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.gitlab-ci.yml
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.gitlab-ci.yml
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stages:
- pylint_test
- dev_test
- pip_test
- pip_publish
- release_notes
pylintjob:
stage: pylint_test
except:
- 139-correct-toynet-demo-instructions
- 148-publish-niftynet-v0-2-0-on-python-package-index-pypi
- 170-add-niftynet-paper-on-rtd-doc
- 167-document-cli-option-for-path-to-new-networks
- 176-document-pip-installer-bundling-guidelines
- 174-design-a-workflow-that-allows-prs-from-github-to-be-merged
- 206-improve-the-handling-of-release-notes
- 221-add-changelog-entry-for-version-0.2.2
- 223-put-bug-fixes-under-fixed-header-in-changelog
script:
- pylint --rcfile=tests/pylintrc niftynet/engine
- pylint --rcfile=tests/pylintrc niftynet/io
- pylint --rcfile=tests/pylintrc niftynet/utilities/user_parameters_*py
- pylint --rcfile=tests/pylintrc niftynet/utilities/download.py
- pylint --rcfile=tests/pylintrc niftynet/utilities/niftynet_global_config.py
tags:
- gift-little
testjob:
stage: dev_test
only:
- master
- dev
- tags
script:
# !!kill coverage in case of hanging processes
- if pgrep coverage; then pkill -f coverage; fi
- export TF_CPP_MIN_LOG_LEVEL=2
# print system info
- which nvidia-smi
- nvidia-smi
- pwd
- ls -la /dev | grep nvidia
# removing existing testing data
- if [ -d models ]; then rm -r models; fi
- if [ -d testing_data ]; then rm -r testing_data; fi
- if [ -d example_volumes ]; then rm -r example_volumes; fi
# download data
- wget -q https://www.dropbox.com/s/lioecnpv82r5n6e/example_volumes_v0_2.tar.gz
- tar -xzvf example_volumes_v0_2.tar.gz
- wget -q https://www.dropbox.com/s/2g8jr3wq8rw5xtv/testing_code_v0_3.tar.gz
- wget -q https://www.dropbox.com/s/5p5fdgy053tgmdj/testing_data_v0_3.tar.gz
- mkdir -p testing_data
- tar -xzvf testing_data_v0_3.tar.gz -C testing_data
- tar -xzvf testing_code_v0_3.tar.gz -C testing_data
- wget -N https://www.dropbox.com/s/gt0hm6o61rlsfcc/csv_data.tar.gz
- tar -C data -xzvf csv_data.tar.gz
######### Python 3 ###################### run python3 code with coverage wrapper
# save NiftyNet folder path just in case
- export niftynet_dir=$(pwd)
# create a virtual env to dev-test
- venv="niftynet-dev-test-py3"
- mypython=$(which python3)
- virtualenv -p $mypython $venv
- cd $venv
- venv_dir=$(pwd)
- source bin/activate
# print Python version to CI output
- which python
- python --version
- cd $niftynet_dir
- pip install coverage
- pip install --upgrade pip
- pip install -r requirements.txt
- echo $(python tests/get_gpu_index.py)
- export CUDA_VISIBLE_DEVICES=$(python tests/get_gpu_index.py)
- python net_download.py testing -r
- coverage run -a --source . net_segment.py train -c config/highres3dnet_config.ini --batch_size=1 --num_threads=2 --queue_length=40 --max_iter=10
- coverage run -a --source . net_segment.py inference -c config/highres3dnet_config.ini --batch_size 8 --spatial_window_size 64,64,64 --queue_length 64
- coverage run -a --source . net_segment.py train -c config/scalenet_config.ini --batch_size 1 --queue_length 5 --num_threads 2
- coverage run -a --source . net_segment.py inference -c config/scalenet_config.ini --batch_size 16 --spatial_window_size 64,64,64 --queue_length 32
- coverage run -a --source . net_segment.py train -c config/vnet_config.ini --batch_size 1 --queue_length 5 --num_threads 2 --activation_function relu
- coverage run -a --source . net_segment.py inference -c config/vnet_config.ini --batch_size 16 --spatial_window_size 64,64,64 --queue_length 32 --activation_function relu
# need a large GPU to run
- coverage run -a --source . net_segment.py train -c config/unet_config.ini --batch_size 1 --queue_length 5 --num_threads 2
- coverage run -a --source . net_segment.py inference -c config/unet_config.ini --batch_size 1 --spatial_window_size 96,96,96 --queue_length 5
#- coverage run -a --source . net_segment.py train -c config/deepmedic_config.ini --batch_size 128 --queue_length 48 --num_threads 4
#- coverage run -a --source . net_segment.py inference -c config/deepmedic_config.ini --batch_size 12 --spatial_window_size 135,135,135 --queue_length 128
- coverage run -a --source . net_segment.py train -c config/default_segmentation.ini --batch_size 3 --queue_length 6
- coverage run -a --source . net_segment.py train -c config/default_segmentation.ini --batch_size 3 --queue_length 6 --exclude_fraction_for_validation 0.1 --validation_every_n 3
- coverage run -a --source . net_segment.py train -c config/default_segmentation.ini --batch_size 3 --queue_length 6 --starting_iter 0 --max_iter 15
- coverage run -a --source . net_segment.py inference -c config/default_segmentation.ini --spatial_window_size 84,84,84 --batch_size 7 --queue_length 14 --inference_iter 15
- coverage run -a --source . net_segment.py evaluation -c config/default_segmentation.ini
- coverage run -a --source . net_segment.py train -c config/default_multimodal_segmentation.ini --batch_size 3
- coverage run -a --source . net_segment.py inference -c config/default_multimodal_segmentation.ini --spatial_window_size 64,64,64 --batch_size 7
- coverage run -a --source . net_classify.py train -c testing_data/test_classification.ini
- coverage run -a --source . net_classify.py inference -c testing_data/test_classification.ini
# - coverage run -a --source . net_classify.py evaluation -c testing_data/test_classification.ini
- coverage run -a --source . net_regress.py train -c config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- coverage run -a --source . net_run.py train -a net_regress -c config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- coverage run -a --source . net_regress.py inference -c config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- coverage run -a --source . net_regress.py evaluation -c config/default_monomodal_regression.ini
- coverage run -a --source . net_gan.py train -c config/GAN_demo_train_config.ini --max_iter 5
- coverage run -a --source . net_run.py train -a net_gan -c config/GAN_demo_train_config.ini --max_iter 5
- coverage run -a --source . net_gan.py inference -c config/GAN_demo_train_config.ini
- coverage run -a --source . net_autoencoder.py train -c config/vae_config.ini --max_iter 5
- coverage run -a --source . net_autoencoder.py inference -c config/vae_config.ini --inference_type sample
- coverage run -a --source . net_autoencoder.py inference -c config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- coverage run -a --source . net_autoencoder.py inference -c config/vae_config.ini --inference_type encode-decode
- coverage run -a --source . net_download.py dense_vnet_abdominal_ct_model_zoo -r
- coverage run -a --source . -m tests.test_model_zoo
- coverage run -a --source . -m unittest discover -s "tests" -p "*_test.py"
- coverage report --omit='niftynet-dev-test*' -m
# deactivate virtual environment
- deactivate
- cd $niftynet_dir
###############end of python3
- echo 'finished test'
tags:
- gift-little
quicktest:
stage: dev_test
except:
- master
- dev
- tags
- 147-revise-contribution-guidelines-to-include-github
- 150-properly-format-the-bibtex-entry-to-the-ipmi-2017-paper-on-the-main-readme
- 148-publish-niftynet-v0-2-0-on-python-package-index-pypi
- 170-add-niftynet-paper-on-rtd-doc
- 167-document-cli-option-for-path-to-new-networks
- 176-document-pip-installer-bundling-guidelines
- 174-design-a-workflow-that-allows-prs-from-github-to-be-merged
- 206-improve-the-handling-of-release-notes
- 221-add-changelog-entry-for-version-0.2.2
- 223-put-bug-fixes-under-fixed-header-in-changelog
- 195-add-evaluation-action-3
script:
# print system info
- which nvidia-smi
- nvidia-smi
- pwd
- ls -la /dev | grep nvidia
- echo $(python tests/get_gpu_index.py)
- export CUDA_VISIBLE_DEVICES=$(python tests/get_gpu_index.py)
# removing existing testing data
- if [ -d models ]; then rm -r models; fi
- if [ -d testing_data ]; then rm -r testing_data; fi
- if [ -d example_volumes ]; then rm -r example_volumes; fi
# download data
- wget -q https://www.dropbox.com/s/lioecnpv82r5n6e/example_volumes_v0_2.tar.gz
- tar -xzvf example_volumes_v0_2.tar.gz
- wget -q https://www.dropbox.com/s/5p5fdgy053tgmdj/testing_data_v0_3.tar.gz
- mkdir -p testing_data
- tar -xzvf testing_data_v0_3.tar.gz -C testing_data
- wget -q https://www.dropbox.com/s/gt0hm6o61rlsfcc/csv_data.tar.gz
- tar -C data -xzvf csv_data.tar.gz
######### Python 3 ###################### run python3 code with coverage wrapper
# save NiftyNet folder path just in case
- export niftynet_dir=$(pwd)
# create a virtual env to dev-test
- venv="niftynet-dev-test-py3"
- mypython=$(which python3)
- virtualenv -p $mypython $venv
- cd $venv
- venv_dir=$(pwd)
- source bin/activate
# print Python version to CI output
- which python
- python --version
- cd $niftynet_dir
- pip install coverage
- pip install --upgrade pip
- pip install -r requirements.txt
- python -c "import tensorflow as tf; print(tf.__version__)"
#- python -c "import tensorflow as tf; from tensorflow.python.client import device_lib; print device_lib.list_local_devices()"
- coverage erase
# run only fast tests
- echo $(python tests/get_gpu_index.py)
- export CUDA_VISIBLE_DEVICES=$(python tests/get_gpu_index.py)
- python net_download.py testing -r
- QUICKTEST=True coverage run -a --source . -m unittest discover -s "tests" -p "*_test.py"
- coverage report --omit='niftynet-dev-test*' -m
# deactivate virtual environment
- deactivate
- cd $niftynet_dir
###############end of python3
- echo 'finished quick tests'
tags:
- gift-little
pip-installer:
stage: pip_test
only:
- master
- dev
- tags
script:
# get the shortened version of last commit's hash
- LAST_COMMIT=$(git rev-parse --short HEAD)
# following three lines copied over from dev script:
- ls -la /dev | grep nvidia
- export niftynet_dir=$(pwd)
# ============= Python 3 ============================
# create a virtual env to test pip installer
- venv="niftynet-pip-installer-venv-py3"
- mypython=$(which python3)
- virtualenv -p $mypython $venv
- cd $venv
- venv_dir=$(pwd)
- source bin/activate
# print Python version to CI output
- which python
- python --version
# NiftyNet console entries should fail gracefully if TF not installed
# i.e. check that the warning displays the TF website
- cd $niftynet_dir
- echo $(python tests/get_gpu_index.py)
- export CUDA_VISIBLE_DEVICES=$(python tests/get_gpu_index.py)
# create a Python file that will import all available packages from the pip installer
- package_importer="$(pwd)/import_niftynet_packages.py"
# traverse the file hierarchy recursively to discover all packages
- find niftynet -type f \( ! -name . \) ! -path "niftynet/contrib/niftyreg_image_resampling*" -print | grep '.py$' | grep -v __init__ | sed 's/\.\.\///g;s/\//\./g;s/\.py//g;s/^niftynet/import niftynet/g' > $package_importer
- set +e
- python -c "import niftynet" 2>&1 | grep "https://www.tensorflow.org/"
- set -e
# install TF
- pip install --upgrade pip
- pip install "tensorflow==1.15.*"
- pip install -r requirements.txt
# source utils
- source ci/utils.sh
# create the NiftyNet wheel
- rm -rf dist # remove dist directory, just in case
- sh ci/bundlewheel.sh
- source ci/findwheel.sh
- echo $niftynet_wheel
- pip install $niftynet_wheel
# install SimpleITK for package importer test to work properly
- pip install simpleitk
# check whether all packages are importable
- cat $package_importer
- python $package_importer
# test niftynet command
- ln -s /home/gitlab-runner/environments/niftynet/data/example_volumes ./example_volumes
- net_download testing -r
- net_segment train -c $niftynet_dir/config/default_segmentation.ini --name toynet --batch_size 3 --max_iter 5
- net_segment inference -c $niftynet_dir/config/default_segmentation.ini --name toynet --spatial_window_size 80,80,80 --batch_size 8
- net_segment evaluation -c $niftynet_dir/config/default_segmentation.ini
- net_run train --app net_segment -c $niftynet_dir/config/default_segmentation.ini --name toynet --batch_size 3 --max_iter 5
- net_run inference --app net_segment -c $niftynet_dir/config/default_segmentation.ini --name toynet --spatial_window_size 80,80,80 --batch_size 8
- net_run evaluation --app net_segment -c $niftynet_dir/config/default_segmentation.ini
- net_classify train -c extensions/testing/test_classification.ini
- net_classify inference -c extensions/testing/test_classification.ini
#- net_classify evaluation -c extensions/testing/test_classification.ini
- net_run --app net_classify train -c extensions/testing/test_classification.ini
- net_run --app net_classify inference -c extensions/testing/test_classification.ini
#- net_run --app net_classify evaluation -c extensions/testing/test_classification.ini
- net_regress train -c $niftynet_dir/config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- net_regress inference -c $niftynet_dir/config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- net_regress evaluation -c $niftynet_dir/config/default_monomodal_regression.ini
- net_run train -a net_regress -c $niftynet_dir/config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- net_run inference -a net_regress -c $niftynet_dir/config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- net_run evaluation -a net_regress -c $niftynet_dir/config/default_monomodal_regression.ini
- net_gan train -c $niftynet_dir/config/GAN_demo_train_config.ini --max_iter 5
- net_gan inference -c $niftynet_dir/config/GAN_demo_train_config.ini
- net_run train --app net_gan -c $niftynet_dir/config/GAN_demo_train_config.ini --max_iter 5
- net_run inference --app net_gan -c $niftynet_dir/config/GAN_demo_train_config.ini
- net_autoencoder train -c $niftynet_dir/config/vae_config.ini --max_iter 5
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type sample
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type encode-decode
- net_run train --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --max_iter 5
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type sample
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type encode-decode
- net_download dense_vnet_abdominal_ct_model_zoo -r
# deactivate virtual environment
- deactivate
- cd $niftynet_dir
tags:
- gift-little
pip-camera-ready:
stage: pip_publish
only:
- tags
script:
- export check_version_venv=check_version_venv
- virtualenv -p $(which python3) $check_version_venv
- source $check_version_venv/bin/activate
- pip install "tensorflow==1.15.*"
- pip install -r requirements.txt
# Copy wheel created in previous stage to a specific location on GIFT-Adelie
- export niftynet_dir=$(pwd)
# create the NiftyNet wheel
- rm -rf dist # remove dist directory, just in case
- source ci/utils.sh
- sh ci/bundlewheel.sh
- source ci/findwheel.sh
- echo $niftynet_wheel
# Creat camera-ready folder if doesn't exist
- camera_ready_dir=/home/gitlab-runner/environments/niftynet/pip/camera-ready
- mkdir -p $camera_ready_dir
- ls -lrtha $camera_ready_dir
# Clean up the camera-ready folder if already there
- rm -rf $camera_ready_dir/*.whl
# check whether version command works
- pip3 install "tensorflow==1.15.*"
- pip3 install -r requirements.txt
- pip3 install $niftynet_wheel
- net_segment --version
- net_segment --version 2>&1 | grep -E 'NiftyNet.*version.*[0-9]+\.[0-9]+\.[0-9]+' | grep -v +
- python -c 'import niftynet; print(niftynet.__version__)' | grep -E 'NiftyNet.*version.*[0-9]+\.[0-9]+\.[0-9]+' | grep -v +
- deactivate
- rm -rf $check_version_venv
# Finally do copy
- cp $niftynet_wheel $camera_ready_dir
- ls -lrtha $camera_ready_dir
# Instruct developer which file to publish
- echo "Camera-ready pip installer bundle (wheel) created:"
- echo "$(ls $camera_ready_dir/*.whl)"
tags:
- gift-little
release-notes:
stage: release_notes
only:
- tags
script:
- echo $CI_COMMIT_REF_NAME
- echo $CI_COMMIT_TAG
# Fail if nothing's been added to changelog for this version
- echo "Checking changelog modified for current release ..."
- grep ${CI_COMMIT_TAG:1} CHANGELOG.md
- echo "... DONE"
# Do actually push release notes to GitHub
- |
if [[ "$CI_COMMIT_TAG" == v* ]]; then
echo "Doing a chandler push for $CI_COMMIT_TAG ..."
chandler push $CI_COMMIT_TAG --github=NifTK/NiftyNet
echo "... DONE"
fi
tags:
- gift-little