Fix tpp pins and add labels

.github/dependabot.yml

@@ -79,3 +79,11 @@ updates:
     package-ecosystem: pip
     schedule:
       interval: weekly
+  - directory: /preset
+    groups:
+      preset:
+        patterns:
+          - "*requirements.txt"
+    package-ecosystem: pip
+    schedule:
+      interval: weekly

docs/matrix.md

@@ -23,13 +23,13 @@
             {{ read_csv('assets/max-pvc.csv') }}
 
 === "[Preset Containers](https://github.com/intel/ai-containers/blob/main/preset/README.md)"
-    === "Data Analytics"
+    === "[Data Analytics](https://hub.docker.com/r/intel/data-analytics)"
         {{ read_csv('assets/data_analytics.csv') }}
-    === "Classical ML"
+    === "[Classical ML](https://hub.docker.com/r/intel/classical-ml)"
         {{ read_csv('assets/classical_ml.csv') }}
-    === "Deep Learning"
+    === "[Deep Learning](https://hub.docker.com/r/intel/deep-learning)"
         {{ read_csv('assets/deep_learning.csv') }}
-    === "Inference Optimization"
+    === "[Inference Optimization](https://hub.docker.com/r/intel/inference-optimization)"
         {{ read_csv('assets/inference_optimization.csv') }}
 
 === "[Workflows](https://hub.docker.com/r/intel/ai-workflows)"

docs/scripts/hook.py

@@ -36,10 +36,10 @@ def create_support_matrix():
     compose_to_csv("classical-ml", None)
 
     # get_repo(models)
-    # compose_to_csv("preset/data-analytics", "data_analytics")
-    # compose_to_csv("preset/classical-ml", "classical_ml")
-    # compose_to_csv("preset/deep-learning", "deep_learning")
-    # compose_to_csv("preset/inference-optimization", "inference_optimization")
+    compose_to_csv("preset/data-analytics", "data_analytics")
+    compose_to_csv("preset/classical-ml", "classical_ml")
+    compose_to_csv("preset/deep-learning", "deep_learning")
+    compose_to_csv("preset/inference-optimization", "inference_optimization")
     compose_to_csv("workflows/charts/huggingface-llm", "genai")
 
 

preset/classical-ml/.actions.json

@@ -1,6 +1,5 @@
 {
     "PYTHON_VERSION": ["3.9", "3.10"],
     "experimental": [true],
-    "RELEASE": ["2024.1.0"],
     "runner_label": ["PVC"]
 }

preset/classical-ml/Dockerfile

@@ -67,10 +67,10 @@ RUN wget --progress=dot:giga --no-check-certificate https://github.com/conda-for
     conda config --add channels intel && \
     conda init --all && \
     conda install -y \
-        jupyterlab \
-        notebook \
-        jupyterhub \
-        jupyter-server-proxy \
+        'jupyterlab>=4.1.8' \
+        'notebook>=7.1.3' \
+        'jupyterhub>=4.1.5' \
+        'jupyter-server-proxy>=4.1.2' \
         'mako>=1.2.2' \
         'pyjwt>=2.4.0' \
         'cryptography>=42.0.5' \
@@ -99,15 +99,15 @@ RUN conda create -yn classical-ml -c ${INTEL_CHANNEL} -c conda-forge \
         scikit-learn-intelex==${SKLEARNEX_VERSION} \
         xgboost=${XGBOOST_VERSION} \
         modin-ray=${MODIN_VERSION} \
-        python-dotenv \
-        tqdm \
-        matplotlib-base \
-        dataset_librarian \
-        threadpoolctl \
-        ipython \
-        ipykernel \
-        kernda \
-        protobuf=4.24 \
+        'python-dotenv>=1.0.1' \
+        'tqdm>=4.66.2' \
+        'matplotlib-base>=3.4.3' \
+        'dataset_librarian>=1.0.4' \
+        'threadpoolctl>=3.3.0' \
+        'ipython>=8.18.1' \
+        'ipykernel>=6.29.3' \
+        'kernda>=0.3.0' \
+        'protobuf>=4.24' \
         'pillow>=10.2.0' \
         'tornado>=6.3.3' && \
     conda clean -y --all
@@ -116,7 +116,7 @@ RUN conda create -yn classical-ml -c ${INTEL_CHANNEL} -c conda-forge \
 
 # PyPI packages
 RUN conda run -n classical-ml python -m pip install --no-deps --no-cache-dir \
-    cloud-data-connector
+    'cloud-data-connector==1.0.3'
 
 
 ENV PYTHONSTARTUP=~/.patch_sklearn.py

preset/classical-ml/docker-compose.yaml

@@ -37,11 +37,59 @@ services:
         https_proxy: ${https_proxy}
         no_proxy: ''
       context: .
+      labels:
+        docs: classical_ml
+        org.opencontainers.image.title: "Intel® AI Tools Selector Preset Containers - Classical ML"
+        org.opencontainers.base.name: "ubuntu:22.04"
+        org.opencontainers.image.name: "intel/classical-ml"
+        org.opencontainers.image.version: 2024.1.0-py${PYTHON_VERSION:-3.10}
+        dependency.python: ${PYTHON_VERSION:-3.10}
+        dependency.python.pip: requirements.txt
+        dependency.apt.bzip2: true
+        dependency.apt.ca-certificates: true
+        dependency.apt.diffutils: true
+        dependency.apt.gcc: true
+        dependency.apt.git: true
+        dependency.apt.gzip: true
+        dependency.apt.make: true
+        dependency.apt.patch: true
+        dependency.apt.rsync: true
+        dependency.apt.unzip: true
+        dependency.apt.wget: true
+        dependency.apt.xz-utils: true
+        dependency.conda.jupyterlab: '>=4.1.8'
+        dependency.conda.notebook: '>=7.1.3'
+        dependency.conda.jupyterhub: '>=4.1.5'
+        dependency.conda.jupyter-server-proxy: '>=4.1.2'
+        dependency.conda.mako: '>=1.2.2'
+        dependency.conda.pyjwt: '>=2.4.0'
+        dependency.conda.cryptography: '>=42.0.5'
+        dependency.conda.nodejs: '>=20.12.2'
+        dependency.conda.aiohttp: '>=3.9.4'
+        dependency.conda.idna: '>=3.7'
+        dependency.conda.oauthlib: '>=3.2.2'
+        dependency.conda.dpnp: '>=0.14.0'
+        dependency.conda.numpy: '>=1.26.4'
+        dependency.conda.python: "=${PYTHON_VERSION:-3.10}"
+        dependency.conda.scikit-learn-intelex: '>=2024.2.0'
+        dependency.conda.xgboost: '>=2.0.3'
+        dependency.conda.modin-ray: '>=0.26.1'
+        dependency.conda.python-dotenv: '>=1.0.1'
+        dependency.conda.tqdm: '>=4.66.2'
+        dependency.conda.matplotlib-base: '>=3.4.3'
+        dependency.conda.dataset_librarian: '>=1.0.4'
+        dependency.conda.threadpoolctl: '>=3.3.0'
+        dependency.conda.ipython: '>=8.18.1'
+        dependency.conda.ipykernel: '>=6.29.3'
+        dependency.conda.kernda: '>=0.3.0'
+        dependency.conda.protobuf: '>=4.24'
+        dependency.conda.pillow: '>=10.2.0'
+        dependency.conda.tornado: '>=6.3.3'
       target: classical-ml-jupyter
     command: |
       bash -c "conda run -n classical-ml python -c 'import sklearn; import xgboost; print(\"SciKit:\", sklearn.__version__, \" XGBoost:\",xgboost.__version__)' && \
       conda run -n classical-ml python -c 'import modin.pandas as pd, modin.config as cfg; cfg.Engine.put(\"Ray\"); df = pd.DataFrame([1]);print(df+1)'"
-    image: ${REGISTRY}/${REPO}:b-${GITHUB_RUN_NUMBER:-0}-classical-ml-${IDP_VERSION:-2024.1.0}-py${PYTHON_VERSION:-3.10}
+    image: ${REGISTRY}/${REPO}:b-${GITHUB_RUN_NUMBER:-0}-classical-ml-2024.1.0-py${PYTHON_VERSION:-3.10}
     environment:
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}

preset/classical-ml/requirements.txt

@@ -0,0 +1 @@
+cloud-data-connector==1.0.3

preset/data-analytics/.actions.json

@@ -1,6 +1,5 @@
 {
     "PYTHON_VERSION": ["3.9", "3.10"],
     "experimental": [true],
-    "RELEASE": ["2024.1.0"],
     "runner_label": ["PVC"]
 }

preset/data-analytics/Dockerfile

@@ -68,16 +68,16 @@ RUN wget --progress=dot:giga --no-check-certificate "https://github.com/conda-fo
     conda config --add channels intel && \
     conda init --all && \
     conda install -y \
-        jupyterlab \
-        notebook \
-        jupyterhub \
-        jupyter-server-proxy \
+        'jupyterlab>=4.1.8' \
+        'notebook>=7.1.3' \
+        'jupyterhub>=4.1.5' \
+        'jupyter-server-proxy>=4.1.2' \
         'mako>=1.2.2' \
         'pyjwt>=2.4.0' \
         'cryptography>=42.0.5' \
+        'nodejs>=20.12.2' \
         'idna>=3.7' \
         'tqdm>=4.66.2' \
-        'nodejs>=20.12.2' \
         && \
     jupyter labextension disable "@jupyterlab/apputils-extension:announcements" && \
     conda clean -y --all
@@ -95,22 +95,22 @@ RUN conda create -yn data-analytics -c "${INTEL_CHANNEL}" -c conda-forge \
         numpy="${NUMPY_VERSION}" \
         python="${PYTHON_VERSION}" \
         modin-ray="${MODIN_VERSION}" \
-        python-dotenv \
-        tqdm \
-        matplotlib-base \
-        dataset_librarian \
-        threadpoolctl \
-        ipython \
-        ipykernel \
-        kernda  \
-        protobuf=4.24 \
+        'python-dotenv>=1.0.1' \
+        'tqdm>=4.66.2' \
+        'matplotlib-base>=3.4.3' \
+        'dataset_librarian>=1.0.4' \
+        'threadpoolctl>=3.3.0' \
+        'ipython>=8.18.1' \
+        'ipykernel>=6.29.3' \
+        'kernda>=0.3.0'  \
+        'protobuf>=4.24.4' \
         'pillow>=10.2.0' \
         'idna>=3.7' \
         'tornado>=6.3.3' && \
     conda clean -y --all
 
 RUN conda run -n data-analytics python -m pip install --no-deps --no-cache-dir \
-    cloud-data-connector
+    'cloud-data-connector==1.0.3'
 
 FROM data-analytics-python as data-analytics-jupyter
 

preset/data-analytics/docker-compose.yaml

@@ -17,7 +17,7 @@
 
 version: '3'
 services:
-  classical-ml:
+  data-analytics:
     build:
       args:
         BASE_IMAGE: ${BASE_IMAGE:-ubuntu}
@@ -35,10 +35,54 @@ services:
         https_proxy: ${https_proxy}
         no_proxy: ''
       context: .
+      labels:
+        docs: data_analytics
+        org.opencontainers.image.title: "Intel® AI Tools Selector Preset Containers - Data Analytics"
+        org.opencontainers.base.name: "ubuntu:22.04"
+        org.opencontainers.image.name: "intel/data-analytics"
+        org.opencontainers.image.version: 2024.1.0-py${PYTHON_VERSION:-3.10}
+        dependency.python: ${PYTHON_VERSION:-3.10}
+        dependency.python.pip: requirements.txt
+        dependency.apt.bzip2: true
+        dependency.apt.ca-certificates: true
+        dependency.apt.diffutils: true
+        dependency.apt.gcc: true
+        dependency.apt.git: true
+        dependency.apt.gzip: true
+        dependency.apt.make: true
+        dependency.apt.patch: true
+        dependency.apt.rsync: true
+        dependency.apt.unzip: true
+        dependency.apt.wget: true
+        dependency.apt.xz-utils: true
+        dependency.conda.jupyterlab: '>=4.1.8'
+        dependency.conda.notebook: '>=7.1.3'
+        dependency.conda.jupyterhub: '>=4.1.5'
+        dependency.conda.jupyter-server-proxy: '>=4.1.2'
+        dependency.conda.mako: '>=1.2.2'
+        dependency.conda.pyjwt: '>=2.4.0'
+        dependency.conda.cryptography: '>=42.0.5'
+        dependency.conda.nodejs: '>=20.12.2'
+        dependency.conda.idna: '>=3.7'
+        dependency.conda.tqdm: '>=4.66.2'
+        dependency.conda.dpnp: '>=0.14.0'
+        dependency.conda.numpy: '>=1.26.4'
+        dependency.conda.python: "=${PYTHON_VERSION:-3.10}"
+        dependency.conda.modin-ray: '>=0.26.1'
+        dependency.conda.python-dotenv: '>=1.0.1'
+        dependency.conda.matplotlib-base: '>=3.4.3'
+        dependency.conda.dataset_librarian: '>=1.0.4'
+        dependency.conda.threadpoolctl: '>=3.3.0'
+        dependency.conda.ipython: '>=8.18.1'
+        dependency.conda.ipykernel: '>=6.29.3'
+        dependency.conda.kernda: '>=0.3.0'
+        dependency.conda.protobuf: '>=4.24.4'
+        dependency.conda.pillow: '>=10.2.0'
+        dependency.conda.tornado: '>=6.3.3'
       target: data-analytics-jupyter
     command: >
         bash -c "conda run -n data-analytics python -c 'import modin.pandas as pd, modin.config as cfg; cfg.Engine.put(\"Ray\"); df = pd.DataFrame([1]);print(df+1)'"
-    image: ${REGISTRY}/${REPO}:b-${GITHUB_RUN_NUMBER:-0}-data-analytics-${IDP_VERSION:-2024.1.0}-py${PYTHON_VERSION:-3.10}
+    image: ${REGISTRY}/${REPO}:b-${GITHUB_RUN_NUMBER:-0}-data-analytics-2024.1.0-py${PYTHON_VERSION:-3.10}
     environment:
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}

preset/data-analytics/requirements.txt

@@ -0,0 +1 @@
+cloud-data-connector==1.0.3

preset/deep-learning/.actions.json

@@ -1,6 +1,5 @@
 {
     "PYTHON_VERSION": ["3.9", "3.10"],
     "experimental": [true],
-    "RELEASE": ["2024.1.0"],
     "runner_label": ["PVC"]
 }