pre �[38;5;241m=�[39m pre

E           �[0;32m--> 920�[0m out �[38;5;241m=�[39m �[38;5;28;43msuper�[39;49m�[43m(�[49m�[43m)�[49m�[38;5;241;43m.�[39;49m�[43mfit�[49m�[43m(�[49m�[38;5;241;43m�[39;49m�[38;5;241;43m�[39;49m�[43mfit_kwargs�[49m�[43m)�[49m

E           �[1;32m    922�[0m �[38;5;28;01mif�[39;00m pre:

E           �[1;32m    923�[0m     �[38;5;28;01mdel�[39;00m �[38;5;28mself�[39m�[38;5;241m.�[39mtrain_pre

E

E           File �[0;32m~/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:67�[0m, in �[0;36mfilter_traceback..error_handler�[0;34m(*args, **kwargs)�[0m

E           �[1;32m     65�[0m �[38;5;28;01mexcept�[39;00m �[38;5;167;01mException�[39;00m �[38;5;28;01mas�[39;00m e:  �[38;5;66;03m# pylint: disable=broad-except�[39;00m

E           �[1;32m     66�[0m   filtered_tb �[38;5;241m=�[39m process_traceback_frames(e�[38;5;241m.�[39m__traceback_)

E           �[0;32m---> 67�[0m   �[38;5;28;01mraise�[39;00m e�[38;5;241m.�[39mwith_traceback(filtered_tb) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E           �[1;32m     68�[0m �[38;5;28;01mfinally�[39;00m:

E           �[1;32m     69�[0m   �[38;5;28;01mdel�[39;00m filtered_tb

E

E           File �[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:54�[0m, in �[0;36mquick_execute�[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)�[0m

E           �[1;32m     52�[0m �[38;5;28;01mtry�[39;00m:

E           �[1;32m     53�[0m   ctx�[38;5;241m.�[39mensure_initialized()

E           �[0;32m---> 54�[0m   tensors �[38;5;241m=�[39m pywrap_tfe�[38;5;241m.�[39mTFE_Py_Execute(ctx�[38;5;241m.�[39m_handle, device_name, op_name,

E           �[1;32m     55�[0m                                       inputs, attrs, num_outputs)

E           �[1;32m     56�[0m �[38;5;28;01mexcept�[39;00m core�[38;5;241m.�[39m_NotOkStatusException �[38;5;28;01mas�[39;00m e:

E           �[1;32m     57�[0m   �[38;5;28;01mif�[39;00m name �[38;5;129;01mis�[39;00m �[38;5;129;01mnot�[39;00m �[38;5;28;01mNone�[39;00m:

E

E           �[0;31mResourceExhaustedError�[0m: Graph execution error:

E

E           Detected at node 'Adam/Adam/update_17/mul_1' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_5778/3741080137.py", line 2, in 

E                 model1.fit(train, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 206, in _resource_apply_sparse

E                 m_t = tf.compat.v1.assign(m, m * coefficients['beta_1_t'],

E           Node: 'Adam/Adam/update_17/mul_1'

E           Detected at node 'Adam/Adam/update_17/mul_1' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_5778/3741080137.py", line 2, in 

E                 model1.fit(train, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 206, in _resource_apply_sparse

E                 m_t = tf.compat.v1.assign(m, m * coefficients['beta_1_t'],

E           Node: 'Adam/Adam/update_17/mul_1'

E           2 root error(s) found.

E             (0) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_17/mul_1}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           	 [[StatefulPartitionedCall/cond/pivot_t/_131/_53]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E             (1) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_17/mul_1}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           0 successful operations.

E           0 derived errors ignored. [Op:__inference_train_function_3861]

E           ResourceExhaustedError: Graph execution error:

E

E           Detected at node 'Adam/Adam/update_17/mul_1' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_5778/3741080137.py", line 2, in 

E                 model1.fit(train, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 206, in _resource_apply_sparse

E                 m_t = tf.compat.v1.assign(m, m * coefficients['beta_1_t'],

E           Node: 'Adam/Adam/update_17/mul_1'

E           Detected at node 'Adam/Adam/update_17/mul_1' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_5778/3741080137.py", line 2, in 

E                 model1.fit(train, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 206, in _resource_apply_sparse

E                 m_t = tf.compat.v1.assign(m, m * coefficients['beta_1_t'],

E           Node: 'Adam/Adam/update_17/mul_1'

E           2 root error(s) found.

E             (0) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_17/mul_1}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           	 [[StatefulPartitionedCall/cond/pivot_t/_131/_53]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E             (1) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_17/mul_1}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           0 successful operations.

E           0 derived errors ignored. [Op:__inference_train_function_3861]
../../../.local/lib/python3.8/site-packages/nbclient/client.py:862: CellExecutionError

----------------------------- Captured stderr call -----------------------------

2022-11-18 22:09:54.320669: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-11-18 22:09:58.488075: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 0

2022-11-18 22:09:58.488180: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 8139 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-11-18 22:09:58.488839: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 1

2022-11-18 22:09:58.488899: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13875 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

2022-11-18 22:09:58.489499: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 2

2022-11-18 22:09:58.489551: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13875 MB memory:  -> device: 2, name: Tesla P100-DGXS-16GB, pci bus id: 0000:0e:00.0, compute capability: 6.0

2022-11-18 22:09:58.490105: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 3

2022-11-18 22:09:58.490161: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13875 MB memory:  -> device: 3, name: Tesla P100-DGXS-16GB, pci bus id: 0000:0f:00.0, compute capability: 6.0

2022-11-18 22:10:15.533453: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:288] gpu_async_0 cuMemAllocAsync failed to allocate 1083564064 bytes: CUDA error: out of memory (CUDA_ERROR_OUT_OF_MEMORY)

Reported by CUDA: Free memory/Total memory: 3735552/17069309952

2022-11-18 22:10:15.533518: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:293] Stats: Limit:                      8534360064

InUse:                      4616331776

MaxInUse:                   4616331776

NumAllocs:                         264

MaxAllocSize:               1083564064

Reserved:                            0

PeakReserved:                        0

LargestFreeBlock:                    0
2022-11-18 22:10:15.533546: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:56] Histogram of current allocation: (allocation_size_in_bytes, nb_allocation_of_that_sizes), ...;

2022-11-18 22:10:15.533557: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1, 4

2022-11-18 22:10:15.533563: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4, 34

2022-11-18 22:10:15.533569: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 8, 8

2022-11-18 22:10:15.533575: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 40, 2

2022-11-18 22:10:15.533580: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 128, 9

2022-11-18 22:10:15.533586: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 160, 10

2022-11-18 22:10:15.533591: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 192, 5

2022-11-18 22:10:15.533597: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 256, 7

2022-11-18 22:10:15.533602: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 288, 5

2022-11-18 22:10:15.533608: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 480, 5

2022-11-18 22:10:15.533613: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1028, 1

2022-11-18 22:10:15.533619: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 3168, 5

2022-11-18 22:10:15.533648: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 61440, 3

2022-11-18 22:10:15.533655: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 65536, 3

2022-11-18 22:10:15.533661: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 584352, 5

2022-11-18 22:10:15.533666: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 823872, 5

2022-11-18 22:10:15.533672: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4324736, 5

2022-11-18 22:10:15.533677: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 7426048, 5

2022-11-18 22:10:15.533683: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 15401440, 3

2022-11-18 22:10:15.533688: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 18678720, 5

2022-11-18 22:10:15.533694: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 39970560, 5

2022-11-18 22:10:15.533705: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 56589504, 5

2022-11-18 22:10:15.533710: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 135407776, 5

2022-11-18 22:10:15.533716: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1083564064, 3

2022-11-18 22:10:15.533743: W tensorflow/core/framework/op_kernel.cc:1733] RESOURCE_EXHAUSTED: failed to allocate memory

2022-11-18 22:10:15.534431: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:288] gpu_async_0 cuMemAllocAsync failed to allocate 1083564064 bytes: CUDA error: out of memory (CUDA_ERROR_OUT_OF_MEMORY)

Reported by CUDA: Free memory/Total memory: 3735552/17069309952

2022-11-18 22:10:15.534451: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:293] Stats: Limit:                      8534360064

InUse:                      4616331904

MaxInUse:                   4616331904

NumAllocs:                         265

MaxAllocSize:               1083564064

Reserved:                            0

PeakReserved:                        0

LargestFreeBlock:                    0
2022-11-18 22:10:15.534465: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:56] Histogram of current allocation: (allocation_size_in_bytes, nb_allocation_of_that_sizes), ...;

2022-11-18 22:10:15.534472: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1, 4

2022-11-18 22:10:15.534478: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4, 34

2022-11-18 22:10:15.534484: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 8, 8

2022-11-18 22:10:15.534489: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 40, 2

2022-11-18 22:10:15.534495: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 128, 10

2022-11-18 22:10:15.534500: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 160, 10

2022-11-18 22:10:15.534506: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 192, 5

2022-11-18 22:10:15.534512: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 256, 7

2022-11-18 22:10:15.534517: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 288, 5

2022-11-18 22:10:15.534523: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 480, 5

2022-11-18 22:10:15.534528: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1028, 1

2022-11-18 22:10:15.534534: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 3168, 5

2022-11-18 22:10:15.534539: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 61440, 3

2022-11-18 22:10:15.534545: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 65536, 3

2022-11-18 22:10:15.534551: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 584352, 5

2022-11-18 22:10:15.534556: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 823872, 5

2022-11-18 22:10:15.534576: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4324736, 5

2022-11-18 22:10:15.534583: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 7426048, 5

2022-11-18 22:10:15.534589: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 15401440, 3

2022-11-18 22:10:15.534595: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 18678720, 5

2022-11-18 22:10:15.534600: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 39970560, 5

2022-11-18 22:10:15.534606: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 56589504, 5

2022-11-18 22:10:15.534611: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 135407776, 5

2022-11-18 22:10:15.534617: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1083564064, 3

2022-11-18 22:10:15.534626: W tensorflow/core/framework/op_kernel.cc:1733] RESOURCE_EXHAUSTED: failed to allocate memory

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

_______________ test_usecase_incremental_training_layer_freezing _______________
tb = <testbook.client.TestbookNotebookClient object at 0x7f15d816d4f0>
@testbook(
    REPO_ROOT / p,
    timeout=180,
    execute=False,
)
def test_usecase_incremental_training_layer_freezing(tb):
    tb.inject(
        """
        import os
        os.environ["NUM_ROWS"] = "1000"
        """
    )


  tb.execute()


tests/unit/tf/examples/test_usecase_incremental_training_layer_freezing.py:22:

../../../.local/lib/python3.8/site-packages/testbook/client.py:147: in execute

super().execute_cell(cell, index)

../../../.local/lib/python3.8/site-packages/nbclient/util.py:84: in wrapped

return just_run(coro(*args, **kwargs))

../../../.local/lib/python3.8/site-packages/nbclient/util.py:62: in just_run

return loop.run_until_complete(coro)

/usr/lib/python3.8/asyncio/base_events.py:616: in run_until_complete

return future.result()

../../../.local/lib/python3.8/site-packages/nbclient/client.py:965: in async_execute_cell

await self._check_raise_for_error(cell, cell_index, exec_reply)

self = <testbook.client.TestbookNotebookClient object at 0x7f15d816d4f0>

cell = {'cell_type': 'code', 'execution_count': 8, 'id': '791e06ec-c0cb-4c0f-9e41-7e5c8fa1dc4e', 'metadata': {'execution': {'...: 'model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))\nmodel.fit(day_1, batch_size=1024, epochs=1)'}

cell_index = 13

exec_reply = {'buffers': [], 'content': {'ename': 'ResourceExhaustedError', 'engine_info': {'engine_id': -1, 'engine_uuid': '67eef5...e, 'engine': '67eef542-0bf2-49bf-afb1-6701ec2f5b80', 'started': '2022-11-18T22:11:17.739343Z', 'status': 'error'}, ...}
async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(self.on_cell_error, cell=cell, cell_index=cell_index)
    if not cell_allows_errors:


      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)


E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E           model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))

E           model.fit(day_1, batch_size=1024, epochs=1)

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mResourceExhaustedError�[0m                    Traceback (most recent call last)

E           Cell �[0;32mIn [8], line 2�[0m

E           �[1;32m      1�[0m model�[38;5;241m.�[39mcompile(optimizer�[38;5;241m=�[39mtf�[38;5;241m.�[39mkeras�[38;5;241m.�[39moptimizers�[38;5;241m.�[39mAdam(learning_rate�[38;5;241m=�[39m�[38;5;241m0.01�[39m))

E           �[0;32m----> 2�[0m �[43mmodel�[49m�[38;5;241;43m.�[39;49m�[43mfit�[49m�[43m(�[49m�[43mday_1�[49m�[43m,�[49m�[43m �[49m�[43mbatch_size�[49m�[38;5;241;43m=�[39;49m�[38;5;241;43m1024�[39;49m�[43m,�[49m�[43m �[49m�[43mepochs�[49m�[38;5;241;43m=�[39;49m�[38;5;241;43m1�[39;49m�[43m)�[49m

E

E           File �[0;32m~/workspace/merlin_models/models/merlin/models/tf/models/base.py:920�[0m, in �[0;36mBaseModel.fit�[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing, train_metrics_steps, pre, **kwargs)�[0m

E           �[1;32m    917�[0m     �[38;5;28mself�[39m�[38;5;241m.�[39m_reset_compile_cache()

E           �[1;32m    918�[0m     �[38;5;28mself�[39m�[38;5;241m.�[39mtrain_pre �[38;5;241m=�[39m pre

E           �[0;32m--> 920�[0m out �[38;5;241m=�[39m �[38;5;28;43msuper�[39;49m�[43m(�[49m�[43m)�[49m�[38;5;241;43m.�[39;49m�[43mfit�[49m�[43m(�[49m�[38;5;241;43m�[39;49m�[38;5;241;43m�[39;49m�[43mfit_kwargs�[49m�[43m)�[49m

E           �[1;32m    922�[0m �[38;5;28;01mif�[39;00m pre:

E           �[1;32m    923�[0m     �[38;5;28;01mdel�[39;00m �[38;5;28mself�[39m�[38;5;241m.�[39mtrain_pre

E

E           File �[0;32m~/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:67�[0m, in �[0;36mfilter_traceback..error_handler�[0;34m(*args, **kwargs)�[0m

E           �[1;32m     65�[0m �[38;5;28;01mexcept�[39;00m �[38;5;167;01mException�[39;00m �[38;5;28;01mas�[39;00m e:  �[38;5;66;03m# pylint: disable=broad-except�[39;00m

E           �[1;32m     66�[0m   filtered_tb �[38;5;241m=�[39m process_traceback_frames(e�[38;5;241m.�[39m__traceback_)

E           �[0;32m---> 67�[0m   �[38;5;28;01mraise�[39;00m e�[38;5;241m.�[39mwith_traceback(filtered_tb) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E           �[1;32m     68�[0m �[38;5;28;01mfinally�[39;00m:

E           �[1;32m     69�[0m   �[38;5;28;01mdel�[39;00m filtered_tb

E

E           File �[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:54�[0m, in �[0;36mquick_execute�[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)�[0m

E           �[1;32m     52�[0m �[38;5;28;01mtry�[39;00m:

E           �[1;32m     53�[0m   ctx�[38;5;241m.�[39mensure_initialized()

E           �[0;32m---> 54�[0m   tensors �[38;5;241m=�[39m pywrap_tfe�[38;5;241m.�[39mTFE_Py_Execute(ctx�[38;5;241m.�[39m_handle, device_name, op_name,

E           �[1;32m     55�[0m                                       inputs, attrs, num_outputs)

E           �[1;32m     56�[0m �[38;5;28;01mexcept�[39;00m core�[38;5;241m.�[39m_NotOkStatusException �[38;5;28;01mas�[39;00m e:

E           �[1;32m     57�[0m   �[38;5;28;01mif�[39;00m name �[38;5;129;01mis�[39;00m �[38;5;129;01mnot�[39;00m �[38;5;28;01mNone�[39;00m:

E

E           �[0;31mResourceExhaustedError�[0m: Graph execution error:

E

E           Detected at node 'Adam/Adam/update_19/mul_4' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_6307/2071866865.py", line 2, in 

E                 model.fit(day_1, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 214, in _resource_apply_sparse

E                 v_t = tf.compat.v1.assign(v, v * coefficients['beta_2_t'],

E           Node: 'Adam/Adam/update_19/mul_4'

E           Detected at node 'Adam/Adam/update_19/mul_4' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_6307/2071866865.py", line 2, in 

E                 model.fit(day_1, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 214, in _resource_apply_sparse

E                 v_t = tf.compat.v1.assign(v, v * coefficients['beta_2_t'],

E           Node: 'Adam/Adam/update_19/mul_4'

E           2 root error(s) found.

E             (0) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_19/mul_4}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           	 [[StatefulPartitionedCall/cond/pivot_t/_131/_53]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E             (1) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_19/mul_4}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           0 successful operations.

E           0 derived errors ignored. [Op:__inference_train_function_4007]

E           ResourceExhaustedError: Graph execution error:

E

E           Detected at node 'Adam/Adam/update_19/mul_4' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_6307/2071866865.py", line 2, in 

E                 model.fit(day_1, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 214, in _resource_apply_sparse

E                 v_t = tf.compat.v1.assign(v, v * coefficients['beta_2_t'],

E           Node: 'Adam/Adam/update_19/mul_4'

E           Detected at node 'Adam/Adam/update_19/mul_4' defined at (most recent call last):

E               File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main

E                 return _run_code(code, main_globals, None,

E               File "/usr/lib/python3.8/runpy.py", line 87, in _run_code

E                 exec(code, run_globals)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py", line 17, in 

E                 app.launch_new_instance()

E               File "/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py", line 978, in launch_instance

E                 app.start()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py", line 712, in start

E                 self.io_loop.start()

E               File "/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start

E                 self.asyncio_loop.run_forever()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

E                 self._run_once()

E               File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

E                 handle._run()

E               File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

E                 self._context.run(self._callback, *self._args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue

E                 await self.process_one()

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 499, in process_one

E                 await dispatch(*args)

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell

E                 await result

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 730, in execute_request

E                 reply_content = await reply_content

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 383, in do_execute

E                 res = shell.run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 528, in run_cell

E                 return super().run_cell(*args, **kwargs)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_cell

E                 result = self._run_cell(

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2940, in _run_cell

E                 return runner(coro)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 129, in pseudo_sync_runner

E                 coro.send(None)

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3139, in run_cell_async

E                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3318, in run_ast_nodes

E                 if await self.run_code(code, result, async=asy):

E               File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3378, in run_code

E                 exec(code_obj, self.user_global_ns, self.user_ns)

E               File "/tmp/ipykernel_6307/2071866865.py", line 2, in 

E                 model.fit(day_1, batch_size=1024, epochs=1)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 920, in fit

E                 out = super().fit(**fit_kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler

E                 return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1409, in fit

E                 tmp_logs = self.train_function(iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1051, in train_function

E                 return step_function(self, iterator)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1040, in step_function

E                 outputs = model.distribute_strategy.run(run_step, args=(data,))

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1030, in run_step

E                 outputs = model.train_step(data)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 724, in train_step

E                 self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize

E                 return self.apply_gradients(grads_and_vars, name=name)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients

E                 return tf.internal.distribute.interim.maybe_merge_call(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply

E                 update_op = distribution.extended.update(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 701, in apply_grad_to_update_var

E                 return self._resource_apply_sparse_duplicate_indices(

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1326, in _resource_apply_sparse_duplicate_indices

E                 return self._resource_apply_sparse(summed_grad, handle, unique_indices,

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py", line 214, in _resource_apply_sparse

E                 v_t = tf.compat.v1.assign(v, v * coefficients['beta_2_t'],

E           Node: 'Adam/Adam/update_19/mul_4'

E           2 root error(s) found.

E             (0) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_19/mul_4}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           	 [[StatefulPartitionedCall/cond/pivot_t/_131/_53]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E             (1) RESOURCE_EXHAUSTED:  failed to allocate memory

E           	 [[{{node Adam/Adam/update_19/mul_4}}]]

E           Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.

E

E           0 successful operations.

E           0 derived errors ignored. [Op:__inference_train_function_4007]
../../../.local/lib/python3.8/site-packages/nbclient/client.py:862: CellExecutionError

----------------------------- Captured stderr call -----------------------------

2022-11-18 22:11:10.737167: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-11-18 22:11:14.856971: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 0

2022-11-18 22:11:14.857083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 8139 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-11-18 22:11:14.857876: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 1

2022-11-18 22:11:14.857937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13875 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

2022-11-18 22:11:14.858524: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 2

2022-11-18 22:11:14.858576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13875 MB memory:  -> device: 2, name: Tesla P100-DGXS-16GB, pci bus id: 0000:0e:00.0, compute capability: 6.0

2022-11-18 22:11:14.859175: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 3

2022-11-18 22:11:14.859227: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13875 MB memory:  -> device: 3, name: Tesla P100-DGXS-16GB, pci bus id: 0000:0f:00.0, compute capability: 6.0

2022-11-18 22:11:32.074908: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:288] gpu_async_0 cuMemAllocAsync failed to allocate 1083564064 bytes: CUDA error: out of memory (CUDA_ERROR_OUT_OF_MEMORY)

Reported by CUDA: Free memory/Total memory: 28901376/17069309952

2022-11-18 22:11:32.074981: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:293] Stats: Limit:                      8534360064

InUse:                      4356167964

MaxInUse:                   4356167964

NumAllocs:                         268

MaxAllocSize:               1083564064

Reserved:                            0

PeakReserved:                        0

LargestFreeBlock:                    0
2022-11-18 22:11:32.075006: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:56] Histogram of current allocation: (allocation_size_in_bytes, nb_allocation_of_that_sizes), ...;

2022-11-18 22:11:32.075016: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1, 4

2022-11-18 22:11:32.075025: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4, 35

2022-11-18 22:11:32.075033: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 8, 7

2022-11-18 22:11:32.075041: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 40, 2

2022-11-18 22:11:32.075049: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 128, 7

2022-11-18 22:11:32.075057: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 160, 7

2022-11-18 22:11:32.075065: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 192, 4

2022-11-18 22:11:32.075072: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 256, 7

2022-11-18 22:11:32.075080: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 288, 3

2022-11-18 22:11:32.075088: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 480, 4

2022-11-18 22:11:32.075096: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 512, 6

2022-11-18 22:11:32.075104: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1028, 1

2022-11-18 22:11:32.075138: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 3168, 4

2022-11-18 22:11:32.075149: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 32768, 6

2022-11-18 22:11:32.075157: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 122880, 3

2022-11-18 22:11:32.075165: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 131072, 3

2022-11-18 22:11:32.075173: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 584352, 3

2022-11-18 22:11:32.075180: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 823872, 4

2022-11-18 22:11:32.075188: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4324736, 3

2022-11-18 22:11:32.075196: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 7426048, 3

2022-11-18 22:11:32.075204: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 15401440, 4

2022-11-18 22:11:32.075212: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 18678720, 4

2022-11-18 22:11:32.075220: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 39970560, 4

2022-11-18 22:11:32.075227: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 56589504, 4

2022-11-18 22:11:32.075235: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 135407776, 4

2022-11-18 22:11:32.075243: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1083564064, 3

2022-11-18 22:11:32.075275: W tensorflow/core/framework/op_kernel.cc:1733] RESOURCE_EXHAUSTED: failed to allocate memory

2022-11-18 22:11:32.076374: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:288] gpu_async_0 cuMemAllocAsync failed to allocate 1083564064 bytes: CUDA error: out of memory (CUDA_ERROR_OUT_OF_MEMORY)

Reported by CUDA: Free memory/Total memory: 28901376/17069309952

2022-11-18 22:11:32.076404: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:293] Stats: Limit:                      8534360064

InUse:                      4398936356

MaxInUse:                   4398936356

NumAllocs:                         279

MaxAllocSize:               1083564064

Reserved:                            0

PeakReserved:                        0

LargestFreeBlock:                    0
2022-11-18 22:11:32.076426: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:56] Histogram of current allocation: (allocation_size_in_bytes, nb_allocation_of_that_sizes), ...;

2022-11-18 22:11:32.076435: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1, 4

2022-11-18 22:11:32.076444: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4, 35

2022-11-18 22:11:32.076452: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 8, 8

2022-11-18 22:11:32.076460: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 40, 2

2022-11-18 22:11:32.076468: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 128, 8

2022-11-18 22:11:32.076475: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 160, 8

2022-11-18 22:11:32.076483: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 192, 4

2022-11-18 22:11:32.076491: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 256, 7

2022-11-18 22:11:32.076499: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 288, 4

2022-11-18 22:11:32.076506: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 480, 4

2022-11-18 22:11:32.076514: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 512, 6

2022-11-18 22:11:32.076522: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1028, 1

2022-11-18 22:11:32.076530: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 3168, 5

2022-11-18 22:11:32.076538: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 32768, 6

2022-11-18 22:11:32.076561: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 122880, 3

2022-11-18 22:11:32.076572: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 131072, 3

2022-11-18 22:11:32.076580: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 584352, 4

2022-11-18 22:11:32.076588: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 823872, 4

2022-11-18 22:11:32.076596: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 4324736, 5

2022-11-18 22:11:32.076604: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 7426048, 5

2022-11-18 22:11:32.076611: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 15401440, 4

2022-11-18 22:11:32.076619: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 18678720, 5

2022-11-18 22:11:32.076627: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 39970560, 4

2022-11-18 22:11:32.076635: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 56589504, 4

2022-11-18 22:11:32.076643: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 135407776, 4

2022-11-18 22:11:32.076651: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:59] 1083564064, 3

2022-11-18 22:11:32.076663: W tensorflow/core/framework/op_kernel.cc:1733] RESOURCE_EXHAUSTED: failed to allocate memory

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 6 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 120 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 5 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 86 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 55 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filelh9tyxr1.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/raw/init.py                        0      0   100%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147     14    90%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              169     62    63%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          244     50    80%

merlin/models/tf/core/combinators.py                                   426     54    87%

merlin/models/tf/core/encoder.py                                       175     28    84%

merlin/models/tf/core/index.py                                         104     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     32    89%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         60     10    83%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     32    93%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        762    106    86%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59      6    90%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     36    92%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      7    90%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     42    80%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      8    74%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11581   2370    80%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

==== 3 failed, 863 passed, 13 skipped, 1438 warnings in 1727.19s (0:28:47) =====

ERROR: InvocationError for command /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/bin/python -m pytest --cov-report term --cov merlin -rxs tests/unit (exited with code 1)

___________________________________ summary ____________________________________

ERROR:   py38-gpu: commands failed

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins10318382386606475839.sh

nvidia-merlin-bot · 2022-11-21T18:51:34Z

Click to view CI Results

GitHub pull request #890 of commit 66fb21cb6821243bc9db6884eb74bc75b981fa54, no merge conflicts.
Running as SYSTEM
Setting status of 66fb21cb6821243bc9db6884eb74bc75b981fa54 to PENDING with url http://merlin-infra1.nvidia.com:8080/job/merlin_models/1908/ and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/890/*:refs/remotes/origin/pr/890/* # timeout=10
 > git rev-parse 66fb21cb6821243bc9db6884eb74bc75b981fa54^{commit} # timeout=10
Checking out Revision 66fb21cb6821243bc9db6884eb74bc75b981fa54 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 66fb21cb6821243bc9db6884eb74bc75b981fa54 # timeout=10
Commit message: "Merge branch 'main' into clm-test-use-model-pre"
 > git rev-list --no-walk 8ab76c1fc5b27010c55f59539987413fa0dcaf38 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins12293458720511183910.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
Requirement already satisfied: nbclient>=0.4.0 in /usr/local/lib/python3.8/dist-packages (from testbook) (0.6.8)
Requirement already satisfied: nbformat>=5.0.4 in /usr/local/lib/python3.8/dist-packages (from testbook) (5.5.0)
Requirement already satisfied: traitlets>=5.2.2 in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (5.4.0)
Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (1.5.5)
Requirement already satisfied: jupyter-client>=6.1.5 in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (7.3.5)
Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (4.16.0)
Requirement already satisfied: jupyter_core in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (4.11.1)
Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (2.16.1)
Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (22.1.0)
Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (5.9.0)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (1.3.10)
Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (24.0.0)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (2.8.2)
Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: entrypoints in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (0.4)
Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/4/merlin-models-0.9.0+53.g66fb21cb.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.13,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.13,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.1.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.5.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==5.0.4,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-dataloader==0.0.2,merlin-models==0.9.0+53.g66fb21cb,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,nvtabular @ git+https://github.com/NVIDIA-Merlin/NVTabular.git@e5b7351deb9e4885c4038aa0bbc9f146d8477a0e,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.3,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.9.1,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==2.5.0,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='354554538'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-x_q5s_hr
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/core.git /tmp/pip-req-build-x_q5s_hr
  Resolved https://github.com/NVIDIA-Merlin/core.git to commit 04ff5225cee6af90b6b08070493677ac53b96836
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+16.g04ff522) (7.0.0)
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+16.g04ff522) (4.64.1)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+16.g04ff522) (2022.5.0)
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+16.g04ff522) (0.55.1)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+16.g04ff522) (1.3.5)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+16.g04ff522) (1.2.5)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+16.g04ff522) (21.3)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+16.g04ff522) (1.10.0)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+16.g04ff522) (2022.3.0)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.8.0+16.g04ff522) (3.19.5)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.8.0+16.g04ff522) (2022.3.0)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.8.0+16.g04ff522) (1.2.0)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.8.0+16.g04ff522) (0.4.3)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (1.2.0)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (0.12.0)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (5.4.1)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (2.2.0)
Requirement already satisfied: tblib>=1.6.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/tblib-1.7.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (1.7.0)
Requirement already satisfied: zict>=0.1.3 in /var/jenkins_home/.local/lib/python3.8/site-packages/zict-2.0.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (2.0.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (8.1.3)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (2.4.0)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (5.8.0)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (3.1.2)
Requirement already satisfied: tornado>=6.0.3 in ./.tox/py38-gpu/lib/python3.8/site-packages (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (6.2)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.8.0+16.g04ff522) (1.0.4)
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Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.8.0+16.g04ff522-py3-none-any.whl size=118833 sha256=d41bd0f5cece0f9a01e1994110bb94e83bacc4a5fd474e404689fef1085dab89
  Stored in directory: /tmp/pip-ephem-wheel-cache-92tw3w7v/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.8.0+16.g04ff522
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-upsflagg
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-upsflagg
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit e5b7351deb9e4885c4038aa0bbc9f146d8477a0e
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
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Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+6.ge5b7351d) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+6.ge5b7351d) (4.0.0)
Building wheels for collected packages: nvtabular
  Building wheel for nvtabular (pyproject.toml): started
  Building wheel for nvtabular (pyproject.toml): finished with status 'done'
  Created wheel for nvtabular: filename=nvtabular-1.6.0+6.ge5b7351d-cp38-cp38-linux_x86_64.whl size=257597 sha256=8bec4d3f4ee8661a57e298b49ceb9e1fe248128914de9a94edd43a14924c8402
  Stored in directory: /tmp/pip-ephem-wheel-cache-pzofj1o8/wheels/8f/d9/f9/30f2cdc5bf8787fae6fdfe55afd6e1b493e619ec32c32ec40b
Successfully built nvtabular
Installing collected packages: nvtabular
  Attempting uninstall: nvtabular
    Found existing installation: nvtabular 1.1.1
    Not uninstalling nvtabular at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'nvtabular'. No files were found to uninstall.
Successfully installed nvtabular-1.6.0+6.ge5b7351d
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
collected 883 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss.s                            [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ..........                         [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  3%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_loader.py ................                            [  6%]

tests/unit/tf/test_public_api.py .                                       [  6%]

tests/unit/tf/blocks/test_cross.py ...........                           [  7%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [  8%]

tests/unit/tf/blocks/test_interactions.py ...                            [  9%]

tests/unit/tf/blocks/test_mlp.py ....................................... [ 13%]

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tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

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tests/unit/tf/blocks/retrieval/test_base.py .                            [ 22%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ............            [ 24%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 24%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 24%]

tests/unit/tf/core/test_aggregation.py .........                         [ 25%]

tests/unit/tf/core/test_base.py ..                                       [ 26%]

tests/unit/tf/core/test_combinators.py s.....................            [ 28%]

tests/unit/tf/core/test_encoder.py ..                                    [ 28%]

tests/unit/tf/core/test_index.py ...                                     [ 29%]

tests/unit/tf/core/test_prediction.py ..                                 [ 29%]

tests/unit/tf/core/test_tabular.py ......                                [ 30%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 30%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 30%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 30%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 30%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 30%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 30%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 30%]

tests/unit/tf/examples/test_usecase_accelerate_training_by_lazyadam.py . [ 30%]

[ 30%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 31%]

tests/unit/tf/examples/test_usecase_incremental_training_layer_freezing.py . [ 31%]

[ 31%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 31%]

tests/unit/tf/examples/test_usecase_retrieval_with_hpo.py .              [ 31%]

tests/unit/tf/examples/test_usecase_transformers_next_item_prediction.py . [ 31%]

[ 31%]

tests/unit/tf/horovod/test_horovod.py ...                                [ 31%]

tests/unit/tf/inputs/test_base.py .                                      [ 31%]

tests/unit/tf/inputs/test_continuous.py ........                         [ 32%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 36%]

........                                                                 [ 37%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 39%]

tests/unit/tf/layers/test_queue.py ..............                        [ 41%]

tests/unit/tf/losses/test_losses.py .......................              [ 43%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 44%]

tests/unit/tf/metrics/test_metrics_topk.py .........................     [ 47%]

tests/unit/tf/models/test_base.py s.........................             [ 50%]

tests/unit/tf/models/test_benchmark.py ..                                [ 50%]

tests/unit/tf/models/test_ranking.py ..................................  [ 54%]

tests/unit/tf/models/test_retrieval.py ................................. [ 57%]

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tests/unit/tf/outputs/test_base.py ......                                [ 63%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py ..............                 [ 65%]

tests/unit/tf/outputs/test_regression.py ..                              [ 65%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 66%]

tests/unit/tf/outputs/test_topk.py .                                     [ 66%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 66%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 68%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 69%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 69%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 69%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 70%]

tests/unit/tf/transformers/test_block.py ......................          [ 72%]

tests/unit/tf/transformers/test_transforms.py ..........                 [ 74%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 74%]

tests/unit/tf/transforms/test_features.py s............................. [ 77%]

.......................s......                                           [ 81%]

tests/unit/tf/transforms/test_negative_sampling.py .........             [ 82%]

tests/unit/tf/transforms/test_noise.py .....                             [ 82%]

tests/unit/tf/transforms/test_sequence.py ....................           [ 84%]

tests/unit/tf/transforms/test_tensor.py ...                              [ 85%]

tests/unit/tf/utils/test_batch.py ....                                   [ 85%]

tests/unit/tf/utils/test_dataset.py ..                                   [ 85%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 86%]

tests/unit/torch/test_dataset.py .........                               [ 87%]

tests/unit/torch/test_public_api.py .                                    [ 87%]

tests/unit/torch/block/test_base.py ....                                 [ 88%]

tests/unit/torch/block/test_mlp.py .                                     [ 88%]

tests/unit/torch/features/test_continuous.py ..                          [ 88%]

tests/unit/torch/features/test_embedding.py ..............               [ 90%]

tests/unit/torch/features/test_tabular.py ....                           [ 90%]

tests/unit/torch/model/test_head.py ............                         [ 91%]

tests/unit/torch/model/test_model.py ..                                  [ 92%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 92%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 93%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 94%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 10 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 120 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 9 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 86 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 55 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filehjp5adjk.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/raw/init.py                        0      0   100%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147      8    95%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              169     62    63%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          244     50    80%

merlin/models/tf/core/combinators.py                                   426     53    88%

merlin/models/tf/core/encoder.py                                       175     28    84%

merlin/models/tf/core/index.py                                         104     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     29    90%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         60     10    83%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     32    93%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        762    104    86%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59      6    90%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     36    92%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      4    94%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     42    80%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      2    94%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11581   2349    80%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========= 870 passed, 13 skipped, 1446 warnings in 1769.63s (0:29:29) ==========

___________________________________ summary ____________________________________

py38-gpu: commands succeeded

congratulations :)

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins11646617205478157883.sh

nvidia-merlin-bot · 2022-11-23T15:06:55Z

Click to view CI Results

GitHub pull request #890 of commit 7cf0f5fbd0185d31701d8d46a994b0da45c8f66a, no merge conflicts.
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Setting status of 7cf0f5fbd0185d31701d8d46a994b0da45c8f66a to PENDING with url http://merlin-infra1.nvidia.com:8080/job/merlin_models/1940/ and message: 'Pending'
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Building on master in workspace /var/jenkins_home/workspace/merlin_models
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Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
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using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
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 > git rev-parse 7cf0f5fbd0185d31701d8d46a994b0da45c8f66a^{commit} # timeout=10
Checking out Revision 7cf0f5fbd0185d31701d8d46a994b0da45c8f66a (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 7cf0f5fbd0185d31701d8d46a994b0da45c8f66a # timeout=10
Commit message: "Merge branch 'main' into clm-test-use-model-pre"
 > git rev-list --no-walk 6ffb409f9964ee68176bb99978b34afaa1090102 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins1021147879375214999.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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Requirement already satisfied: nbformat>=5.0.4 in /usr/local/lib/python3.8/dist-packages (from testbook) (5.5.0)
Requirement already satisfied: nbclient>=0.4.0 in /usr/local/lib/python3.8/dist-packages (from testbook) (0.6.8)
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Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (1.5.5)
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Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (2.16.1)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (5.9.0)
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Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (2.8.2)
Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (24.0.0)
Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/4/merlin-models-0.9.0+59.g7cf0f5fb.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.15,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.15,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.1.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.1.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==5.0.4,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-models==0.9.0+59.g7cf0f5fb,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,# Editable install with no version control (nvtabular==1.4.0+8.g95e12d347),-e /usr/local/lib/python3.8/dist-packages,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.3,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.9.1,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==2.5.0,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='1825525056'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-y0ft3g7d
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/core.git /tmp/pip-req-build-y0ft3g7d
  Resolved https://github.com/NVIDIA-Merlin/core.git to commit 025f10da5f96c9ae470ee31133801da05fabfbf9
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+12.g025f10d) (21.3)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+12.g025f10d) (1.3.5)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+12.g025f10d) (1.2.5)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+12.g025f10d) (2022.5.0)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+12.g025f10d) (2022.3.0)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+12.g025f10d) (7.0.0)
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+12.g025f10d) (0.55.1)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+12.g025f10d) (3.19.5)
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+12.g025f10d) (4.64.1)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+12.g025f10d) (2022.3.0)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+12.g025f10d) (1.10.0)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+12.g025f10d) (0.4.3)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+12.g025f10d) (1.2.0)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (5.4.1)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (2.2.0)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (0.12.0)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (1.2.0)
Requirement already satisfied: tornado>=6.0.3 in ./.tox/py38-gpu/lib/python3.8/site-packages (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (6.2)
Requirement already satisfied: zict>=0.1.3 in /var/jenkins_home/.local/lib/python3.8/site-packages/zict-2.0.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (2.0.0)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (2.4.0)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (3.1.2)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (5.8.0)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (1.0.4)
Requirement already satisfied: tblib>=1.6.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/tblib-1.7.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (1.7.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (8.1.3)
Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+12.g025f10d) (65.5.1)
Requirement already satisfied: numpy<1.22,>=1.18 in /var/jenkins_home/.local/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+12.g025f10d) (1.20.3)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+12.g025f10d) (0.38.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->merlin-core==0.9.0+12.g025f10d) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+12.g025f10d) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+12.g025f10d) (2022.2.1)
Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+12.g025f10d) (1.52.0)
Requirement already satisfied: absl-py<2.0.0,>=0.9 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+12.g025f10d) (1.2.0)
Requirement already satisfied: locket in /var/jenkins_home/.local/lib/python3.8/site-packages/locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (0.2.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+12.g025f10d) (1.15.0)
Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (1.0.1)
Requirement already satisfied: h2<5,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+12.g025f10d) (4.1.0)
Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+12.g025f10d) (6.0.2)
Requirement already satisfied: MarkupSafe>=2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from jinja2->distributed>=2022.3.0->merlin-core==0.9.0+12.g025f10d) (2.0.1)
Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+12.g025f10d) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+12.g025f10d) (4.0.0)
Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.9.0+12.g025f10d-py3-none-any.whl size=118882 sha256=702ac2cf559e66764bfcc19d06ac9608794696174d9d71b233057d41974182a0
  Stored in directory: /tmp/pip-ephem-wheel-cache-53n8ir9o/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.9.0+12.g025f10d
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-fcg54y_5
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-fcg54y_5
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit fc7f0b2620ae311db13ee8180cbf7226ef09d6de
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: merlin-core>=0.2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from nvtabular==1.6.0+8.gfc7f0b26) (0.9.0+12.g025f10d)
Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from nvtabular==1.6.0+8.gfc7f0b26) (1.8.1)
Collecting merlin-dataloader>=0.0.2
  Downloading merlin-dataloader-0.0.3.tar.gz (48 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 48.3/48.3 kB 1.6 MB/s eta 0:00:00
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (21.3)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (1.3.5)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (1.2.5)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (2022.5.0)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (2022.3.0)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (7.0.0)
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Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (3.19.5)
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (4.64.1)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (2022.3.0)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (1.10.0)
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Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (0.4.3)
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Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (5.4.1)
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Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (65.5.1)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (0.38.1)
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Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (2022.2.1)
Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (1.52.0)
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Requirement already satisfied: locket in /var/jenkins_home/.local/lib/python3.8/site-packages/locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (0.2.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (1.15.0)
Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (1.0.1)
Requirement already satisfied: h2<5,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (4.1.0)
Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (6.0.2)
Requirement already satisfied: MarkupSafe>=2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from jinja2->distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (2.0.1)
Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+8.gfc7f0b26) (4.0.0)
Building wheels for collected packages: nvtabular, merlin-dataloader
  Building wheel for nvtabular (pyproject.toml): started
  Building wheel for nvtabular (pyproject.toml): finished with status 'done'
  Created wheel for nvtabular: filename=nvtabular-1.6.0+8.gfc7f0b26-cp38-cp38-linux_x86_64.whl size=257586 sha256=77842494e19c161e036909959812d03022ddafe0e3def7d2c446eb19427c495a
  Stored in directory: /tmp/pip-ephem-wheel-cache-wszr1uaz/wheels/8f/d9/f9/30f2cdc5bf8787fae6fdfe55afd6e1b493e619ec32c32ec40b
  Building wheel for merlin-dataloader (pyproject.toml): started
  Building wheel for merlin-dataloader (pyproject.toml): finished with status 'done'
  Created wheel for merlin-dataloader: filename=merlin_dataloader-0.0.3-py3-none-any.whl size=37647 sha256=55fe6f87d4a95d18ea6dfb681b4b14b17c917ab49492500a0d076860c29d3dc1
  Stored in directory: /tmp/pip-ephem-wheel-cache-wszr1uaz/wheels/1c/a3/4a/0feebb30e0c8cb7ba7046544390b43c7017a2195232f5305a1
Successfully built nvtabular merlin-dataloader
Installing collected packages: merlin-dataloader, nvtabular
  Attempting uninstall: nvtabular
    Found existing installation: nvtabular 1.1.1
    Not uninstalling nvtabular at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'nvtabular'. No files were found to uninstall.
Successfully installed merlin-dataloader-0.0.3 nvtabular-1.6.0+8.gfc7f0b26
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
collected 886 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss.s                            [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ..........                         [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  3%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_loader.py ................                            [  6%]

tests/unit/tf/test_public_api.py .                                       [  6%]

tests/unit/tf/blocks/test_cross.py ...........                           [  7%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [  8%]

tests/unit/tf/blocks/test_interactions.py ...                            [  9%]

tests/unit/tf/blocks/test_mlp.py ....................................... [ 13%]

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tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

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tests/unit/tf/blocks/retrieval/test_base.py .                            [ 22%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ............            [ 24%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 24%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 24%]

tests/unit/tf/core/test_aggregation.py .........                         [ 25%]

tests/unit/tf/core/test_base.py ..                                       [ 25%]

tests/unit/tf/core/test_combinators.py s.....................            [ 28%]

tests/unit/tf/core/test_encoder.py ..                                    [ 28%]

tests/unit/tf/core/test_index.py ...                                     [ 29%]

tests/unit/tf/core/test_prediction.py ..                                 [ 29%]

tests/unit/tf/core/test_tabular.py ......                                [ 29%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 30%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 30%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 30%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 30%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 30%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 30%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 30%]

tests/unit/tf/examples/test_usecase_accelerate_training_by_lazyadam.py . [ 30%]

[ 30%]

tests/unit/tf/examples/test_usecase_data_parallel.py .                   [ 30%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 31%]

tests/unit/tf/examples/test_usecase_incremental_training_layer_freezing.py . [ 31%]

[ 31%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 31%]

tests/unit/tf/examples/test_usecase_retrieval_with_hpo.py .              [ 31%]

tests/unit/tf/examples/test_usecase_transformers_next_item_prediction.py . [ 31%]

[ 31%]

tests/unit/tf/horovod/test_horovod.py ...                                [ 31%]

tests/unit/tf/inputs/test_base.py .                                      [ 31%]

tests/unit/tf/inputs/test_continuous.py ........                         [ 32%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 36%]

........                                                                 [ 37%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 39%]

tests/unit/tf/layers/test_queue.py ..............                        [ 41%]

tests/unit/tf/losses/test_losses.py .......................              [ 43%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 44%]

tests/unit/tf/metrics/test_metrics_topk.py .........................     [ 47%]

tests/unit/tf/models/test_base.py s.........................             [ 50%]

tests/unit/tf/models/test_benchmark.py ..                                [ 50%]

tests/unit/tf/models/test_ranking.py ..................................  [ 54%]

tests/unit/tf/models/test_retrieval.py ................................. [ 57%]

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tests/unit/tf/outputs/test_base.py ......                                [ 63%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py ..............                 [ 65%]

tests/unit/tf/outputs/test_regression.py ..                              [ 66%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 66%]

tests/unit/tf/outputs/test_topk.py .                                     [ 66%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 66%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 68%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 69%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 69%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 69%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 70%]

tests/unit/tf/transformers/test_block.py ......................          [ 73%]

tests/unit/tf/transformers/test_transforms.py ..........                 [ 74%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 74%]

tests/unit/tf/transforms/test_features.py s............................. [ 77%]

.......................s......                                           [ 81%]

tests/unit/tf/transforms/test_negative_sampling.py .........             [ 82%]

tests/unit/tf/transforms/test_noise.py .....                             [ 82%]

tests/unit/tf/transforms/test_sequence.py ....................           [ 84%]

tests/unit/tf/transforms/test_tensor.py ...                              [ 85%]

tests/unit/tf/utils/test_batch.py ....                                   [ 85%]

tests/unit/tf/utils/test_dataset.py ..                                   [ 86%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 86%]

tests/unit/torch/test_dataset.py .........                               [ 87%]

tests/unit/torch/test_public_api.py .                                    [ 87%]

tests/unit/torch/block/test_base.py ....                                 [ 88%]

tests/unit/torch/block/test_mlp.py .                                     [ 88%]

tests/unit/torch/features/test_continuous.py ..                          [ 88%]

tests/unit/torch/features/test_embedding.py ..............               [ 90%]

tests/unit/torch/features/test_tabular.py ....                           [ 90%]

tests/unit/torch/model/test_head.py ............                         [ 91%]

tests/unit/torch/model/test_model.py ..                                  [ 92%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 93%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 93%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 94%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 10 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 123 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 9 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 89 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 63 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_file_eo_52cj.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/raw/init.py                        0      0   100%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147      8    95%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              175     62    65%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          242     50    79%

merlin/models/tf/core/combinators.py                                   426     53    88%

merlin/models/tf/core/encoder.py                                       175     28    84%

merlin/models/tf/core/index.py                                         106     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     29    90%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         64     10    84%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     32    93%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        764    104    86%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59      6    90%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     36    92%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      4    94%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     42    80%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      2    94%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11593   2349    80%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========= 873 passed, 13 skipped, 1460 warnings in 1806.94s (0:30:06) ==========

/usr/local/lib/python3.8/dist-packages/coverage/data.py:130: CoverageWarning: Data file '/var/jenkins_home/workspace/merlin_models/models/.coverage.10.20.17.231.25130.493813' doesn't seem to be a coverage data file: cannot unpack non-iterable NoneType object

data._warn(str(exc))

___________________________________ summary ____________________________________

py38-gpu: commands succeeded

congratulations :)

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins9204738165026148478.sh

nvidia-merlin-bot · 2022-11-23T19:38:48Z

Click to view CI Results

GitHub pull request #890 of commit 89869a3ee4aa2ebd1e5781194aaff1f001aaf341, no merge conflicts.
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 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
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Checking out Revision 89869a3ee4aa2ebd1e5781194aaff1f001aaf341 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 89869a3ee4aa2ebd1e5781194aaff1f001aaf341 # timeout=10
Commit message: "Merge branch 'main' into clm-test-use-model-pre"
 > git rev-list --no-walk 6b38b9b37f1a8bfde76dc46818e7212bc2035204 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins7590716298507576710.sh
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Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (2.16.1)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (5.9.0)
Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (1.3.10)
Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (22.1.0)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (2.8.2)
Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (24.0.0)
Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: entrypoints in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (0.4)
Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/4/merlin-models-0.9.0+61.g89869a3e.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.15,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.15,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.1.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.1.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==5.0.4,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-models==0.9.0+61.g89869a3e,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,# Editable install with no version control (nvtabular==1.4.0+8.g95e12d347),-e /usr/local/lib/python3.8/dist-packages,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.3,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.9.1,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==2.5.0,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='3178358880'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-h60czd8g
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/core.git /tmp/pip-req-build-h60czd8g
  Resolved https://github.com/NVIDIA-Merlin/core.git to commit 78f1f0b0952fd14b76913b0dd258565c06694abe
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (4.64.1)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (1.10.0)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (7.0.0)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (3.19.5)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (1.2.5)
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (0.55.1)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (21.3)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (1.3.5)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (2022.5.0)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (2022.3.0)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (2022.3.0)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (0.4.3)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (1.2.0)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (5.4.1)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.2.0)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (0.12.0)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.2.0)
Requirement already satisfied: tblib>=1.6.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/tblib-1.7.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.7.0)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (3.1.2)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.0.4)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (5.8.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (8.1.3)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.4.0)
Requirement already satisfied: zict>=0.1.3 in /var/jenkins_home/.local/lib/python3.8/site-packages/zict-2.0.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.0.0)
Requirement already satisfied: tornado>=6.0.3 in ./.tox/py38-gpu/lib/python3.8/site-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (6.2)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+13.g78f1f0b) (0.38.1)
Requirement already satisfied: numpy<1.22,>=1.18 in /var/jenkins_home/.local/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+13.g78f1f0b) (1.20.3)
Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+13.g78f1f0b) (65.5.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->merlin-core==0.9.0+13.g78f1f0b) (3.0.9)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (2022.2.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (2.8.2)
Requirement already satisfied: absl-py<2.0.0,>=0.9 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (1.2.0)
Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (1.52.0)
Requirement already satisfied: locket in /var/jenkins_home/.local/lib/python3.8/site-packages/locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (0.2.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (1.15.0)
Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.0.1)
Requirement already satisfied: h2<5,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (4.1.0)
Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (6.0.2)
Requirement already satisfied: MarkupSafe>=2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from jinja2->distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.0.1)
Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (4.0.0)
Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.9.0+13.g78f1f0b-py3-none-any.whl size=118889 sha256=b08109d9b4ad2ba03789b24ed6cd4686672b045ba1d3877035daced41a66999a
  Stored in directory: /tmp/pip-ephem-wheel-cache-4rdusaut/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.9.0+13.g78f1f0b
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-utkbdc8o
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-utkbdc8o
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit ff186c6fac1bca957d4edd76e8e81c77ca6a649e
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from nvtabular==1.6.0+10.gff186c6f) (1.8.1)
Collecting merlin-dataloader>=0.0.2
  Downloading merlin-dataloader-0.0.3.tar.gz (48 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 48.3/48.3 kB 1.9 MB/s eta 0:00:00
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: merlin-core>=0.2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from nvtabular==1.6.0+10.gff186c6f) (0.9.0+13.g78f1f0b)
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (4.64.1)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (1.10.0)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (7.0.0)
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Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (2022.5.0)
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Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (2022.3.0)
Requirement already satisfied: numpy<1.25.0,>=1.17.3 in /var/jenkins_home/.local/lib/python3.8/site-packages (from scipy->nvtabular==1.6.0+10.gff186c6f) (1.20.3)
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Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (5.4.1)
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Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (0.38.1)
Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (65.5.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (3.0.9)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (2022.2.1)
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Requirement already satisfied: absl-py<2.0.0,>=0.9 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (1.2.0)
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (1.15.0)
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Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (6.0.2)
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Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+10.gff186c6f) (4.0.0)
Building wheels for collected packages: nvtabular, merlin-dataloader
  Building wheel for nvtabular (pyproject.toml): started
  Building wheel for nvtabular (pyproject.toml): finished with status 'done'
  Created wheel for nvtabular: filename=nvtabular-1.6.0+10.gff186c6f-cp38-cp38-linux_x86_64.whl size=257603 sha256=3e8b16906ad26cfe2ffbba1a4172bdd9c8f3a20de956139ccadbc0ab5a2e018e
  Stored in directory: /tmp/pip-ephem-wheel-cache-wo2n3l_f/wheels/8f/d9/f9/30f2cdc5bf8787fae6fdfe55afd6e1b493e619ec32c32ec40b
  Building wheel for merlin-dataloader (pyproject.toml): started
  Building wheel for merlin-dataloader (pyproject.toml): finished with status 'done'
  Created wheel for merlin-dataloader: filename=merlin_dataloader-0.0.3-py3-none-any.whl size=37647 sha256=c5f1dedf3168f5189e0f99a81bc4d94371c0a120284df98bccc1e0cc414a44cf
  Stored in directory: /tmp/pip-ephem-wheel-cache-wo2n3l_f/wheels/1c/a3/4a/0feebb30e0c8cb7ba7046544390b43c7017a2195232f5305a1
Successfully built nvtabular merlin-dataloader
Installing collected packages: merlin-dataloader, nvtabular
  Attempting uninstall: nvtabular
    Found existing installation: nvtabular 1.1.1
    Not uninstalling nvtabular at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'nvtabular'. No files were found to uninstall.
Successfully installed merlin-dataloader-0.0.3 nvtabular-1.6.0+10.gff186c6f
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
collected 886 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss.s                            [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ..........                         [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  3%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_loader.py ................                            [  6%]

tests/unit/tf/test_public_api.py .                                       [  6%]

tests/unit/tf/blocks/test_cross.py ...........                           [  7%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [  8%]

tests/unit/tf/blocks/test_interactions.py ...                            [  9%]

tests/unit/tf/blocks/test_mlp.py ....................................... [ 13%]

............................                                             [ 16%]

tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

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tests/unit/tf/blocks/retrieval/test_base.py .                            [ 22%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ............            [ 24%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 24%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 24%]

tests/unit/tf/core/test_aggregation.py .........                         [ 25%]

tests/unit/tf/core/test_base.py ..                                       [ 25%]

tests/unit/tf/core/test_combinators.py s.....................            [ 28%]

tests/unit/tf/core/test_encoder.py ..                                    [ 28%]

tests/unit/tf/core/test_index.py ...                                     [ 29%]

tests/unit/tf/core/test_prediction.py ..                                 [ 29%]

tests/unit/tf/core/test_tabular.py ......                                [ 29%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 30%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 30%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 30%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 30%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 30%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 30%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 30%]

tests/unit/tf/examples/test_usecase_accelerate_training_by_lazyadam.py . [ 30%]

[ 30%]

tests/unit/tf/examples/test_usecase_data_parallel.py .                   [ 30%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 31%]

tests/unit/tf/examples/test_usecase_incremental_training_layer_freezing.py . [ 31%]

[ 31%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 31%]

tests/unit/tf/examples/test_usecase_retrieval_with_hpo.py .              [ 31%]

tests/unit/tf/examples/test_usecase_transformers_next_item_prediction.py . [ 31%]

[ 31%]

tests/unit/tf/horovod/test_horovod.py ...                                [ 31%]

tests/unit/tf/inputs/test_base.py .                                      [ 31%]

tests/unit/tf/inputs/test_continuous.py ........                         [ 32%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 36%]

........                                                                 [ 37%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 39%]

tests/unit/tf/layers/test_queue.py ..............                        [ 41%]

tests/unit/tf/losses/test_losses.py .......................              [ 43%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 44%]

tests/unit/tf/metrics/test_metrics_topk.py .........................     [ 47%]

tests/unit/tf/models/test_base.py s.........................             [ 50%]

tests/unit/tf/models/test_benchmark.py ..                                [ 50%]

tests/unit/tf/models/test_ranking.py ..................................  [ 54%]

tests/unit/tf/models/test_retrieval.py ................................. [ 57%]

.............................................                            [ 62%]

tests/unit/tf/outputs/test_base.py ......                                [ 63%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py ..............                 [ 65%]

tests/unit/tf/outputs/test_regression.py ..                              [ 66%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 66%]

tests/unit/tf/outputs/test_topk.py .                                     [ 66%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 66%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 68%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 69%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 69%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 69%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 70%]

tests/unit/tf/transformers/test_block.py ......................          [ 73%]

tests/unit/tf/transformers/test_transforms.py ..........                 [ 74%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 74%]

tests/unit/tf/transforms/test_features.py s............................. [ 77%]

.......................s......                                           [ 81%]

tests/unit/tf/transforms/test_negative_sampling.py .........             [ 82%]

tests/unit/tf/transforms/test_noise.py .....                             [ 82%]

tests/unit/tf/transforms/test_sequence.py ....................           [ 84%]

tests/unit/tf/transforms/test_tensor.py ...                              [ 85%]

tests/unit/tf/utils/test_batch.py ....                                   [ 85%]

tests/unit/tf/utils/test_dataset.py ..                                   [ 86%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 86%]

tests/unit/torch/test_dataset.py .........                               [ 87%]

tests/unit/torch/test_public_api.py .                                    [ 87%]

tests/unit/torch/block/test_base.py ....                                 [ 88%]

tests/unit/torch/block/test_mlp.py .                                     [ 88%]

tests/unit/torch/features/test_continuous.py ..                          [ 88%]

tests/unit/torch/features/test_embedding.py ..............               [ 90%]

tests/unit/torch/features/test_tabular.py ....                           [ 90%]

tests/unit/torch/model/test_head.py ............                         [ 91%]

tests/unit/torch/model/test_model.py ..                                  [ 92%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 93%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 93%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 94%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 10 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 123 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 9 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 89 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 63 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filevfx6k54b.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/raw/init.py                        0      0   100%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147      8    95%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              175     62    65%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          242     50    79%

merlin/models/tf/core/combinators.py                                   426     53    88%

merlin/models/tf/core/encoder.py                                       175     28    84%

merlin/models/tf/core/index.py                                         106     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     29    90%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         64     10    84%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     32    93%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        764    104    86%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59      6    90%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     36    92%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      4    94%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     42    80%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      2    94%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11593   2349    80%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========= 873 passed, 13 skipped, 1460 warnings in 1789.26s (0:29:49) ==========

___________________________________ summary ____________________________________

py38-gpu: commands succeeded

congratulations :)

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins2883498024672905939.sh

nvidia-merlin-bot · 2022-11-25T17:26:48Z

Click to view CI Results

GitHub pull request #890 of commit 5e3ea7a5bc49cf9e52840c5a824c8da055c66219, no merge conflicts.
Running as SYSTEM
Setting status of 5e3ea7a5bc49cf9e52840c5a824c8da055c66219 to PENDING with url http://merlin-infra1.nvidia.com:8080/job/merlin_models/1957/ and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/890/*:refs/remotes/origin/pr/890/* # timeout=10
 > git rev-parse 5e3ea7a5bc49cf9e52840c5a824c8da055c66219^{commit} # timeout=10
Checking out Revision 5e3ea7a5bc49cf9e52840c5a824c8da055c66219 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 5e3ea7a5bc49cf9e52840c5a824c8da055c66219 # timeout=10
Commit message: "Merge branch 'main' into clm-test-use-model-pre"
 > git rev-list --no-walk c3ea68e3466ec3cd45c85a3c4b4c06ce1975b004 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins2008788156911887856.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/4/merlin-models-0.9.0+66.g5e3ea7a5.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.16,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.16,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.1.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.1.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==6.0.0,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-models==0.9.0+66.g5e3ea7a5,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,# Editable install with no version control (nvtabular==1.4.0+8.g95e12d347),-e /usr/local/lib/python3.8/dist-packages,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.3,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.10.0,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==3.0.1,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='3612540410'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-vth7j8ry
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/core.git /tmp/pip-req-build-vth7j8ry
  Resolved https://github.com/NVIDIA-Merlin/core.git to commit 78f1f0b0952fd14b76913b0dd258565c06694abe
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (4.64.1)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (1.2.5)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (7.0.0)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (2022.3.0)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (1.3.5)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (1.10.0)
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (0.55.1)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (2022.3.0)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (3.19.5)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+13.g78f1f0b) (2022.5.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+13.g78f1f0b) (21.3)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (1.2.0)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (0.4.3)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.2.0)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.2.0)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (5.4.1)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (0.12.0)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.0.4)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (3.1.2)
Requirement already satisfied: tornado>=6.0.3 in ./.tox/py38-gpu/lib/python3.8/site-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (6.2)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (5.8.0)
Requirement already satisfied: tblib>=1.6.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/tblib-1.7.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.7.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (8.1.3)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.4.0)
Requirement already satisfied: zict>=0.1.3 in /var/jenkins_home/.local/lib/python3.8/site-packages/zict-2.0.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.0.0)
Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+13.g78f1f0b) (65.5.1)
Requirement already satisfied: numpy<1.22,>=1.18 in /var/jenkins_home/.local/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+13.g78f1f0b) (1.20.3)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+13.g78f1f0b) (0.38.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->merlin-core==0.9.0+13.g78f1f0b) (3.0.9)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (2022.2.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (2.8.2)
Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (1.52.0)
Requirement already satisfied: absl-py<2.0.0,>=0.9 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (1.2.0)
Requirement already satisfied: locket in /var/jenkins_home/.local/lib/python3.8/site-packages/locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (0.2.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+13.g78f1f0b) (1.15.0)
Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (1.0.1)
Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (6.0.2)
Requirement already satisfied: h2<5,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (4.1.0)
Requirement already satisfied: MarkupSafe>=2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from jinja2->distributed>=2022.3.0->merlin-core==0.9.0+13.g78f1f0b) (2.0.1)
Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+13.g78f1f0b) (4.0.0)
Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.9.0+13.g78f1f0b-py3-none-any.whl size=118889 sha256=c79510afd49e9c0274f88684a94e8cb86c95118dcfdc49004c62a032d201aaf5
  Stored in directory: /tmp/pip-ephem-wheel-cache-8xo9sd21/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.9.0+13.g78f1f0b
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-jfk2cboc
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-jfk2cboc
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit ee3b7440eb22fad71ad50a6df978e1fa526f4817
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from nvtabular==1.6.0+11.gee3b7440) (1.8.1)
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Collecting merlin-dataloader>=0.0.2
  Downloading merlin-dataloader-0.0.3.tar.gz (48 kB)
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Building wheels for collected packages: nvtabular, merlin-dataloader
  Building wheel for nvtabular (pyproject.toml): started
  Building wheel for nvtabular (pyproject.toml): finished with status 'done'
  Created wheel for nvtabular: filename=nvtabular-1.6.0+11.gee3b7440-cp38-cp38-linux_x86_64.whl size=257603 sha256=16e0e1612472ea8da6c26f65815ffa0ceaf52e317d60014cd94c4ec2319050a0
  Stored in directory: /tmp/pip-ephem-wheel-cache-o0vk1z1f/wheels/8f/d9/f9/30f2cdc5bf8787fae6fdfe55afd6e1b493e619ec32c32ec40b
  Building wheel for merlin-dataloader (pyproject.toml): started
  Building wheel for merlin-dataloader (pyproject.toml): finished with status 'done'
  Created wheel for merlin-dataloader: filename=merlin_dataloader-0.0.3-py3-none-any.whl size=37647 sha256=c2655c59205a79cc440deae6bd26629b512466ded0b73db292ac0aa4a09e6fce
  Stored in directory: /tmp/pip-ephem-wheel-cache-o0vk1z1f/wheels/1c/a3/4a/0feebb30e0c8cb7ba7046544390b43c7017a2195232f5305a1
Successfully built nvtabular merlin-dataloader
Installing collected packages: merlin-dataloader, nvtabular
  Attempting uninstall: nvtabular
    Found existing installation: nvtabular 1.1.1
    Not uninstalling nvtabular at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'nvtabular'. No files were found to uninstall.
Successfully installed merlin-dataloader-0.0.3 nvtabular-1.6.0+11.gee3b7440
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
collected 886 items
tests/unit/config/test_schema.py ....                                    [  0%]

tests/unit/datasets/test_advertising.py .s                               [  0%]

tests/unit/datasets/test_ecommerce.py ..sss.s                            [  1%]

tests/unit/datasets/test_entertainment.py ....sss.                       [  2%]

tests/unit/datasets/test_social.py .                                     [  2%]

tests/unit/datasets/test_synthetic.py ..........                         [  3%]

tests/unit/implicit/test_implicit.py .                                   [  3%]

tests/unit/lightfm/test_lightfm.py .                                     [  3%]

tests/unit/tf/test_core.py ......                                        [  4%]

tests/unit/tf/test_loader.py ................                            [  6%]

tests/unit/tf/test_public_api.py .                                       [  6%]

tests/unit/tf/blocks/test_cross.py ...........                           [  7%]

tests/unit/tf/blocks/test_dlrm.py ..........                             [  8%]

tests/unit/tf/blocks/test_interactions.py ...                            [  9%]

tests/unit/tf/blocks/test_mlp.py ....................................... [ 13%]

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tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]

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tests/unit/tf/blocks/retrieval/test_base.py .                            [ 22%]

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py ..           [ 23%]

tests/unit/tf/blocks/retrieval/test_two_tower.py ............            [ 24%]

tests/unit/tf/blocks/sampling/test_cross_batch.py .                      [ 24%]

tests/unit/tf/blocks/sampling/test_in_batch.py .                         [ 24%]

tests/unit/tf/core/test_aggregation.py .........                         [ 25%]

tests/unit/tf/core/test_base.py ..                                       [ 25%]

tests/unit/tf/core/test_combinators.py s.....................            [ 28%]

tests/unit/tf/core/test_encoder.py ..                                    [ 28%]

tests/unit/tf/core/test_index.py ...                                     [ 29%]

tests/unit/tf/core/test_prediction.py ..                                 [ 29%]

tests/unit/tf/core/test_tabular.py ......                                [ 29%]

tests/unit/tf/examples/test_01_getting_started.py .                      [ 30%]

tests/unit/tf/examples/test_02_dataschema.py .                           [ 30%]

tests/unit/tf/examples/test_03_exploring_different_models.py .           [ 30%]

tests/unit/tf/examples/test_04_export_ranking_models.py .                [ 30%]

tests/unit/tf/examples/test_05_export_retrieval_model.py .               [ 30%]

tests/unit/tf/examples/test_06_advanced_own_architecture.py .            [ 30%]

tests/unit/tf/examples/test_07_train_traditional_models.py .             [ 30%]

tests/unit/tf/examples/test_usecase_accelerate_training_by_lazyadam.py . [ 30%]

[ 30%]

tests/unit/tf/examples/test_usecase_data_parallel.py .                   [ 30%]

tests/unit/tf/examples/test_usecase_ecommerce_session_based.py .         [ 31%]

tests/unit/tf/examples/test_usecase_incremental_training_layer_freezing.py . [ 31%]

[ 31%]

tests/unit/tf/examples/test_usecase_pretrained_embeddings.py .           [ 31%]

tests/unit/tf/examples/test_usecase_retrieval_with_hpo.py .              [ 31%]

tests/unit/tf/examples/test_usecase_transformers_next_item_prediction.py . [ 31%]

[ 31%]

tests/unit/tf/horovod/test_horovod.py ...                                [ 31%]

tests/unit/tf/inputs/test_base.py .                                      [ 31%]

tests/unit/tf/inputs/test_continuous.py ........                         [ 32%]

tests/unit/tf/inputs/test_embedding.py ................................. [ 36%]

........                                                                 [ 37%]

tests/unit/tf/inputs/test_tabular.py ..................                  [ 39%]

tests/unit/tf/layers/test_queue.py ..............                        [ 41%]

tests/unit/tf/losses/test_losses.py .......................              [ 43%]

tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 44%]

tests/unit/tf/metrics/test_metrics_topk.py .........................     [ 47%]

tests/unit/tf/models/test_base.py s.........................             [ 50%]

tests/unit/tf/models/test_benchmark.py ..                                [ 50%]

tests/unit/tf/models/test_ranking.py ..................................  [ 54%]

tests/unit/tf/models/test_retrieval.py ................................. [ 57%]

.............................................                            [ 62%]

tests/unit/tf/outputs/test_base.py ......                                [ 63%]

tests/unit/tf/outputs/test_classification.py ......                      [ 64%]

tests/unit/tf/outputs/test_contrastive.py ..............                 [ 65%]

tests/unit/tf/outputs/test_regression.py ..                              [ 66%]

tests/unit/tf/outputs/test_sampling.py ....                              [ 66%]

tests/unit/tf/outputs/test_topk.py .                                     [ 66%]

tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 66%]

tests/unit/tf/prediction_tasks/test_multi_task.py ................       [ 68%]

tests/unit/tf/prediction_tasks/test_next_item.py .....                   [ 69%]

tests/unit/tf/prediction_tasks/test_regression.py .....                  [ 69%]

tests/unit/tf/prediction_tasks/test_retrieval.py .                       [ 69%]

tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 70%]

tests/unit/tf/transformers/test_block.py ......................          [ 73%]

tests/unit/tf/transformers/test_transforms.py ..........                 [ 74%]

tests/unit/tf/transforms/test_bias.py ..                                 [ 74%]

tests/unit/tf/transforms/test_features.py s............................. [ 77%]

.......................s......                                           [ 81%]

tests/unit/tf/transforms/test_negative_sampling.py .........             [ 82%]

tests/unit/tf/transforms/test_noise.py .....                             [ 82%]

tests/unit/tf/transforms/test_sequence.py ....................           [ 84%]

tests/unit/tf/transforms/test_tensor.py ...                              [ 85%]

tests/unit/tf/utils/test_batch.py ....                                   [ 85%]

tests/unit/tf/utils/test_dataset.py ..                                   [ 86%]

tests/unit/tf/utils/test_tf_utils.py .....                               [ 86%]

tests/unit/torch/test_dataset.py .........                               [ 87%]

tests/unit/torch/test_public_api.py .                                    [ 87%]

tests/unit/torch/block/test_base.py ....                                 [ 88%]

tests/unit/torch/block/test_mlp.py .                                     [ 88%]

tests/unit/torch/features/test_continuous.py ..                          [ 88%]

tests/unit/torch/features/test_embedding.py ..............               [ 90%]

tests/unit/torch/features/test_tabular.py ....                           [ 90%]

tests/unit/torch/model/test_head.py ............                         [ 91%]

tests/unit/torch/model/test_model.py ..                                  [ 92%]

tests/unit/torch/tabular/test_aggregation.py ........                    [ 93%]

tests/unit/torch/tabular/test_tabular.py ...                             [ 93%]

tests/unit/torch/tabular/test_transformations.py .......                 [ 94%]

tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 10 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 123 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 9 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 89 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 63 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filewc2jczg6.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/raw/init.py                        0      0   100%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147      8    95%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              175     62    65%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          242     51    79%

merlin/models/tf/core/combinators.py                                   426     53    88%

merlin/models/tf/core/encoder.py                                       175     28    84%

merlin/models/tf/core/index.py                                         106     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     29    90%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         64     20    69%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     51    89%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        764    104    86%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59     17    71%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     42    90%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      4    94%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     43    79%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      2    94%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11593   2397    79%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

========= 873 passed, 13 skipped, 1460 warnings in 1860.82s (0:31:00) ==========

/usr/local/lib/python3.8/dist-packages/coverage/data.py:130: CoverageWarning: Data file '/var/jenkins_home/workspace/merlin_models/models/.coverage.10.20.17.231.23302.559106' doesn't seem to be a coverage data file: cannot unpack non-iterable NoneType object

data._warn(str(exc))

___________________________________ summary ____________________________________

py38-gpu: commands succeeded

congratulations :)

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_models] $ /bin/bash /tmp/jenkins12230580166320385325.sh

gabrielspmoreira · 2022-11-30T16:27:02Z

Thanks for the fix @oliverholworthy .
Are we going to face the serving issue #889 for any Loader transform that returns a RaggedTensor? If that is the case, what about raising an exception in the Loader when a RaggedTensor is returned by the Loader?

There are other tests that use SequencePredictNext, SequencePredictLast, and SequencePredictRandom as a Loader transform, which I list here.

test_youtube_dnn_retrieval
test_youtube_dnn_retrieval_v2
test_youtube_dnn_v2_export_embeddings
test_youtube_dnn_topk_evaluation
test_seq_predict_next
test_seq_predict_last
test_seq_predict_random

Should those tests be also updated to avoid users using them as template?

nvidia-merlin-bot · 2022-12-01T21:38:42Z

Click to view CI Results

GitHub pull request #890 of commit 4ee01cff977ab2206fb2d5545df123795e9dc8e6, no merge conflicts.
Running as SYSTEM
Setting status of 4ee01cff977ab2206fb2d5545df123795e9dc8e6 to PENDING with url http://merlin-infra1.nvidia.com:8080/job/merlin_models/1987/ and message: 'Pending'
Using context: Jenkins
Building on the built-in node in workspace /var/jenkins_home/jobs/merlin_models/workspace
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
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Checking out Revision 4ee01cff977ab2206fb2d5545df123795e9dc8e6 (detached)
 > git config core.sparsecheckout # timeout=10
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Commit message: "Update youtube dnn tests to use transform as model fit pre"
 > git rev-list --no-walk d6b123af15db859e71344c11e4944b4c262bd146 # timeout=10
[workspace] $ /bin/bash /tmp/jenkins8184337981212883206.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/4/merlin-models-0.9.0+67.g4ee01cff.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.20,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.20,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.1.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.1.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==6.0.0,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-models==0.9.0+67.g4ee01cff,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,# Editable install with no version control (nvtabular==1.4.0+8.g95e12d347),-e /usr/local/lib/python3.8/dist-packages,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.4,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.10.0,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==3.0.1,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='1065045896'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-02mwcfzx
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/core.git /tmp/pip-req-build-02mwcfzx
  Resolved https://github.com/NVIDIA-Merlin/core.git to commit 4f73ff5bd4121c1acaabdc01a123af4f986ffc78
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (3.19.5)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (1.2.5)
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (0.55.1)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (7.0.0)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (2022.3.0)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (1.3.5)
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (4.64.1)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (1.10.0)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (2022.3.0)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (2022.5.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (21.3)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (0.4.3)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (1.2.0)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.2.0)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.2.0)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (5.4.1)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (0.12.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (8.1.3)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.0.4)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.4.0)
Requirement already satisfied: zict>=0.1.3 in /var/jenkins_home/.local/lib/python3.8/site-packages/zict-2.0.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.0.0)
Requirement already satisfied: tornado>=6.0.3 in ./.tox/py38-gpu/lib/python3.8/site-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (6.2)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (5.8.0)
Requirement already satisfied: tblib>=1.6.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/tblib-1.7.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.7.0)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (3.1.2)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+14.g4f73ff5) (0.38.1)
Requirement already satisfied: numpy<1.22,>=1.18 in /var/jenkins_home/.local/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+14.g4f73ff5) (1.20.3)
Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+14.g4f73ff5) (65.5.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->merlin-core==0.9.0+14.g4f73ff5) (3.0.9)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (2022.2.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (2.8.2)
Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (1.52.0)
Requirement already satisfied: absl-py<2.0.0,>=0.9 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (1.2.0)
Requirement already satisfied: locket in /var/jenkins_home/.local/lib/python3.8/site-packages/locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (0.2.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (1.15.0)
Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.0.1)
Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (6.0.2)
Requirement already satisfied: h2<5,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (4.1.0)
Requirement already satisfied: MarkupSafe>=2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from jinja2->distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.0.1)
Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (4.0.0)
Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.9.0+14.g4f73ff5-py3-none-any.whl size=119010 sha256=b912865a43283a91be5a17498de1865eeeff3e1715096d3e01c4beb65bb0e0e5
  Stored in directory: /tmp/pip-ephem-wheel-cache-trr8o9v1/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.9.0+14.g4f73ff5
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-d6mo2q4x
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-d6mo2q4x
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit 51af616069689b3ba57e8842a6f4a20377795df7
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: merlin-core>=0.2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from nvtabular==1.6.0+13.g51af6160) (0.9.0+14.g4f73ff5)
Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from nvtabular==1.6.0+13.g51af6160) (1.8.1)
Collecting merlin-dataloader>=0.0.2
  Downloading merlin-dataloader-0.0.3.tar.gz (48 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 48.3/48.3 kB 1.1 MB/s eta 0:00:00
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
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Building wheels for collected packages: nvtabular, merlin-dataloader
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Successfully installed merlin-dataloader-0.0.3 nvtabular-1.6.0+13.g51af6160
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
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=================================== FAILURES ===================================

____________________ test_youtube_dnn_topk_evaluation[True] ____________________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7ff58bcd23a0>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_youtube_dnn_topk_evaluation(sequence_testing_data: Dataset, run_eagerly):
    to_remove = (
        sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
        .select_by_tag(Tags.CONTINUOUS)
        .column_names
    )
    sequence_testing_data.schema = sequence_testing_data.schema.excluding_by_name(to_remove)

    seq_schema = sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
    target = sequence_testing_data.schema.select_by_tag(Tags.ITEM_ID).column_names[0]
    predict_next = mm.SequencePredictLast(schema=seq_schema, target=target)

    model = mm.YoutubeDNNRetrievalModelV2(
        schema=sequence_testing_data.schema, top_block=mm.MLPBlock([32]), num_sampled=1000
    )

    dataloader = mm.Loader(sequence_testing_data, batch_size=50)

    model, _ = testing_utils.model_test(
        model, dataloader, reload_model=False, fit_kwargs=dict(pre=predict_next)
    )

    # Top-K evaluation
    topk_model = model.to_top_k_encoder(k=20)
    topk_model.compile(run_eagerly=run_eagerly)


  metrics = topk_model.evaluate(dataloader, return_dict=True)


tests/unit/tf/models/test_retrieval.py:990:

merlin/models/tf/models/base.py:988: in evaluate

out = super().evaluate(

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1756: in evaluate

tmp_logs = self.test_function(iterator)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1557: in test_function

return step_function(self, iterator)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1546: in step_function

outputs = model.distribute_strategy.run(run_step, args=(data,))

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1535: in run_step

outputs = model.test_step(data)

merlin/models/tf/models/base.py:753: in test_step

outputs = self.call_train_test(x, y, sample_weight=sample_weight, testing=True)

merlin/models/tf/models/base.py:576: in call_train_test

forward = self(

merlin/models/tf/core/encoder.py:183: in call

return super().call(inputs, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/encoder.py:159: in call

return combinators.call_sequentially(

merlin/models/tf/core/combinators.py:836: in call_sequentially

outputs = call_layer(layer, outputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:437: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/outputs/base.py:136: in call

outputs = super(ModelOutput, self).call(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/outputs/topk.py:323: in call

return tf_utils.call_layer(

merlin/models/tf/utils/tf_utils.py:437: in call_layer

return layer(inputs, *args, **filtered_kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

self = BruteForce(

(ids): <tf.Variable 'ids:0' shape=(51997,) dtype=int32, numpy=array([    0,     1,     2, ..., 51994, 51...      [ 0.00418389,  0.04842576, -0.01663413, ...,  0.02249278,

0.04446541, -0.03718138]], dtype=float32)>

)

inputs = <tf.Tensor: shape=(50, 32), dtype=float32, numpy=

array([[0.        , 0.        , 0.        , ..., 0.03298352, 0.     ...059209],

[0.04745115, 0.        , 0.00045138, ..., 0.01363494, 0.        ,

0.        ]], dtype=float32)>

targets = None, testing = True, k = 20
def call(
    self,
    inputs: tf.Tensor,
    targets: tf.Tensor = None,
    testing: bool = False,
    k: int = None,
) -> Union[Prediction, TopKPrediction]:
    """Compute the scores between the query inputs and all indexed candidates,
    then retrieve the top-k candidates with the highest scores.

    Parameters
    ----------
    inputs : tf.Tensor
        The query embeddings representation
    targets: tf.Tensor
        The tensor of positive candidates
    testing: bool
        Flag that indicates whether in evaluation mode, by default False
    k: int
        Number of candidates to return
    """
    k = k if k is not None else self._k
    if self._candidates is None:
        raise ValueError(
            "You should call the `index` method first to " "set the _candidates index."
        )

    tf.assert_equal(
        tf.shape(inputs)[1],
        tf.shape(self._candidates)[1],
        "Query and candidates vectors must have the same embedding size "
        f"(got query dimension of {tf.shape(inputs)[1]} and candidates"
        f" dimension of {tf.shape(self._candidates)[1]} ",
    )
    scores = self._score(inputs, self._candidates)
    top_scores, top_idx = tf.math.top_k(scores, k=k)
    top_ids = tf.gather(self._ids, top_idx)
    if testing:


      assert targets is not None, ValueError(


            "Targets should be provided during the evaluation mode"
        )

E           AssertionError: Exception encountered when calling layer "brute_force" (type BruteForce).

E

E           Targets should be provided during the evaluation mode

E

E           Call arguments received by layer "brute_force" (type BruteForce):

E             • inputs=tf.Tensor(shape=(50, 32), dtype=float32)

E             • targets=None

E             • testing=True

E             • k=None
merlin/models/tf/outputs/topk.py:221: AssertionError

----------------------------- Captured stdout call -----------------------------
1/1 [==============================] - ETA: 0s - loss: 6.9084 - recall_at_10: 0.0200 - mrr_at_10: 0.0050 - ndcg_at_10: 0.0086 - map_at_10: 0.0050 - precision_at_10: 0.0020 - regularization_loss: 0.0000e+00 - loss_batch: 6.9084����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

1/1 [==============================] - 1s 883ms/step - loss: 6.9084 - recall_at_10: 0.0200 - mrr_at_10: 0.0050 - ndcg_at_10: 0.0086 - map_at_10: 0.0050 - precision_at_10: 0.0020 - regularization_loss: 0.0000e+00 - loss_batch: 6.9084

----------------------------- Captured stderr call -----------------------------

WARNING:tensorflow:Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

------------------------------ Captured log call -------------------------------

WARNING  tensorflow:utils.py:76 Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

___________________ test_youtube_dnn_topk_evaluation[False] ____________________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7ff5899c9a60>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_youtube_dnn_topk_evaluation(sequence_testing_data: Dataset, run_eagerly):
    to_remove = (
        sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
        .select_by_tag(Tags.CONTINUOUS)
        .column_names
    )
    sequence_testing_data.schema = sequence_testing_data.schema.excluding_by_name(to_remove)

    seq_schema = sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
    target = sequence_testing_data.schema.select_by_tag(Tags.ITEM_ID).column_names[0]
    predict_next = mm.SequencePredictLast(schema=seq_schema, target=target)

    model = mm.YoutubeDNNRetrievalModelV2(
        schema=sequence_testing_data.schema, top_block=mm.MLPBlock([32]), num_sampled=1000
    )

    dataloader = mm.Loader(sequence_testing_data, batch_size=50)

    model, _ = testing_utils.model_test(
        model, dataloader, reload_model=False, fit_kwargs=dict(pre=predict_next)
    )

    # Top-K evaluation
    topk_model = model.to_top_k_encoder(k=20)
    topk_model.compile(run_eagerly=run_eagerly)


  metrics = topk_model.evaluate(dataloader, return_dict=True)


tests/unit/tf/models/test_retrieval.py:990:

merlin/models/tf/models/base.py:988: in evaluate

out = super().evaluate(

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1756: in evaluate

tmp_logs = self.test_function(iterator)

../../../.local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:141: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:915: in call

result = self.call(*args, **kwds)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:963: in call

self.initialize(args, kwds, add_initializers_to=initializers)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:785: in initialize

self.stateful_fn.get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2480: in get_concrete_function_internal_garbage_collected

graph_function, _ = self.maybe_define_function(args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2711: in maybe_define_function

graph_function = self.create_graph_function(args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2627: in create_graph_function

func_graph_module.func_graph_from_py_func(

../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:1141: in func_graph_from_py_func

func_outputs = python_func(*func_args, **func_kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:677: in wrapped_fn

out = weak_wrapped_fn().wrapped(*args, **kwds)

../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:1127: in autograph_handler

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:1116: in autograph_handler

return autograph.converted_call(

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_filebpwdrwbl.py:15: in tf__test_function

retval = ag.converted_call(ag.ld(step_function), (ag.ld(self), ag.ld(iterator)), None, fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:377: in converted_call

return call_unconverted(f, args, kwargs, options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:459: in call_unconverted

return f(*args)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1546: in step_function

outputs = model.distribute_strategy.run(run_step, args=(data,))

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self.extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self.call_for_each_replica(fn, args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3689: in call_for_each_replica

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:377: in converted_call

return call_unconverted(f, args, kwargs, options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:458: in call_unconverted

return f(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1535: in run_step

outputs = model.test_step(data)

merlin/models/tf/models/base.py:753: in test_step

outputs = self.call_train_test(x, y, sample_weight=sample_weight, testing=True)

merlin/models/tf/models/base.py:576: in call_train_test

forward = self(

merlin/models/tf/core/encoder.py:183: in call

return super().call(inputs, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:692: in wrapper

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file1ljsfsu3.py:12: in tf__call

retval = ag.converted_call(ag.ld(combinators).call_sequentially, (ag.converted_call(ag.ld(list), (ag.ld(self).to_call,), None, fscope),), dict(inputs=ag.ld(inputs), features=ag__.ld(inputs), targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_filesq1yxdai.py:25: in tf__call_sequentially

ag.for_stmt(ag__.ld(layers), None, loop_body, get_state, set_state, ('outputs',), {'iterate_names': 'layer'})

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:449: in for_stmt

py_for_stmt(iter, extra_test, body, None, None)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:498: in py_for_stmt

body(target)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:464: in protected_body

original_body(protected_iter)

/tmp/autograph_generated_filesq1yxdai.py:23: in loop_body

outputs = ag.converted_call(ag_.ld(call_layer), (ag__.ld(layer), ag__.ld(outputs)), dict(**ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_fileq8qb207q.py:50: in tf__call_layer

retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file1b58_iaa.py:27: in tf____call

outputs = ag__.converted_call(ag__.converted_call(ag__.ld(super), (ag__.ld(ModelOutput), ag__.ld(self)), None, fscope).call, ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:331: in converted_call

return call_unconverted(f, args, kwargs, options, False)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:458: in call_unconverted

return f(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:692: in wrapper

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_filey7675wnn.py:41: in tf__call

retval = ag.converted_call(ag_.ld(tf_utils).call_layer, (ag__.ld(self).to_call, ag__.ld(inputs)), dict(targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_fileq8qb207q.py:50: in tf__call_layer

retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:377: in converted_call

return call_unconverted(f, args, kwargs, options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:458: in call_unconverted

return f(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:692: in wrapper

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file4zz0am8m.py:58: in tf__call

ag.if_stmt(ag.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1341: in if_stmt

_py_if_stmt(cond, body, orelse)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1394: in _py_if_stmt

return body() if cond else orelse()

def if_body_1():
    nonlocal targets, do_return, retval_


  assert (ag__.ld(targets) is not None), ag__.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)


E       AssertionError: in user code:

E

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1557, in test_function  *

E               return step_function(self, iterator)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1546, in step_function  **

E               outputs = model.distribute_strategy.run(run_step, args=(data,))

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 1312, in run

E               return self.extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 2888, in call_for_each_replica

E               return self.call_for_each_replica(fn, args, kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 3689, in call_for_each_replica

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1535, in run_step  **

E               outputs = model.test_step(data)

E           File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 753, in test_step

E               outputs = self.call_train_test(x, y, sample_weight=sample_weight, testing=True)

E           File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 576, in call_train_test

E               forward = self(

E           File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/encoder.py", line 183, in call

E               return super().call(inputs, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 490, in call

E               return super().call(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E               outputs = call_fn(inputs, *args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E               raise new_e.with_traceback(e.traceback) from None

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E               return fn(*args, **kwargs)

E           File "/tmp/autograph_generated_file1ljsfsu3.py", line 12, in tf__call  **

E               retval = ag.converted_call(ag.ld(combinators).call_sequentially, (ag__.converted_call(ag__.ld(list), (ag__.ld(self).to_call,), None, fscope),), dict(inputs=ag__.ld(inputs), features=ag__.ld(inputs), targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

E           File "/tmp/autograph_generated_filesq1yxdai.py", line 25, in tf__call_sequentially  **

E               ag.for_stmt(ag__.ld(layers), None, loop_body, get_state, set_state, ('outputs',), {'iterate_names': 'layer'})

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 449, in for_stmt

E               py_for_stmt(iter, extra_test, body, None, None)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 498, in py_for_stmt

E               body(target)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 464, in protected_body

E               original_body(protected_iter)

E           File "/tmp/autograph_generated_filesq1yxdai.py", line 23, in loop_body

E               outputs = ag.converted_call(ag_.ld(call_layer), (ag__.ld(layer), ag__.ld(outputs)), dict(**ag__.ld(kwargs)), fscope)

E           File "/tmp/autograph_generated_fileq8qb207q.py", line 50, in tf__call_layer  **

E               retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

E           File "/tmp/autograph_generated_file1b58_iaa.py", line 27, in tf____call  **

E               outputs = ag__.converted_call(ag__.converted_call(ag__.ld(super), (ag__.ld(ModelOutput), ag__.ld(self)), None, fscope).call, ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(kwargs)), fscope)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E               outputs = call_fn(inputs, *args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E               raise new_e.with_traceback(e.traceback) from None

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E               return fn(*args, **kwargs)

E           File "/tmp/autograph_generated_filey7675wnn.py", line 41, in tf__call  **

E               retval = ag_.converted_call(ag__.ld(tf_utils).call_layer, (ag__.ld(self).to_call, ag__.ld(inputs)), dict(targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

E           File "/tmp/autograph_generated_fileq8qb207q.py", line 50, in tf__call_layer  **

E               retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E               outputs = call_fn(inputs, *args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E               raise new_e.with_traceback(e.traceback) from None

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E               return fn(*args, **kwargs)

E           File "/tmp/autograph_generated_file4zz0am8m.py", line 58, in tf__call  **

E               ag.if_stmt(ag__.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1341, in if_stmt

E               py_if_stmt(cond, body, orelse)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1394, in py_if_stmt

E               return body() if cond else orelse()

E           File "/tmp/autograph_generated_file4zz0am8m.py", line 39, in if_body_1

E               assert (ag.ld(targets) is not None), ag.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)

E

E           AssertionError: Exception encountered when calling layer "top_k_encoder_1" (type TopKEncoder).

E

E           in user code:

E

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/encoder.py", line 160, in call  *

E                   list(self.to_call),

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/combinators.py", line 836, in call_sequentially  *

E                   outputs = call_layer(layer, outputs, **kwargs)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py", line 437, in call_layer  *

E                   return layer(inputs, *args, **filtered_kwargs)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/outputs/base.py", line 136, in call  *

E                   outputs = super(ModelOutput, self).call(inputs, *args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E                   return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E                   outputs = call_fn(inputs, *args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E                   raise new_e.with_traceback(e.traceback) from None

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E                   return fn(*args, **kwargs)

E               File "/tmp/autograph_generated_filey7675wnn.py", line 41, in tf__call  **

E                   retval = ag_.converted_call(ag__.ld(tf_utils).call_layer, (ag__.ld(self).to_call, ag__.ld(inputs)), dict(targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

E               File "/tmp/autograph_generated_fileq8qb207q.py", line 50, in tf__call_layer  **

E                   retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E                   return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E                   outputs = call_fn(inputs, *args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E                   raise new_e.with_traceback(e.traceback) from None

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E                   return fn(*args, **kwargs)

E               File "/tmp/autograph_generated_file4zz0am8m.py", line 58, in tf__call  **

E                   ag.if_stmt(ag__.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1341, in if_stmt

E                   py_if_stmt(cond, body, orelse)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1394, in py_if_stmt

E                   return body() if cond else orelse()

E               File "/tmp/autograph_generated_file4zz0am8m.py", line 39, in if_body_1

E                   assert (ag.ld(targets) is not None), ag.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)

E

E               AssertionError: Exception encountered when calling layer "item_id_seq/top_k_output" (type TopKOutput).

E

E               in user code:

E

E                   File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/outputs/topk.py", line 324, in call  *

E                       self.to_call, inputs, targets=targets, training=training, testing=testing, **kwargs

E                   File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py", line 437, in call_layer  *

E                       return layer(inputs, *args, **filtered_kwargs)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E                       return fn(*args, **kwargs)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E                       outputs = call_fn(inputs, *args, **kwargs)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E                       raise new_e.with_traceback(e.traceback) from None

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E                       return fn(*args, **kwargs)

E                   File "/tmp/autograph_generated_file4zz0am8m.py", line 58, in tf__call  **

E                       ag.if_stmt(ag__.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1341, in if_stmt

E                       py_if_stmt(cond, body, orelse)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1394, in py_if_stmt

E                       return body() if cond else orelse()

E                   File "/tmp/autograph_generated_file4zz0am8m.py", line 39, in if_body_1

E                       assert (ag.ld(targets) is not None), ag.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)

E

E                   AssertionError: Exception encountered when calling layer "brute_force" (type BruteForce).

E

E                   in user code:

E

E                       File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/outputs/topk.py", line 221, in call  *

E                           assert targets is not None, ValueError(

E

E                       AssertionError: Targets should be provided during the evaluation mode

E

E

E                   Call arguments received by layer "brute_force" (type BruteForce):

E                     • inputs=tf.Tensor(shape=(None, 32), dtype=float32)

E                     • targets=None

E                     • testing=True

E                     • k=None

E

E

E               Call arguments received by layer "item_id_seq/top_k_output" (type TopKOutput):

E                 • inputs=tf.Tensor(shape=(None, 32), dtype=float32)

E                 • targets=None

E                 • training=False

E                 • testing=True

E                 • kwargs={'features': {'item_id_seq': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'categories': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'test_user_id': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_country': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_age': 'tf.Tensor(shape=(None, None), dtype=float32)'}}

E

E

E           Call arguments received by layer "top_k_encoder_1" (type TopKEncoder):

E             • inputs={'item_id_seq': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'categories': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'test_user_id': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_country': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_age': 'tf.Tensor(shape=(None, None), dtype=float32)'}

E             • training=False

E             • testing=True

E             • targets=None

E             • kwargs=<class 'inspect._empty'>
/tmp/__autograph_generated_file4zz0am8m.py:39: AssertionError

----------------------------- Captured stdout call -----------------------------
1/1 [==============================] - ETA: 0s - loss: 6.9080 - recall_at_10: 0.0000e+00 - mrr_at_10: 0.0000e+00 - ndcg_at_10: 0.0000e+00 - map_at_10: 0.0000e+00 - precision_at_10: 0.0000e+00 - regularization_loss: 0.0000e+00 - loss_batch: 6.9080������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

1/1 [==============================] - 1s 862ms/step - loss: 6.9080 - recall_at_10: 0.0000e+00 - mrr_at_10: 0.0000e+00 - ndcg_at_10: 0.0000e+00 - map_at_10: 0.0000e+00 - precision_at_10: 0.0000e+00 - regularization_loss: 0.0000e+00 - loss_batch: 6.9080

----------------------------- Captured stderr call -----------------------------

WARNING:tensorflow:Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

------------------------------ Captured log call -------------------------------

WARNING  tensorflow:utils.py:76 Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 10 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 123 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 9 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 89 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 63 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_filehymg47ty.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/raw/init.py                        0      0   100%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147      8    95%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              175     62    65%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          242     51    79%

merlin/models/tf/core/combinators.py                                   426     53    88%

merlin/models/tf/core/encoder.py                                       175     28    84%

merlin/models/tf/core/index.py                                         106     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     29    90%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         64     20    69%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     51    89%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        764    104    86%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59     17    71%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     42    90%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      4    94%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     43    79%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      2    94%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11593   2397    79%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

==== 2 failed, 871 passed, 13 skipped, 1460 warnings in 1835.52s (0:30:35) =====

/usr/local/lib/python3.8/dist-packages/coverage/data.py:130: CoverageWarning: Data file '/var/jenkins_home/workspace/merlin_models/models/.coverage.10.20.17.231.17761.267542' doesn't seem to be a coverage data file: cannot unpack non-iterable NoneType object

data._warn(str(exc))

ERROR: InvocationError for command /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/bin/python -m pytest --cov-report term --cov merlin -rxs tests/unit (exited with code 1)

___________________________________ summary ____________________________________

ERROR:   py38-gpu: commands failed

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[workspace] $ /bin/bash /tmp/jenkins16601113192696406428.sh

nvidia-merlin-bot · 2022-12-01T22:44:09Z

Click to view CI Results

GitHub pull request #890 of commit 1abd8d5446c68e1a58707757a2d88d8a386847a0, no merge conflicts.
Running as SYSTEM
Setting status of 1abd8d5446c68e1a58707757a2d88d8a386847a0 to PENDING with url http://merlin-infra1.nvidia.com:8080/job/merlin_models/1989/ and message: 'Pending'
Using context: Jenkins
Building on the built-in node in workspace /var/jenkins_home/jobs/merlin_models/workspace
using credential nvidia-merlin-bot
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
 > git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/890/*:refs/remotes/origin/pr/890/* # timeout=10
 > git rev-parse 1abd8d5446c68e1a58707757a2d88d8a386847a0^{commit} # timeout=10
Checking out Revision 1abd8d5446c68e1a58707757a2d88d8a386847a0 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 1abd8d5446c68e1a58707757a2d88d8a386847a0 # timeout=10
Commit message: "Merge branch 'main' into clm-test-use-model-pre"
 > git rev-list --no-walk c7961b4356d809111e06637b1dfb1a60b3e7a342 # timeout=10
[workspace] $ /bin/bash /tmp/jenkins9953957792654785336.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
Requirement already satisfied: nbformat>=5.0.4 in /usr/local/lib/python3.8/dist-packages (from testbook) (5.5.0)
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Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (1.5.5)
Requirement already satisfied: jupyter-client>=6.1.5 in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (7.3.5)
Requirement already satisfied: traitlets>=5.2.2 in /usr/local/lib/python3.8/dist-packages (from nbclient>=0.4.0->testbook) (5.4.0)
Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (4.16.0)
Requirement already satisfied: jupyter_core in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (4.11.1)
Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (2.16.1)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (1.3.10)
Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (22.1.0)
Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (5.9.0)
Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (24.0.0)
Requirement already satisfied: tornado>=6.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: entrypoints in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (0.4)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (2.8.2)
Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
GLOB sdist-make: /var/jenkins_home/workspace/merlin_models/models/setup.py
py38-gpu recreate: /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
py38-gpu installdeps: -rrequirements/dev.txt, tensorflow<2.10
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu inst: /var/jenkins_home/workspace/merlin_models/models/.tox/.tmp/package/4/merlin-models-0.9.0+70.g1abd8d54.zip
WARNING: Discarding $PYTHONPATH from environment, to override specify PYTHONPATH in 'passenv' in your configuration.
py38-gpu installed: absl-py==1.2.0,aiohttp==3.8.1,aiosignal==1.2.0,alabaster==0.7.12,alembic==1.8.1,anyio==3.6.1,appdirs==1.4.4,argon2-cffi==21.3.0,argon2-cffi-bindings==21.2.0,astroid==2.5.6,asttokens==2.0.8,astunparse==1.6.3,asv==0.5.1,asvdb==0.4.2,async-timeout==4.0.2,attrs==22.1.0,autopage==0.5.1,awscli==1.27.20,Babel==2.10.3,backcall==0.2.0,beautifulsoup4==4.11.1,betterproto==1.2.5,black==20.8b1,bleach==5.0.1,bokeh==3.0.2,boto3==1.24.75,botocore==1.29.20,Brotli==1.0.9,build==0.9.0,cachetools==5.2.0,certifi==2019.11.28,cffi==1.15.1,chardet==3.0.4,charset-normalizer==2.1.1,check-manifest==0.48,clang==5.0,click==8.1.3,cliff==4.1.0,cloudpickle==2.2.0,cmaes==0.9.0,cmake==3.24.1.1,cmd2==2.4.2,colorama==0.4.4,colorlog==6.7.0,contourpy==1.0.5,coverage==6.5.0,cuda-python==11.7.1,cupy-cuda117==10.6.0,cycler==0.11.0,Cython==0.29.32,dask==2022.1.1,dbus-python==1.2.16,debugpy==1.6.3,decorator==5.1.1,defusedxml==0.7.1,dill==0.3.5.1,distlib==0.3.6,distributed==2022.5.1,distro==1.7.0,dm-tree==0.1.6,docker-pycreds==0.4.0,docutils==0.16,emoji==1.7.0,entrypoints==0.4,execnet==1.9.0,executing==1.0.0,faiss==1.7.2,faiss-gpu==1.7.2,fastai==2.7.9,fastapi==0.85.0,fastavro==1.6.1,fastcore==1.5.27,fastdownload==0.0.7,fastjsonschema==2.16.1,fastprogress==1.0.3,fastrlock==0.8,feast==0.19.4,fiddle==0.2.2,filelock==3.8.0,flake8==6.0.0,flatbuffers==1.12,fonttools==4.37.3,frozenlist==1.3.1,fsspec==2022.5.0,gast==0.4.0,gevent==21.12.0,geventhttpclient==2.0.2,gitdb==4.0.9,GitPython==3.1.27,google==3.0.0,google-api-core==2.10.1,google-auth==2.11.1,google-auth-oauthlib==0.4.6,google-pasta==0.2.0,googleapis-common-protos==1.52.0,graphviz==0.20.1,greenlet==1.1.3,grpcio==1.41.0,grpcio-channelz==1.49.0,grpcio-reflection==1.48.1,grpclib==0.4.3,h11==0.13.0,h2==4.1.0,h5py==3.7.0,HeapDict==1.0.1,horovod==0.26.1,hpack==4.0.0,httptools==0.5.0,hugectr2onnx==0.0.0,huggingface-hub==0.9.1,hyperframe==6.0.1,idna==2.8,imagesize==1.4.1,implicit==0.6.1,importlib-metadata==4.12.0,importlib-resources==5.9.0,iniconfig==1.1.1,interrogate==1.5.0,ipykernel==6.15.3,ipython==8.5.0,ipython-genutils==0.2.0,ipywidgets==7.7.0,isort==5.10.1,jedi==0.18.1,Jinja2==3.1.2,jmespath==1.0.1,joblib==1.2.0,json5==0.9.10,jsonschema==4.16.0,jupyter==1.0.0,jupyter-cache==0.4.3,jupyter-console==6.4.4,jupyter-core==4.11.1,jupyter-server==1.18.1,jupyter-server-mathjax==0.2.5,jupyter-sphinx==0.3.2,jupyter_client==7.3.5,jupyterlab==3.4.7,jupyterlab-pygments==0.2.2,jupyterlab-widgets==1.1.0,jupyterlab_server==2.15.1,keras==2.9.0,Keras-Preprocessing==1.1.2,kiwisolver==1.4.4,lazy-object-proxy==1.8.0,libclang==14.0.6,libcst==0.4.7,lightfm==1.16,lightgbm==3.3.2,linkify-it-py==1.0.3,llvmlite==0.39.1,locket==1.0.0,lxml==4.9.1,Mako==1.2.4,Markdown==3.4.1,markdown-it-py==1.1.0,MarkupSafe==2.1.1,matplotlib==3.6.0,matplotlib-inline==0.1.6,mccabe==0.7.0,mdit-py-plugins==0.2.8,merlin-core==0.6.0+1.g5926fcf,merlin-models==0.9.0+70.g1abd8d54,merlin-systems==0.5.0+4.g15074ad,mistune==2.0.4,mmh3==3.0.0,mpi4py==3.1.3,msgpack==1.0.4,multidict==6.0.2,mypy==0.991,mypy-extensions==0.4.3,myst-nb==0.13.2,myst-parser==0.15.2,natsort==8.1.0,nbclassic==0.4.3,nbclient==0.6.8,nbconvert==7.0.0,nbdime==3.1.1,nbformat==5.5.0,nest-asyncio==1.5.5,ninja==1.10.2.3,notebook==6.4.12,notebook-shim==0.1.0,numba==0.56.2,numpy==1.22.4,nvidia-pyindex==1.0.9,# Editable install with no version control (nvtabular==1.4.0+8.g95e12d347),-e /usr/local/lib/python3.8/dist-packages,nvtx==0.2.5,oauthlib==3.2.1,oldest-supported-numpy==2022.8.16,onnx==1.12.0,onnxruntime==1.11.1,opt-einsum==3.3.0,optuna==3.0.4,packaging==21.3,pandas==1.3.5,pandavro==1.5.2,pandocfilters==1.5.0,parso==0.8.3,partd==1.3.0,pathspec==0.10.2,pathtools==0.1.2,pbr==5.11.0,pep517==0.13.0,pexpect==4.8.0,pickleshare==0.7.5,Pillow==9.2.0,pkgutil_resolve_name==1.3.10,platformdirs==2.5.2,plotly==5.11.0,pluggy==1.0.0,prettytable==3.5.0,prometheus-client==0.14.1,promise==2.3,prompt-toolkit==3.0.31,proto-plus==1.19.6,protobuf==3.19.5,psutil==5.9.2,ptyprocess==0.7.0,pure-eval==0.2.2,py==1.11.0,pyarrow==7.0.0,pyasn1==0.4.8,pyasn1-modules==0.2.8,pybind11==2.10.0,pycodestyle==2.10.0,pycparser==2.21,pydantic==1.10.2,pydot==1.4.2,pyflakes==3.0.1,Pygments==2.13.0,PyGObject==3.36.0,pynvml==11.4.1,pyparsing==3.0.9,pyperclip==1.8.2,pyrsistent==0.18.1,pytest==7.1.3,pytest-cov==4.0.0,pytest-xdist==3.0.2,python-apt==2.0.0+ubuntu0.20.4.8,python-dateutil==2.8.2,python-dotenv==0.21.0,python-rapidjson==1.8,pytz==2022.2.1,PyYAML==5.4.1,pyzmq==24.0.0,qtconsole==5.4.0,QtPy==2.3.0,regex==2022.9.13,requests==2.22.0,requests-oauthlib==1.3.1,requests-unixsocket==0.2.0,rsa==4.7.2,s3fs==2022.2.0,s3transfer==0.6.0,sacremoses==0.0.53,scikit-build==0.15.0,scikit-learn==1.1.2,scipy==1.8.1,seedir==0.3.0,Send2Trash==1.8.0,sentry-sdk==1.9.8,setproctitle==1.3.2,setuptools-scm==7.0.5,shortuuid==1.0.9,six==1.15.0,sklearn==0.0,smmap==5.0.0,sniffio==1.3.0,snowballstemmer==2.2.0,sortedcontainers==2.4.0,soupsieve==2.3.2.post1,Sphinx==5.3.0,sphinx-multiversion==0.2.4,sphinx-togglebutton==0.3.1,sphinx_external_toc==0.3.0,sphinxcontrib-applehelp==1.0.2,sphinxcontrib-copydirs @ git+https://github.com/mikemckiernan/sphinxcontrib-copydirs.git@bd8c5d79b3f91cf5f1bb0d6995aeca3fe84b670e,sphinxcontrib-devhelp==1.0.2,sphinxcontrib-htmlhelp==2.0.0,sphinxcontrib-jsmath==1.0.1,sphinxcontrib-qthelp==1.0.3,sphinxcontrib-serializinghtml==1.1.5,SQLAlchemy==1.4.44,stack-data==0.5.0,starlette==0.20.4,stevedore==4.1.1,stringcase==1.2.0,supervisor==4.1.0,tabulate==0.8.10,tblib==1.7.0,tdqm==0.0.1,tenacity==8.0.1,tensorboard==2.9.1,tensorboard-data-server==0.6.1,tensorboard-plugin-wit==1.8.1,tensorflow==2.9.2,tensorflow-estimator==2.9.0,tensorflow-gpu==2.9.2,tensorflow-io-gcs-filesystem==0.27.0,tensorflow-metadata==1.10.0,termcolor==2.0.1,terminado==0.15.0,testbook==0.4.2,threadpoolctl==3.1.0,tinycss2==1.1.1,tokenizers==0.10.3,toml==0.10.2,tomli==2.0.1,toolz==0.12.0,torch==1.12.1+cu113,torchmetrics==0.3.2,tornado==6.2,tox==3.26.0,tqdm==4.64.1,traitlets==5.4.0,transformers==4.12.0,transformers4rec==0.1.12+2.gbcc939255,treelite==2.3.0,treelite-runtime==2.3.0,tritonclient==2.25.0,typed-ast==1.5.4,typing-inspect==0.8.0,typing_extensions==4.3.0,uc-micro-py==1.0.1,urllib3==1.26.12,uvicorn==0.18.3,uvloop==0.17.0,versioneer==0.20,virtualenv==20.16.5,wandb==0.13.3,watchfiles==0.17.0,wcwidth==0.2.5,webencodings==0.5.1,websocket-client==1.4.1,websockets==10.3,Werkzeug==2.2.2,widgetsnbextension==3.6.0,wrapt==1.12.1,xgboost==1.6.2,xyzservices==2022.9.0,yarl==1.8.1,zict==2.2.0,zipp==3.8.1,zope.event==4.5.0,zope.interface==5.4.0
py38-gpu run-test-pre: PYTHONHASHSEED='1240368517'
py38-gpu run-test: commands[0] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/core.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/core.git
  Cloning https://github.com/NVIDIA-Merlin/core.git to /tmp/pip-req-build-y6158lvf
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/core.git /tmp/pip-req-build-y6158lvf
  Resolved https://github.com/NVIDIA-Merlin/core.git to commit 4f73ff5bd4121c1acaabdc01a123af4f986ffc78
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (4.64.1)
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (0.55.1)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (3.19.5)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (21.3)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (2022.3.0)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (1.3.5)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (2022.3.0)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (7.0.0)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (1.10.0)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core==0.9.0+14.g4f73ff5) (1.2.5)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core==0.9.0+14.g4f73ff5) (2022.5.0)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (0.4.3)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (1.2.0)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.2.0)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (0.12.0)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.2.0)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (5.4.1)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (5.8.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (8.1.3)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (3.1.2)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.0.4)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.4.0)
Requirement already satisfied: tornado>=6.0.3 in ./.tox/py38-gpu/lib/python3.8/site-packages (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (6.2)
Requirement already satisfied: zict>=0.1.3 in /var/jenkins_home/.local/lib/python3.8/site-packages/zict-2.0.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.0.0)
Requirement already satisfied: tblib>=1.6.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/tblib-1.7.0-py3.8.egg (from distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.7.0)
Requirement already satisfied: setuptools in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+14.g4f73ff5) (65.5.1)
Requirement already satisfied: numpy<1.22,>=1.18 in /var/jenkins_home/.local/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+14.g4f73ff5) (1.20.3)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.tox/py38-gpu/lib/python3.8/site-packages (from numba>=0.54->merlin-core==0.9.0+14.g4f73ff5) (0.38.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->merlin-core==0.9.0+14.g4f73ff5) (3.0.9)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (2022.2.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (2.8.2)
Requirement already satisfied: absl-py<2.0.0,>=0.9 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (1.2.0)
Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow-metadata>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (1.52.0)
Requirement already satisfied: locket in /var/jenkins_home/.local/lib/python3.8/site-packages/locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (0.2.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas<1.4.0dev0,>=1.2.0->merlin-core==0.9.0+14.g4f73ff5) (1.15.0)
Requirement already satisfied: heapdict in /var/jenkins_home/.local/lib/python3.8/site-packages/HeapDict-1.0.1-py3.8.egg (from zict>=0.1.3->distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (1.0.1)
Requirement already satisfied: h2<5,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (4.1.0)
Requirement already satisfied: multidict in /usr/local/lib/python3.8/dist-packages (from grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (6.0.2)
Requirement already satisfied: MarkupSafe>=2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from jinja2->distributed>=2022.3.0->merlin-core==0.9.0+14.g4f73ff5) (2.0.1)
Requirement already satisfied: hyperframe<7,>=6.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (6.0.1)
Requirement already satisfied: hpack<5,>=4.0 in /usr/local/lib/python3.8/dist-packages (from h2<5,>=3.1.0->grpclib->betterproto<2.0.0->merlin-core==0.9.0+14.g4f73ff5) (4.0.0)
Building wheels for collected packages: merlin-core
  Building wheel for merlin-core (pyproject.toml): started
  Building wheel for merlin-core (pyproject.toml): finished with status 'done'
  Created wheel for merlin-core: filename=merlin_core-0.9.0+14.g4f73ff5-py3-none-any.whl size=119010 sha256=9460047c8ca71c5ff8d108c0c8278ca105d481d6b521b53e867c84f4d0798a8d
  Stored in directory: /tmp/pip-ephem-wheel-cache-ufy66jlb/wheels/c8/38/16/a6968787eafcec5fa772148af8408b089562f71af0752e8e84
Successfully built merlin-core
Installing collected packages: merlin-core
  Attempting uninstall: merlin-core
    Found existing installation: merlin-core 0.3.0+12.g78ecddd
    Not uninstalling merlin-core at /var/jenkins_home/.local/lib/python3.8/site-packages, outside environment /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu
    Can't uninstall 'merlin-core'. No files were found to uninstall.
Successfully installed merlin-core-0.9.0+14.g4f73ff5
py38-gpu run-test: commands[1] | python -m pip install --upgrade git+https://github.com/NVIDIA-Merlin/nvtabular.git
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting git+https://github.com/NVIDIA-Merlin/nvtabular.git
  Cloning https://github.com/NVIDIA-Merlin/nvtabular.git to /tmp/pip-req-build-2w73ehzc
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA-Merlin/nvtabular.git /tmp/pip-req-build-2w73ehzc
  Resolved https://github.com/NVIDIA-Merlin/nvtabular.git to commit 51af616069689b3ba57e8842a6f4a20377795df7
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from nvtabular==1.6.0+13.g51af6160) (1.8.1)
Collecting merlin-dataloader>=0.0.2
  Downloading merlin-dataloader-0.0.3.tar.gz (48 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 48.3/48.3 kB 1.6 MB/s eta 0:00:00
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Requirement already satisfied: merlin-core>=0.2.0 in ./.tox/py38-gpu/lib/python3.8/site-packages (from nvtabular==1.6.0+13.g51af6160) (0.9.0+14.g4f73ff5)
Requirement already satisfied: tqdm>=4.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (4.64.1)
Requirement already satisfied: numba>=0.54 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (0.55.1)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (3.19.5)
Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (21.3)
Requirement already satisfied: dask>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (2022.3.0)
Requirement already satisfied: pandas<1.4.0dev0,>=1.2.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (1.3.5)
Requirement already satisfied: distributed>=2022.3.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (2022.3.0)
Requirement already satisfied: pyarrow>=5.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (7.0.0)
Requirement already satisfied: tensorflow-metadata>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (1.10.0)
Requirement already satisfied: betterproto<2.0.0 in /usr/local/lib/python3.8/dist-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (1.2.5)
Requirement already satisfied: fsspec==2022.5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages (from merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (2022.5.0)
Requirement already satisfied: numpy<1.25.0,>=1.17.3 in /var/jenkins_home/.local/lib/python3.8/site-packages (from scipy->nvtabular==1.6.0+13.g51af6160) (1.20.3)
Requirement already satisfied: grpclib in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (0.4.3)
Requirement already satisfied: stringcase in /usr/local/lib/python3.8/dist-packages (from betterproto<2.0.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (1.2.0)
Requirement already satisfied: partd>=0.3.10 in /var/jenkins_home/.local/lib/python3.8/site-packages/partd-1.2.0-py3.8.egg (from dask>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (1.2.0)
Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (0.12.0)
Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from dask>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (2.2.0)
Requirement already satisfied: pyyaml>=5.3.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/PyYAML-5.4.1-py3.8-linux-x86_64.egg (from dask>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (5.4.1)
Requirement already satisfied: psutil>=5.0 in /var/jenkins_home/.local/lib/python3.8/site-packages/psutil-5.8.0-py3.8-linux-x86_64.egg (from distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (5.8.0)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (8.1.3)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (3.1.2)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (1.0.4)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /var/jenkins_home/.local/lib/python3.8/site-packages/sortedcontainers-2.4.0-py3.8.egg (from distributed>=2022.3.0->merlin-core>=0.2.0->nvtabular==1.6.0+13.g51af6160) (2.4.0)
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Building wheels for collected packages: nvtabular, merlin-dataloader
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  Created wheel for nvtabular: filename=nvtabular-1.6.0+13.g51af6160-cp38-cp38-linux_x86_64.whl size=257601 sha256=8f4f51fc0fa8769241d0be78179d4eaaf32a301b00ebe626371ab156be7071b4
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  Building wheel for merlin-dataloader (pyproject.toml): started
  Building wheel for merlin-dataloader (pyproject.toml): finished with status 'done'
  Created wheel for merlin-dataloader: filename=merlin_dataloader-0.0.3-py3-none-any.whl size=37647 sha256=b5aab26d6bc083ca8425db44ecebd8769dc41d21fa89382f90615f477bf8de6e
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Successfully built nvtabular merlin-dataloader
Installing collected packages: merlin-dataloader, nvtabular
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    Found existing installation: nvtabular 1.1.1
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Successfully installed merlin-dataloader-0.0.3 nvtabular-1.6.0+13.g51af6160
py38-gpu run-test: commands[2] | python -m pytest --cov-report term --cov merlin -rxs tests/unit
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.3, pluggy-1.0.0
cachedir: .tox/py38-gpu/.pytest_cache
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.5.0, xdist-3.0.2, cov-4.0.0
collected 886 items
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tests/unit/tf/blocks/retrieval/test_two_tower.py ............            [ 24%]

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tests/unit/tf/metrics/test_metrics_popularity.py .....                   [ 44%]

tests/unit/tf/metrics/test_metrics_topk.py .........................     [ 47%]

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tests/unit/tf/models/test_ranking.py ..................................  [ 54%]

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tests/unit/tf/prediction_tasks/test_classification.py ..                 [ 66%]

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tests/unit/tf/prediction_tasks/test_sampling.py ......                   [ 70%]

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tests/unit/tf/utils/test_tf_utils.py .....                               [ 86%]

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tests/unit/torch/block/test_base.py ....                                 [ 88%]

tests/unit/torch/block/test_mlp.py .                                     [ 88%]

tests/unit/torch/features/test_continuous.py ..                          [ 88%]

tests/unit/torch/features/test_embedding.py ..............               [ 90%]

tests/unit/torch/features/test_tabular.py ....                           [ 90%]

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tests/unit/utils/test_schema_utils.py ................................   [ 97%]

tests/unit/xgb/test_xgboost.py ....................                      [100%]
=================================== FAILURES ===================================

____________________ test_youtube_dnn_topk_evaluation[True] ____________________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f9b859acfa0>

run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_youtube_dnn_topk_evaluation(sequence_testing_data: Dataset, run_eagerly):
    to_remove = (
        sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
        .select_by_tag(Tags.CONTINUOUS)
        .column_names
    )
    sequence_testing_data.schema = sequence_testing_data.schema.excluding_by_name(to_remove)

    seq_schema = sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
    target = sequence_testing_data.schema.select_by_tag(Tags.ITEM_ID).column_names[0]
    predict_next = mm.SequencePredictLast(schema=seq_schema, target=target)

    model = mm.YoutubeDNNRetrievalModelV2(
        schema=sequence_testing_data.schema, top_block=mm.MLPBlock([32]), num_sampled=1000
    )

    dataloader = mm.Loader(sequence_testing_data, batch_size=50)

    model, _ = testing_utils.model_test(
        model, dataloader, reload_model=False, fit_kwargs=dict(pre=predict_next)
    )

    # Top-K evaluation
    topk_model = model.to_top_k_encoder(k=20)
    topk_model.compile(run_eagerly=run_eagerly)


  metrics = topk_model.evaluate(dataloader, return_dict=True)


tests/unit/tf/models/test_retrieval.py:990:

merlin/models/tf/models/base.py:1021: in evaluate

out = super().evaluate(

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1756: in evaluate

tmp_logs = self.test_function(iterator)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1557: in test_function

return step_function(self, iterator)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1546: in step_function

outputs = model.distribute_strategy.run(run_step, args=(data,))

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3689: in _call_for_each_replica

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:595: in wrapper

return func(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1535: in run_step

outputs = model.test_step(data)

merlin/models/tf/models/base.py:786: in test_step

outputs = self.call_train_test(x, y, sample_weight=sample_weight, testing=True)

merlin/models/tf/models/base.py:609: in call_train_test

forward = self(

merlin/models/tf/core/encoder.py:185: in call

return super().call(inputs, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/core/encoder.py:161: in call

return combinators.call_sequentially(

merlin/models/tf/core/combinators.py:836: in call_sequentially

outputs = call_layer(layer, outputs, **kwargs)

merlin/models/tf/utils/tf_utils.py:437: in call_layer

return layer(inputs, *args, **filtered_kwargs)

merlin/models/tf/outputs/base.py:136: in call

outputs = super(ModelOutput, self).call(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:96: in error_handler

raise e

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

merlin/models/tf/outputs/topk.py:323: in call

return tf_utils.call_layer(

merlin/models/tf/utils/tf_utils.py:437: in call_layer

return layer(inputs, *args, **filtered_kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

self = BruteForce(

(ids): <tf.Variable 'ids:0' shape=(51997,) dtype=int32, numpy=array([    0,     1,     2, ..., 51994, 51...      [ 0.04657779, -0.03347921, -0.04878134, ..., -0.03663499,

-0.00389455,  0.03033167]], dtype=float32)>

)

inputs = <tf.Tensor: shape=(50, 32), dtype=float32, numpy=

array([[0.        , 0.01061668, 0.05421533, ..., 0.        , 0.     ...      ],

[0.        , 0.01625308, 0.        , ..., 0.        , 0.        ,

0.        ]], dtype=float32)>

targets = None, testing = True, k = 20
def call(
    self,
    inputs: tf.Tensor,
    targets: tf.Tensor = None,
    testing: bool = False,
    k: int = None,
) -> Union[Prediction, TopKPrediction]:
    """Compute the scores between the query inputs and all indexed candidates,
    then retrieve the top-k candidates with the highest scores.

    Parameters
    ----------
    inputs : tf.Tensor
        The query embeddings representation
    targets: tf.Tensor
        The tensor of positive candidates
    testing: bool
        Flag that indicates whether in evaluation mode, by default False
    k: int
        Number of candidates to return
    """
    k = k if k is not None else self._k
    if self._candidates is None:
        raise ValueError(
            "You should call the `index` method first to " "set the _candidates index."
        )

    tf.assert_equal(
        tf.shape(inputs)[1],
        tf.shape(self._candidates)[1],
        "Query and candidates vectors must have the same embedding size "
        f"(got query dimension of {tf.shape(inputs)[1]} and candidates"
        f" dimension of {tf.shape(self._candidates)[1]} ",
    )
    scores = self._score(inputs, self._candidates)
    top_scores, top_idx = tf.math.top_k(scores, k=k)
    top_ids = tf.gather(self._ids, top_idx)
    if testing:


      assert targets is not None, ValueError(


            "Targets should be provided during the evaluation mode"
        )

E           AssertionError: Exception encountered when calling layer "brute_force" (type BruteForce).

E

E           Targets should be provided during the evaluation mode

E

E           Call arguments received by layer "brute_force" (type BruteForce):

E             • inputs=tf.Tensor(shape=(50, 32), dtype=float32)

E             • targets=None

E             • testing=True

E             • k=None
merlin/models/tf/outputs/topk.py:221: AssertionError

----------------------------- Captured stdout call -----------------------------
1/1 [==============================] - ETA: 0s - loss: 6.9072 - recall_at_10: 0.0200 - mrr_at_10: 0.0050 - ndcg_at_10: 0.0086 - map_at_10: 0.0050 - precision_at_10: 0.0020 - regularization_loss: 0.0000e+00 - loss_batch: 6.9072����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

1/1 [==============================] - 1s 867ms/step - loss: 6.9072 - recall_at_10: 0.0200 - mrr_at_10: 0.0050 - ndcg_at_10: 0.0086 - map_at_10: 0.0050 - precision_at_10: 0.0020 - regularization_loss: 0.0000e+00 - loss_batch: 6.9072

----------------------------- Captured stderr call -----------------------------

WARNING:tensorflow:Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

------------------------------ Captured log call -------------------------------

WARNING  tensorflow:utils.py:76 Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

___________________ test_youtube_dnn_topk_evaluation[False] ____________________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f9b76426df0>

run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_youtube_dnn_topk_evaluation(sequence_testing_data: Dataset, run_eagerly):
    to_remove = (
        sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
        .select_by_tag(Tags.CONTINUOUS)
        .column_names
    )
    sequence_testing_data.schema = sequence_testing_data.schema.excluding_by_name(to_remove)

    seq_schema = sequence_testing_data.schema.select_by_tag(Tags.SEQUENCE)
    target = sequence_testing_data.schema.select_by_tag(Tags.ITEM_ID).column_names[0]
    predict_next = mm.SequencePredictLast(schema=seq_schema, target=target)

    model = mm.YoutubeDNNRetrievalModelV2(
        schema=sequence_testing_data.schema, top_block=mm.MLPBlock([32]), num_sampled=1000
    )

    dataloader = mm.Loader(sequence_testing_data, batch_size=50)

    model, _ = testing_utils.model_test(
        model, dataloader, reload_model=False, fit_kwargs=dict(pre=predict_next)
    )

    # Top-K evaluation
    topk_model = model.to_top_k_encoder(k=20)
    topk_model.compile(run_eagerly=run_eagerly)


  metrics = topk_model.evaluate(dataloader, return_dict=True)


tests/unit/tf/models/test_retrieval.py:990:

merlin/models/tf/models/base.py:1021: in evaluate

out = super().evaluate(

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1756: in evaluate

tmp_logs = self.test_function(iterator)

../../../.local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:141: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:915: in call

result = self.call(*args, **kwds)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:963: in call

self.initialize(args, kwds, add_initializers_to=initializers)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:785: in initialize

self.stateful_fn.get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2480: in get_concrete_function_internal_garbage_collected

graph_function, _ = self.maybe_define_function(args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2711: in maybe_define_function

graph_function = self.create_graph_function(args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2627: in create_graph_function

func_graph_module.func_graph_from_py_func(

../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:1141: in func_graph_from_py_func

func_outputs = python_func(*func_args, **func_kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:677: in wrapped_fn

out = weak_wrapped_fn().wrapped(*args, **kwds)

../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:1127: in autograph_handler

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:1116: in autograph_handler

return autograph.converted_call(

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file_3b6figl.py:15: in tf__test_function

retval = ag.converted_call(ag.ld(step_function), (ag.ld(self), ag.ld(iterator)), None, fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:377: in converted_call

return call_unconverted(f, args, kwargs, options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:459: in call_unconverted

return f(*args)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1546: in step_function

outputs = model.distribute_strategy.run(run_step, args=(data,))

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1312: in run

return self.extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2888: in call_for_each_replica

return self.call_for_each_replica(fn, args, kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3689: in call_for_each_replica

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:377: in converted_call

return call_unconverted(f, args, kwargs, options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:458: in call_unconverted

return f(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:1535: in run_step

outputs = model.test_step(data)

merlin/models/tf/models/base.py:786: in test_step

outputs = self.call_train_test(x, y, sample_weight=sample_weight, testing=True)

merlin/models/tf/models/base.py:609: in call_train_test

forward = self(

merlin/models/tf/core/encoder.py:185: in call

return super().call(inputs, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/training.py:490: in call

return super().call(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:692: in wrapper

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file4a_a1n2k.py:12: in tf__call

retval = ag.converted_call(ag.ld(combinators).call_sequentially, (ag.converted_call(ag.ld(list), (ag.ld(self).to_call,), None, fscope),), dict(inputs=ag.ld(inputs), features=ag__.ld(inputs), targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_filejjccgyn6.py:25: in tf__call_sequentially

ag.for_stmt(ag__.ld(layers), None, loop_body, get_state, set_state, ('outputs',), {'iterate_names': 'layer'})

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:449: in for_stmt

py_for_stmt(iter, extra_test, body, None, None)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:498: in py_for_stmt

body(target)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:464: in protected_body

original_body(protected_iter)

/tmp/autograph_generated_filejjccgyn6.py:23: in loop_body

outputs = ag.converted_call(ag_.ld(call_layer), (ag__.ld(layer), ag__.ld(outputs)), dict(**ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file61qut3wj.py:50: in tf__call_layer

retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_fileq_ayzycg.py:27: in tf____call

outputs = ag__.converted_call(ag__.converted_call(ag__.ld(super), (ag__.ld(ModelOutput), ag__.ld(self)), None, fscope).call, ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:331: in converted_call

return call_unconverted(f, args, kwargs, options, False)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:458: in call_unconverted

return f(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:692: in wrapper

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_filepyja25np.py:41: in tf__call

retval = ag.converted_call(ag_.ld(tf_utils).call_layer, (ag__.ld(self).to_call, ag__.ld(inputs)), dict(targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file61qut3wj.py:50: in tf__call_layer

retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:377: in converted_call

return call_unconverted(f, args, kwargs, options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:458: in call_unconverted

return f(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:60: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/engine/base_layer.py:1014: in call

outputs = call_fn(inputs, *args, **kwargs)

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:146: in error_handler

raise new_e.with_traceback(e.traceback) from None

../../../.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:92: in error_handler

return fn(*args, **kwargs)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:692: in wrapper

raise e.ag_error_metadata.to_exception(e)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:689: in wrapper

return converted_call(f, args, kwargs, options=options)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:439: in converted_call

result = converted_f(*effective_args, **kwargs)

/tmp/autograph_generated_file8aw0ml8p.py:58: in tf__call

ag.if_stmt(ag.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1341: in if_stmt

_py_if_stmt(cond, body, orelse)

../../../.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py:1394: in _py_if_stmt

return body() if cond else orelse()

def if_body_1():
    nonlocal retval_, targets, do_return


  assert (ag__.ld(targets) is not None), ag__.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)


E       AssertionError: in user code:

E

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1557, in test_function  *

E               return step_function(self, iterator)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1546, in step_function  **

E               outputs = model.distribute_strategy.run(run_step, args=(data,))

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 1312, in run

E               return self.extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 2888, in call_for_each_replica

E               return self.call_for_each_replica(fn, args, kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 3689, in call_for_each_replica

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 1535, in run_step  **

E               outputs = model.test_step(data)

E           File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 786, in test_step

E               outputs = self.call_train_test(x, y, sample_weight=sample_weight, testing=True)

E           File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/models/base.py", line 609, in call_train_test

E               forward = self(

E           File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/encoder.py", line 185, in call

E               return super().call(inputs, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/training.py", line 490, in call

E               return super().call(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E               outputs = call_fn(inputs, *args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E               raise new_e.with_traceback(e.traceback) from None

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E               return fn(*args, **kwargs)

E           File "/tmp/autograph_generated_file4a_a1n2k.py", line 12, in tf__call  **

E               retval = ag.converted_call(ag.ld(combinators).call_sequentially, (ag__.converted_call(ag__.ld(list), (ag__.ld(self).to_call,), None, fscope),), dict(inputs=ag__.ld(inputs), features=ag__.ld(inputs), targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

E           File "/tmp/autograph_generated_filejjccgyn6.py", line 25, in tf__call_sequentially  **

E               ag.for_stmt(ag__.ld(layers), None, loop_body, get_state, set_state, ('outputs',), {'iterate_names': 'layer'})

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 449, in for_stmt

E               py_for_stmt(iter, extra_test, body, None, None)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 498, in py_for_stmt

E               body(target)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 464, in protected_body

E               original_body(protected_iter)

E           File "/tmp/autograph_generated_filejjccgyn6.py", line 23, in loop_body

E               outputs = ag.converted_call(ag_.ld(call_layer), (ag__.ld(layer), ag__.ld(outputs)), dict(**ag__.ld(kwargs)), fscope)

E           File "/tmp/autograph_generated_file61qut3wj.py", line 50, in tf__call_layer  **

E               retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

E           File "/tmp/autograph_generated_fileq_ayzycg.py", line 27, in tf____call  **

E               outputs = ag__.converted_call(ag__.converted_call(ag__.ld(super), (ag__.ld(ModelOutput), ag__.ld(self)), None, fscope).call, ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(kwargs)), fscope)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E               outputs = call_fn(inputs, *args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E               raise new_e.with_traceback(e.traceback) from None

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E               return fn(*args, **kwargs)

E           File "/tmp/autograph_generated_filepyja25np.py", line 41, in tf__call  **

E               retval = ag_.converted_call(ag__.ld(tf_utils).call_layer, (ag__.ld(self).to_call, ag__.ld(inputs)), dict(targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

E           File "/tmp/autograph_generated_file61qut3wj.py", line 50, in tf__call_layer  **

E               retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E               return fn(*args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E               outputs = call_fn(inputs, *args, **kwargs)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E               raise new_e.with_traceback(e.traceback) from None

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E               return fn(*args, **kwargs)

E           File "/tmp/autograph_generated_file8aw0ml8p.py", line 58, in tf__call  **

E               ag.if_stmt(ag__.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1341, in if_stmt

E               py_if_stmt(cond, body, orelse)

E           File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1394, in py_if_stmt

E               return body() if cond else orelse()

E           File "/tmp/autograph_generated_file8aw0ml8p.py", line 39, in if_body_1

E               assert (ag.ld(targets) is not None), ag.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)

E

E           AssertionError: Exception encountered when calling layer "top_k_encoder_1" (type TopKEncoder).

E

E           in user code:

E

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/encoder.py", line 162, in call  *

E                   list(self.to_call),

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/combinators.py", line 836, in call_sequentially  *

E                   outputs = call_layer(layer, outputs, **kwargs)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py", line 437, in call_layer  *

E                   return layer(inputs, *args, **filtered_kwargs)

E               File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/outputs/base.py", line 136, in call  *

E                   outputs = super(ModelOutput, self).call(inputs, *args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E                   return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E                   outputs = call_fn(inputs, *args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E                   raise new_e.with_traceback(e.traceback) from None

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E                   return fn(*args, **kwargs)

E               File "/tmp/autograph_generated_filepyja25np.py", line 41, in tf__call  **

E                   retval = ag_.converted_call(ag__.ld(tf_utils).call_layer, (ag__.ld(self).to_call, ag__.ld(inputs)), dict(targets=ag__.ld(targets), training=ag__.ld(training), testing=ag__.ld(testing), **ag__.ld(kwargs)), fscope)

E               File "/tmp/autograph_generated_file61qut3wj.py", line 50, in tf__call_layer  **

E                   retval = ag_.converted_call(ag__.ld(layer), ((ag__.ld(inputs),) + tuple(ag__.ld(args))), dict(**ag__.ld(filtered_kwargs)), fscope)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E                   return fn(*args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E                   outputs = call_fn(inputs, *args, **kwargs)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E                   raise new_e.with_traceback(e.traceback) from None

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E                   return fn(*args, **kwargs)

E               File "/tmp/autograph_generated_file8aw0ml8p.py", line 58, in tf__call  **

E                   ag.if_stmt(ag__.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1341, in if_stmt

E                   py_if_stmt(cond, body, orelse)

E               File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1394, in py_if_stmt

E                   return body() if cond else orelse()

E               File "/tmp/autograph_generated_file8aw0ml8p.py", line 39, in if_body_1

E                   assert (ag.ld(targets) is not None), ag.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)

E

E               AssertionError: Exception encountered when calling layer "item_id_seq/top_k_output" (type TopKOutput).

E

E               in user code:

E

E                   File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/outputs/topk.py", line 324, in call  *

E                       self.to_call, inputs, targets=targets, training=training, testing=testing, **kwargs

E                   File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py", line 437, in call_layer  *

E                       return layer(inputs, *args, **filtered_kwargs)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 60, in error_handler  **

E                       return fn(*args, **kwargs)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/engine/base_layer.py", line 1014, in call

E                       outputs = call_fn(inputs, *args, **kwargs)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 146, in error_handler

E                       raise new_e.with_traceback(e.traceback) from None

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler

E                       return fn(*args, **kwargs)

E                   File "/tmp/autograph_generated_file8aw0ml8p.py", line 58, in tf__call  **

E                       ag.if_stmt(ag__.ld(testing), if_body_1, else_body_1, get_state_1, set_state_1, ('do_return', 'retval_', 'targets'), 2)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1341, in if_stmt

E                       py_if_stmt(cond, body, orelse)

E                   File "/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/operators/control_flow.py", line 1394, in py_if_stmt

E                       return body() if cond else orelse()

E                   File "/tmp/autograph_generated_file8aw0ml8p.py", line 39, in if_body_1

E                       assert (ag.ld(targets) is not None), ag.converted_call(ag__.ld(ValueError), ('Targets should be provided during the evaluation mode',), None, fscope)

E

E                   AssertionError: Exception encountered when calling layer "brute_force" (type BruteForce).

E

E                   in user code:

E

E                       File "/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/outputs/topk.py", line 221, in call  *

E                           assert targets is not None, ValueError(

E

E                       AssertionError: Targets should be provided during the evaluation mode

E

E

E                   Call arguments received by layer "brute_force" (type BruteForce):

E                     • inputs=tf.Tensor(shape=(None, 32), dtype=float32)

E                     • targets=None

E                     • testing=True

E                     • k=None

E

E

E               Call arguments received by layer "item_id_seq/top_k_output" (type TopKOutput):

E                 • inputs=tf.Tensor(shape=(None, 32), dtype=float32)

E                 • targets=None

E                 • training=False

E                 • testing=True

E                 • kwargs={'features': {'item_id_seq': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'categories': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'test_user_id': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_country': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_age': 'tf.Tensor(shape=(None, None), dtype=float32)'}}

E

E

E           Call arguments received by layer "top_k_encoder_1" (type TopKEncoder):

E             • inputs={'item_id_seq': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'categories': ('tf.Tensor(shape=(None, None), dtype=int64)', 'tf.Tensor(shape=(None, None), dtype=int32)'), 'test_user_id': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_country': 'tf.Tensor(shape=(None, None), dtype=int64)', 'user_age': 'tf.Tensor(shape=(None, None), dtype=float32)'}

E             • training=False

E             • testing=True

E             • targets=None

E             • kwargs=<class 'inspect._empty'>
/tmp/__autograph_generated_file8aw0ml8p.py:39: AssertionError

----------------------------- Captured stdout call -----------------------------
1/1 [==============================] - ETA: 0s - loss: 6.9076 - recall_at_10: 0.0200 - mrr_at_10: 0.0020 - ndcg_at_10: 0.0058 - map_at_10: 0.0020 - precision_at_10: 0.0020 - regularization_loss: 0.0000e+00 - loss_batch: 6.9076����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

1/1 [==============================] - 1s 863ms/step - loss: 6.9076 - recall_at_10: 0.0200 - mrr_at_10: 0.0020 - ndcg_at_10: 0.0058 - map_at_10: 0.0020 - precision_at_10: 0.0020 - regularization_loss: 0.0000e+00 - loss_batch: 6.9076

----------------------------- Captured stderr call -----------------------------

WARNING:tensorflow:Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

------------------------------ Captured log call -------------------------------

WARNING  tensorflow:utils.py:76 Gradients do not exist for variables ['retrieval_model_v2/output_layer_bias:0'] when minimizing the loss. If you're using model.compile(), did you forget to provide a lossargument?

=============================== warnings summary ===============================

../../../../../usr/lib/python3/dist-packages/requests/init.py:89

/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!

warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../.local/lib/python3.8/site-packages/flatbuffers/compat.py:19

/var/jenkins_home/.local/lib/python3.8/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses

import imp
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.

'nearest': pil_image.NEAREST,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.

'bilinear': pil_image.BILINEAR,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.

'bicubic': pil_image.BICUBIC,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.

'hamming': pil_image.HAMMING,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.

'box': pil_image.BOX,
../../../.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.

'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning

tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 10 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 6 warnings

tests/unit/tf/core/test_index.py: 8 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 6 warnings

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 38 warnings

tests/unit/tf/models/test_retrieval.py: 123 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning

tests/unit/tf/transformers/test_block.py: 16 warnings

tests/unit/tf/transforms/test_bias.py: 2 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_noise.py: 1 warning

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 9 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 3 warnings

tests/unit/xgb/test_xgboost.py: 18 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 3 warnings

tests/unit/datasets/test_entertainment.py: 4 warnings

tests/unit/datasets/test_social.py: 1 warning

tests/unit/datasets/test_synthetic.py: 9 warnings

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_core.py: 6 warnings

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/test_cross.py: 5 warnings

tests/unit/tf/blocks/test_dlrm.py: 9 warnings

tests/unit/tf/blocks/test_interactions.py: 2 warnings

tests/unit/tf/blocks/test_mlp.py: 60 warnings

tests/unit/tf/blocks/test_optimizer.py: 30 warnings

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 11 warnings

tests/unit/tf/core/test_aggregation.py: 6 warnings

tests/unit/tf/core/test_base.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 7 warnings

tests/unit/tf/core/test_index.py: 3 warnings

tests/unit/tf/core/test_prediction.py: 2 warnings

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_base.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 7 warnings

tests/unit/tf/inputs/test_embedding.py: 20 warnings

tests/unit/tf/inputs/test_tabular.py: 18 warnings

tests/unit/tf/models/test_base.py: 28 warnings

tests/unit/tf/models/test_benchmark.py: 2 warnings

tests/unit/tf/models/test_ranking.py: 36 warnings

tests/unit/tf/models/test_retrieval.py: 91 warnings

tests/unit/tf/outputs/test_base.py: 6 warnings

tests/unit/tf/outputs/test_classification.py: 6 warnings

tests/unit/tf/outputs/test_contrastive.py: 19 warnings

tests/unit/tf/outputs/test_regression.py: 2 warnings

tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings

tests/unit/tf/transformers/test_block.py: 10 warnings

tests/unit/tf/transforms/test_features.py: 10 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings

tests/unit/tf/transforms/test_sequence.py: 15 warnings

tests/unit/tf/utils/test_batch.py: 7 warnings

tests/unit/tf/utils/test_dataset.py: 2 warnings

tests/unit/torch/block/test_base.py: 4 warnings

tests/unit/torch/block/test_mlp.py: 1 warning

tests/unit/torch/features/test_continuous.py: 1 warning

tests/unit/torch/features/test_embedding.py: 4 warnings

tests/unit/torch/features/test_tabular.py: 4 warnings

tests/unit/torch/model/test_head.py: 12 warnings

tests/unit/torch/model/test_model.py: 2 warnings

tests/unit/torch/tabular/test_aggregation.py: 6 warnings

tests/unit/torch/tabular/test_transformations.py: 2 warnings

tests/unit/xgb/test_xgboost.py: 17 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/datasets/test_ecommerce.py: 1 warning

tests/unit/datasets/test_entertainment.py: 1 warning

tests/unit/implicit/test_implicit.py: 1 warning

tests/unit/lightfm/test_lightfm.py: 1 warning

tests/unit/tf/test_loader.py: 1 warning

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings

tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings

tests/unit/tf/core/test_combinators.py: 11 warnings

tests/unit/tf/core/test_encoder.py: 2 warnings

tests/unit/tf/core/test_prediction.py: 1 warning

tests/unit/tf/horovod/test_horovod.py: 1 warning

tests/unit/tf/inputs/test_continuous.py: 4 warnings

tests/unit/tf/inputs/test_embedding.py: 9 warnings

tests/unit/tf/inputs/test_tabular.py: 8 warnings

tests/unit/tf/models/test_ranking.py: 20 warnings

tests/unit/tf/models/test_retrieval.py: 10 warnings

tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings

tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings

tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings

tests/unit/xgb/test_xgboost.py: 12 warnings

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].

warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export

tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:970: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/blocks/retrieval/test_two_tower.py: 1 warning

tests/unit/tf/core/test_index.py: 4 warnings

tests/unit/tf/horovod/test_horovod.py: 3 warnings

tests/unit/tf/models/test_retrieval.py: 63 warnings

tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings

tests/unit/tf/utils/test_batch.py: 2 warnings

/tmp/autograph_generated_fileufd6f96g.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead

ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/core/test_combinators.py::test_parallel_block_select_by_tags

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/tabular.py:602: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working

elif isinstance(self.feature_names, collections.Sequence):
tests/unit/tf/core/test_encoder.py: 1 warning

tests/unit/tf/core/test_index.py: 5 warnings

tests/unit/tf/models/test_retrieval.py: 30 warnings

tests/unit/tf/utils/test_batch.py: 4 warnings

tests/unit/tf/utils/test_dataset.py: 1 warning

/var/jenkins_home/workspace/merlin_models/models/merlin/models/utils/dataset.py:75: DeprecationWarning: unique_rows_by_features is deprecated and will be removed in a future version. Please use unique_by_tag instead.

warnings.warn(
tests/unit/tf/models/test_base.py::test_model_pre_post[True]

tests/unit/tf/models/test_base.py::test_model_pre_post[False]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]

tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.

return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]

tests/unit/tf/models/test_base.py::test_freeze_sequential_block

tests/unit/tf/models/test_base.py::test_freeze_unfreeze

tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks

/var/jenkins_home/.local/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.

super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_base.py::test_retrieval_model_query

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_v2_export_embeddings

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[True]

tests/unit/tf/models/test_retrieval.py::test_youtube_dnn_topk_evaluation[False]

tests/unit/tf/transformers/test_block.py::test_retrieval_transformer[True]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/utils/tf_utils.py:298: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack

tensor_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(tensor)))
tests/unit/tf/models/test_ranking.py::test_deepfm_model_only_categ_feats[False]

tests/unit/tf/models/test_ranking.py::test_deepfm_model_categ_and_continuous_feats[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_3/parallel_block_2/sequential_block_3/sequential_block_2/private__dense_1/dense_1/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]

tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:569: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag

return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_as_classfication_model[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_causal_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/prepare_transformer_inputs_1/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/bert_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask_1/GatherV2:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/boolean_mask/GatherV2:0", shape=(None, 48), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/prepare_transformer_inputs_5/RaggedToTensor/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_3:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Reshape_2:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_1:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/tf/transformers/test_block.py::test_transformer_with_masked_language_modeling_check_eval_masked[False]

/var/jenkins_home/.local/lib/python3.8/site-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/gpt2_block/replace_masked_embeddings/RaggedWhere/RaggedTile_2/Reshape_3:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/concat_features/RaggedConcat/Slice_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("gradient_tape/model/concat_features/RaggedConcat/Shape_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.

warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block

/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)

return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client

tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]

tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]

tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple

tests/unit/xgb/test_xgboost.py::TestEvals::test_default

tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid

tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data

/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:344: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].

warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective

/var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/lib/python3.8/site-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first

self.make_current()
tests/unit/xgb/test_xgboost.py: 14 warnings

/usr/local/lib/python3.8/dist-packages/xgboost/dask.py:884: RuntimeWarning: coroutine 'Client._wait_for_workers' was never awaited

client.wait_for_workers(n_workers)

Enable tracemalloc to get traceback where the object was allocated.

See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.
tests/unit/xgb/test_xgboost.py: 11 warnings

/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:1183: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.

mask = pd.Series(mask)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
---------- coverage: platform linux, python 3.8.10-final-0 -----------

Name                                                                 Stmts   Miss  Cover
merlin/datasets/init.py                                              2      0   100%

merlin/datasets/advertising/init.py                                  2      0   100%

merlin/datasets/advertising/criteo/init.py                           0      0   100%

merlin/datasets/advertising/criteo/dataset.py                           79     49    38%

merlin/datasets/advertising/criteo/transformed/init.py               0      0   100%

merlin/datasets/ecommerce/init.py                                    4      0   100%

merlin/datasets/ecommerce/aliccp/init.py                             0      0   100%

merlin/datasets/ecommerce/aliccp/dataset.py                            141     87    38%

merlin/datasets/ecommerce/aliccp/raw/init.py                         0      0   100%

merlin/datasets/ecommerce/aliccp/transformed/init.py                 0      0   100%

merlin/datasets/ecommerce/booking/init.py                            0      0   100%

merlin/datasets/ecommerce/booking/dataset.py                           127    100    21%

merlin/datasets/ecommerce/booking/raw/init.py                        0      0   100%

merlin/datasets/ecommerce/booking/transformed/init.py                0      0   100%

merlin/datasets/ecommerce/dressipi/init.py                           0      0   100%

merlin/datasets/ecommerce/dressipi/dataset.py                           45     37    18%

merlin/datasets/ecommerce/dressipi/preprocessed/init.py              0      0   100%

merlin/datasets/ecommerce/large/init.py                              0      0   100%

merlin/datasets/ecommerce/small/init.py                              0      0   100%

merlin/datasets/ecommerce/transactions/init.py                       0      0   100%

merlin/datasets/entertainment/init.py                                2      0   100%

merlin/datasets/entertainment/movielens/1m-raw/init.py               0      0   100%

merlin/datasets/entertainment/movielens/1m-raw/ratings/init.py       0      0   100%

merlin/datasets/entertainment/movielens/1m/init.py                   0      0   100%

merlin/datasets/entertainment/movielens/25m/init.py                  0      0   100%

merlin/datasets/entertainment/movielens/100k/init.py                 0      0   100%

merlin/datasets/entertainment/movielens/init.py                      0      0   100%

merlin/datasets/entertainment/movielens/dataset.py                     152    122    20%

merlin/datasets/entertainment/music_streaming/init.py                0      0   100%

merlin/datasets/social/init.py                                       0      0   100%

merlin/datasets/synthetic.py                                           147      8    95%

merlin/datasets/testing/init.py                                      0      0   100%

merlin/datasets/testing/sequence_testing/init.py                     0      0   100%

merlin/models/init.py                                                2      0   100%

merlin/models/_version.py                                              354    205    42%

merlin/models/api.py                                                    14      5    64%

merlin/models/config/init.py                                         0      0   100%

merlin/models/config/schema.py                                          62      0   100%

merlin/models/implicit/init.py                                      27      4    85%

merlin/models/io.py                                                     15      0   100%

merlin/models/lightfm/init.py                                       23      0   100%

merlin/models/loader/init.py                                         0      0   100%

merlin/models/loader/backend.py                                        379     41    89%

merlin/models/loader/dataframe_iter.py                                  21     17    19%

merlin/models/loader/tf_utils.py                                        57     27    53%

merlin/models/loader/utils.py                                           40     15    62%

merlin/models/tf/init.py                                            70      0   100%

merlin/models/tf/blocks/init.py                                      0      0   100%

merlin/models/tf/blocks/cross.py                                        44      0   100%

merlin/models/tf/blocks/dlrm.py                                         49      2    96%

merlin/models/tf/blocks/experts.py                                      99     17    83%

merlin/models/tf/blocks/interaction.py                                 108     40    63%

merlin/models/tf/blocks/mlp.py                                         117      7    94%

merlin/models/tf/blocks/optimizer.py                                   173     12    93%

merlin/models/tf/blocks/retrieval/init.py                            0      0   100%

merlin/models/tf/blocks/retrieval/base.py                              175     62    65%

merlin/models/tf/blocks/retrieval/matrix_factorization.py               35      1    97%

merlin/models/tf/blocks/retrieval/two_tower.py                          30      0   100%

merlin/models/tf/blocks/sampling/init.py                             0      0   100%

merlin/models/tf/blocks/sampling/base.py                                29      2    93%

merlin/models/tf/blocks/sampling/cross_batch.py                         46      2    96%

merlin/models/tf/blocks/sampling/in_batch.py                            35      0   100%

merlin/models/tf/blocks/sampling/queue.py                              115     12    90%

merlin/models/tf/core/init.py                                        0      0   100%

merlin/models/tf/core/aggregation.py                                   241     45    81%

merlin/models/tf/core/base.py                                          242     51    79%

merlin/models/tf/core/combinators.py                                   426     53    88%

merlin/models/tf/core/encoder.py                                       182     28    85%

merlin/models/tf/core/index.py                                         106     16    85%

merlin/models/tf/core/prediction.py                                     50      1    98%

merlin/models/tf/core/tabular.py                                       280     29    90%

merlin/models/tf/distributed/init.py                                 0      0   100%

merlin/models/tf/distributed/backend.py                                  9      2    78%

merlin/models/tf/inputs/init.py                                      0      0   100%

merlin/models/tf/inputs/base.py                                         64     20    69%

merlin/models/tf/inputs/continuous.py                                   39      3    92%

merlin/models/tf/inputs/embedding.py                                   458     51    89%

merlin/models/tf/loader.py                                             268     94    65%

merlin/models/tf/losses/init.py                                      4      0   100%

merlin/models/tf/losses/base.py                                          9      0   100%

merlin/models/tf/losses/listwise.py                                     13      0   100%

merlin/models/tf/losses/pairwise.py                                    115      1    99%

merlin/models/tf/metrics/init.py                                     2      0   100%

merlin/models/tf/metrics/evaluation.py                                 105     48    54%

merlin/models/tf/metrics/topk.py                                       198     48    76%

merlin/models/tf/models/init.py                                      0      0   100%

merlin/models/tf/models/base.py                                        778    104    87%

merlin/models/tf/models/benchmark.py                                    16      0   100%

merlin/models/tf/models/ranking.py                                      67      3    96%

merlin/models/tf/models/retrieval.py                                    78      4    95%

merlin/models/tf/models/utils.py                                        10      1    90%

merlin/models/tf/outputs/init.py                                     0      0   100%

merlin/models/tf/outputs/base.py                                       123     17    86%

merlin/models/tf/outputs/classification.py                              91      1    99%

merlin/models/tf/outputs/contrastive.py                                147     10    93%

merlin/models/tf/outputs/regression.py                                   9      0   100%

merlin/models/tf/outputs/sampling/init.py                            0      0   100%

merlin/models/tf/outputs/sampling/base.py                               78     21    73%

merlin/models/tf/outputs/sampling/in_batch.py                           37      1    97%

merlin/models/tf/outputs/sampling/popularity.py                         27      1    96%

merlin/models/tf/outputs/topk.py                                        98      6    94%

merlin/models/tf/prediction_tasks/init.py                            0      0   100%

merlin/models/tf/prediction_tasks/base.py                              207     37    82%

merlin/models/tf/prediction_tasks/classification.py                     68     17    75%

merlin/models/tf/prediction_tasks/multi.py                               7      0   100%

merlin/models/tf/prediction_tasks/next_item.py                          59     17    71%

merlin/models/tf/prediction_tasks/regression.py                         35      2    94%

merlin/models/tf/prediction_tasks/retrieval.py                          73      3    96%

merlin/models/tf/transformers/init.py                                0      0   100%

merlin/models/tf/transformers/block.py                                 101      2    98%

merlin/models/tf/transformers/transforms.py                             63      0   100%

merlin/models/tf/transforms/init.py                                  0      0   100%

merlin/models/tf/transforms/bias.py                                    111      9    92%

merlin/models/tf/transforms/features.py                                435     42    90%

merlin/models/tf/transforms/negative_sampling.py                        77      3    96%

merlin/models/tf/transforms/noise.py                                    43      1    98%

merlin/models/tf/transforms/regularization.py                           17      1    94%

merlin/models/tf/transforms/sequence.py                                282     42    85%

merlin/models/tf/transforms/tensor.py                                  158     13    92%

merlin/models/tf/typing.py                                               7      0   100%

merlin/models/tf/utils/init.py                                       0      0   100%

merlin/models/tf/utils/batch_utils.py                                   85      4    95%

merlin/models/tf/utils/repr_utils.py                                    69      4    94%

merlin/models/tf/utils/search_utils.py                                  34     22    35%

merlin/models/tf/utils/testing_utils.py                                206     36    83%

merlin/models/tf/utils/tf_utils.py                                     209     43    79%

merlin/models/torch/init.py                                         12      0   100%

merlin/models/torch/block/init.py                                    0      0   100%

merlin/models/torch/block/base.py                                      167     32    81%

merlin/models/torch/block/mlp.py                                        38      5    87%

merlin/models/torch/dataset.py                                          68      5    93%

merlin/models/torch/features/init.py                                 0      0   100%

merlin/models/torch/features/base.py                                     4      0   100%

merlin/models/torch/features/continuous.py                              22      0   100%

merlin/models/torch/features/embedding.py                              165     12    93%

merlin/models/torch/features/tabular.py                                 65      8    88%

merlin/models/torch/losses.py                                           28     28     0%

merlin/models/torch/model/init.py                                    0      0   100%

merlin/models/torch/model/base.py                                      286     65    77%

merlin/models/torch/model/prediction_task.py                            24      0   100%

merlin/models/torch/tabular/init.py                                  0      0   100%

merlin/models/torch/tabular/aggregation.py                              75      0   100%

merlin/models/torch/tabular/base.py                                    247     39    84%

merlin/models/torch/tabular/transformations.py                          67      3    96%

merlin/models/torch/typing.py                                            6      0   100%

merlin/models/torch/utils/init.py                                    0      0   100%

merlin/models/torch/utils/data_utils.py                                117    117     0%

merlin/models/torch/utils/examples_utils.py                             39     39     0%

merlin/models/torch/utils/torch_utils.py                                80     22    72%

merlin/models/utils/init.py                                          0      0   100%

merlin/models/utils/constants.py                                         3      0   100%

merlin/models/utils/dataset.py                                          38      4    89%

merlin/models/utils/dependencies.py                                     26     19    27%

merlin/models/utils/doc_utils.py                                        10      0   100%

merlin/models/utils/example_utils.py                                    31      2    94%

merlin/models/utils/misc_utils.py                                      118     90    24%

merlin/models/utils/nvt_utils.py                                        27     24    11%

merlin/models/utils/registry.py                                        101     29    71%

merlin/models/utils/schema_utils.py                                     90     19    79%

merlin/models/xgb/init.py                                          124      4    97%
TOTAL                                                                11614   2397    79%
=========================== short test summary info ============================

SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:64: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:80: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:94: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP

SKIPPED [1] tests/unit/datasets/test_ecommerce.py:115: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR

SKIPPED [5] ../../../.local/lib/python3.8/site-packages/tensorflow/python/framework/test_util.py:2746: Not a test.

==== 2 failed, 871 passed, 13 skipped, 1465 warnings in 1939.37s (0:32:19) =====

/usr/local/lib/python3.8/dist-packages/coverage/data.py:130: CoverageWarning: Data file '/var/jenkins_home/workspace/merlin_models/models/.coverage.10.20.17.231.28032.582536' doesn't seem to be a coverage data file: cannot unpack non-iterable NoneType object

data._warn(str(exc))

ERROR: InvocationError for command /var/jenkins_home/workspace/merlin_models/models/.tox/py38-gpu/bin/python -m pytest --cov-report term --cov merlin -rxs tests/unit (exited with code 1)

___________________________________ summary ____________________________________

ERROR:   py38-gpu: commands failed

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[workspace] $ /bin/bash /tmp/jenkins16957447490430359165.sh

This reverts commit 1eef7b8.

oliverholworthy · 2022-12-02T21:14:36Z

Are we going to face the serving issue #889 for any Loader transform that returns a RaggedTensor?

Yes we'll face this issue for any loader with ragged or sparse tensors in the input.

If that is the case, what about raising an exception in the Loader when a RaggedTensor is returned by the Loader?

I've added a check to the train_step to raise an exception if the inputs contain ragged or sparse tensors. And updated all the tests that started failing as a result of this assertion.

oliverholworthy · 2022-12-06T18:01:30Z

rerun tests

gabrielspmoreira

@oliverholworthy I saw you updated the YouTubeDNN tests to avoid sequence-based Loader transforms.
I think for the following tests, we should remove the use_loader fixture, and only test them without the Loader, so that users don't expect them to work properly with the Loader:

test_seq_predict_next
test_seq_predict_last
test_seq_predict_random

What do you think?

gabrielspmoreira · 2022-12-07T17:15:15Z

merlin/models/tf/models/base.py

+                for k in x:
+                    if isinstance(x[k], (tf.RaggedTensor, tf.SparseTensor)):
+                        raise ValueError(
+                            "Training with RaggedTensor or SparseTensor inputs is unsupported. "


Suggested change

"Training with RaggedTensor or SparseTensor inputs is unsupported. "

"Training with RaggedTensor or SparseTensor inputs is supported "

"for training and evaluation, but not for inference "

"(with Triton Inference Server or TFServing) because the "

"input feature names are lost in SavedModel when ragged "

and sparse tensors are fed as input."

updated the message with more detail for clarity. changed unsupported to not supported. And added the explanation for why.

gabrielspmoreira · 2022-12-07T17:15:49Z

merlin/models/tf/models/base.py

+                    if isinstance(x[k], (tf.RaggedTensor, tf.SparseTensor)):
+                        raise ValueError(
+                            "Training with RaggedTensor or SparseTensor inputs is unsupported. "
+                            "Please update your loader to pass dense tensors. "


Suggested change

"Please update your loader to pass dense tensors. "

"Please update your loader to pass dense tensors so that you can save the model, "

"and serve it correctly".

added something along these lines, but needed to split across more lines due to line length constraints

oliverholworthy · 2022-12-07T18:41:13Z

I've removed the loader tests from the test_seq_predict_* tests as suggested to avoid any confusion from those looking at these tests. If we can figure out how to make it work with ragged/sparse inputs in future we might be able to add this back.

github-actions · 2022-12-12T15:53:25Z

Documentation preview

https://nvidia-merlin.github.io/models/review/pr-890

gabrielspmoreira

Sounds good to me

…his (#890) * Support `tuple` return typee from `pre` arg to `evaluate`, `predict` * Update CLM transformer test to use `pre` instead of Loader `transform` * Update youtube dnn tests to use transform as model fit pre * Add `pre` to ModelBlock fit/evaluate * Revert "Add `pre` to ModelBlock fit/evaluate" This reverts commit 1eef7b8. * Raise exception if ragged/sparse tensors are passed at training time. * Update model_test helper to avoid passing ragged tensors to `fit` * Handle x and y in model_test * Change process_lists param to False by default * Convert to tuple in test loader * Move order of ragged tensor assertion to before train_pre call * expand dims in test_classification * pass transform as pre in test in batch negatives * Update continuous and retrieval tests * Remove test of sequence predict functions with loader * Update error message about ragged tensors for clarity * Add explanation about why the input types are restricted * Rename dataset to dataloader in model_test Co-authored-by: rnyak <ronayak@hotmail.com>

…guage modeling (#909) * add the inference fix to ReplaceMaskedEmbeddings * first solution for inference support * updates based on PR comments * Apply suggestions from code review Co-authored-by: Gabriel Moreira <gmoreira@nvidia.com> * Use merlin-dataloader package (#845) * Use merlin-dataloader package * remove torch.dataset in favor of merlin.loader.torch * update dressipi notebook * minor clean up * Completely removes models DataLoader * Installs merlin-dataloader in github actions * Adds back the stop method * dataloader can produce sparse tensors using value counts * remove data files * fix torch tests * add missing target to dlrm test * use loader.peek() * add some comments to help understand horovod tests * make sparse tensors optional * cleanup * fix spelling * fix merge * replace while loop with for loop in horovod test * use loader context mananger * Update according to dataloader changes #80 * restore tox.ini * restore gh workflow * revert generator changes * Restore documentation build (#916) - Change Python 3.9.7 to 3.8. - Update the versions of the GH actions. - Update pre-commit config file to get flake8 from GitHub instead of GitLab. * Support `tuple` return type from model `pre` and update test to use this (#890) * Support `tuple` return typee from `pre` arg to `evaluate`, `predict` * Update CLM transformer test to use `pre` instead of Loader `transform` * Update youtube dnn tests to use transform as model fit pre * Add `pre` to ModelBlock fit/evaluate * Revert "Add `pre` to ModelBlock fit/evaluate" This reverts commit 1eef7b8. * Raise exception if ragged/sparse tensors are passed at training time. * Update model_test helper to avoid passing ragged tensors to `fit` * Handle x and y in model_test * Change process_lists param to False by default * Convert to tuple in test loader * Move order of ragged tensor assertion to before train_pre call * expand dims in test_classification * pass transform as pre in test in batch negatives * Update continuous and retrieval tests * Remove test of sequence predict functions with loader * Update error message about ragged tensors for clarity * Add explanation about why the input types are restricted * Rename dataset to dataloader in model_test Co-authored-by: rnyak <ronayak@hotmail.com> * add assertion check to TransformerInferenceHiddenState Co-authored-by: Gabriel Moreira <gmoreira@nvidia.com> Co-authored-by: edknv <109497216+edknv@users.noreply.github.com> Co-authored-by: mikemckiernan <mmckiernan@nvidia.com> Co-authored-by: Oliver Holworthy <oholworthy@nvidia.com> Co-authored-by: rnyak <ronayak@hotmail.com>

oliverholworthy added 2 commits November 16, 2022 16:15

Support tuple return typee from pre arg to evaluate, predict

b698224

Update CLM transformer test to use pre instead of Loader transform

94b2855

oliverholworthy added the chore Maintenance for the repository label Nov 16, 2022

oliverholworthy added this to the Merlin 22.12 milestone Nov 16, 2022

oliverholworthy self-assigned this Nov 16, 2022

Merge branch 'main' into clm-test-use-model-pre

03a063d

rnyak requested a review from gabrielspmoreira November 21, 2022 16:08

Merge branch 'main' into clm-test-use-model-pre

66fb21c

Merge branch 'main' into clm-test-use-model-pre

7cf0f5f

Merge branch 'main' into clm-test-use-model-pre

89869a3

Merge branch 'main' into clm-test-use-model-pre

5e3ea7a

oliverholworthy added 2 commits December 1, 2022 21:05

Update youtube dnn tests to use transform as model fit pre

4ee01cf

Merge branch 'main' into clm-test-use-model-pre

1abd8d5

oliverholworthy added 8 commits December 2, 2022 10:21

Add pre to ModelBlock fit/evaluate

1eef7b8

Revert "Add pre to ModelBlock fit/evaluate"

f58e075

This reverts commit 1eef7b8.

Raise exception if ragged/sparse tensors are passed at training time.

c499616

Update model_test helper to avoid passing ragged tensors to fit

b95d000

Handle x and y in model_test

f617a50

Change process_lists param to False by default

19eefb4

Convert to tuple in test loader

a4b26b3

Move order of ragged tensor assertion to before train_pre call

1f42cfe

oliverholworthy added 3 commits December 2, 2022 19:53

expand dims in test_classification

a332e25

pass transform as pre in test in batch negatives

37cc84d

Update continuous and retrieval tests

17ef3d5

gabrielspmoreira reviewed Dec 7, 2022

View reviewed changes

oliverholworthy added 3 commits December 7, 2022 18:28

Remove test of sequence predict functions with loader

03f6885

Update error message about ragged tensors for clarity

50921b8

Add explanation about why the input types are restricted

deef749

Merge branch 'main' into clm-test-use-model-pre

d68f218

Rename dataset to dataloader in model_test

72e96da

oliverholworthy requested a review from gabrielspmoreira December 13, 2022 10:41

marcromeyn approved these changes Dec 13, 2022

View reviewed changes

gabrielspmoreira approved these changes Dec 13, 2022

View reviewed changes

oliverholworthy merged commit a8ab140 into NVIDIA-Merlin:main Dec 13, 2022

oliverholworthy deleted the clm-test-use-model-pre branch December 13, 2022 14:38

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Support `tuple` return type from model `pre` and update test to use this #890

Support `tuple` return type from model `pre` and update test to use this #890

oliverholworthy commented Nov 16, 2022

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gabrielspmoreira commented Nov 30, 2022

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oliverholworthy commented Dec 2, 2022

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edited

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oliverholworthy commented Dec 7, 2022

github-actions bot commented Dec 12, 2022

gabrielspmoreira left a comment

-                            "Training with RaggedTensor or SparseTensor inputs is unsupported. "
+                            "Training with RaggedTensor or SparseTensor inputs is supported "
+                            "for training and evaluation, but not for inference "
+                            "(with Triton Inference Server or TFServing) because the "
+                            "input feature names are lost in SavedModel when ragged "
+                            and sparse tensors are fed as input."

	"Please update your loader to pass dense tensors. "
	"Please update your loader to pass dense tensors so that you can save the model, "
	"and serve it correctly".

Support tuple return type from model pre and update test to use this #890

Support tuple return type from model pre and update test to use this #890

Conversation

oliverholworthy commented Nov 16, 2022

Goals ⚽

Implementation Details 🚧

Testing Details 🔍

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Support `tuple` return type from model `pre` and update test to use this #890

Support `tuple` return type from model `pre` and update test to use this #890

---------- coverage: platform linux, python 3.8.10-final-0 -----------
Name Stmts Miss Cover

---------- coverage: platform linux, python 3.8.10-final-0 -----------
Name Stmts Miss Cover

---------- coverage: platform linux, python 3.8.10-final-0 -----------
Name Stmts Miss Cover

---------- coverage: platform linux, python 3.8.10-final-0 -----------
Name Stmts Miss Cover

---------- coverage: platform linux, python 3.8.10-final-0 -----------
Name Stmts Miss Cover

---------- coverage: platform linux, python 3.8.10-final-0 -----------
Name Stmts Miss Cover

---------- coverage: platform linux, python 3.8.10-final-0 -----------
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---------- coverage: platform linux, python 3.8.10-final-0 -----------
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