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ma-pinedaTyler Titsworthpre-commit-ci[bot]
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Fix tpp pins and add labels (#167)
Signed-off-by: ma-pineda <miguel.pineda.juarez@intel.com> Signed-off-by: Tyler Titsworth <tyler.titsworth@intel.com> Co-authored-by: Tyler Titsworth <tyler.titsworth@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎.github/dependabot.yml

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

‎docs/matrix.md

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

‎docs/scripts/hook.py

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

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{
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"PYTHON_VERSION": ["3.9", "3.10"],
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"experimental": [true],
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"RELEASE": ["2024.1.0"],
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"runner_label": ["PVC"]
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}

‎preset/classical-ml/Dockerfile

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

‎preset/classical-ml/docker-compose.yaml

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

‎preset/classical-ml/requirements.txt

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cloud-data-connector==1.0.3

‎preset/data-analytics/.actions.json

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{
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"PYTHON_VERSION": ["3.9", "3.10"],
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"experimental": [true],
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"RELEASE": ["2024.1.0"],
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"runner_label": ["PVC"]
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}

‎preset/data-analytics/Dockerfile

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conda config --add channels intel && \
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conda init --all && \
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conda install -y \
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jupyterlab \
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notebook \
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jupyterhub \
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jupyter-server-proxy \
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'jupyterlab>=4.1.8' \
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'notebook>=7.1.3' \
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'jupyterhub>=4.1.5' \
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'jupyter-server-proxy>=4.1.2' \
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'mako>=1.2.2' \
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'pyjwt>=2.4.0' \
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'cryptography>=42.0.5' \
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'nodejs>=20.12.2' \
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'idna>=3.7' \
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'tqdm>=4.66.2' \
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'nodejs>=20.12.2' \
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&& \
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jupyter labextension disable "@jupyterlab/apputils-extension:announcements" && \
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conda clean -y --all
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numpy="${NUMPY_VERSION}" \
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python="${PYTHON_VERSION}" \
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modin-ray="${MODIN_VERSION}" \
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python-dotenv \
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tqdm \
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matplotlib-base \
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dataset_librarian \
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threadpoolctl \
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ipython \
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ipykernel \
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kernda \
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protobuf=4.24 \
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'python-dotenv>=1.0.1' \
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'tqdm>=4.66.2' \
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'matplotlib-base>=3.4.3' \
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'dataset_librarian>=1.0.4' \
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'threadpoolctl>=3.3.0' \
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'ipython>=8.18.1' \
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'ipykernel>=6.29.3' \
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'kernda>=0.3.0' \
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'protobuf>=4.24.4' \
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'pillow>=10.2.0' \
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'idna>=3.7' \
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'tornado>=6.3.3' && \
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conda clean -y --all
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RUN conda run -n data-analytics python -m pip install --no-deps --no-cache-dir \
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cloud-data-connector
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'cloud-data-connector==1.0.3'
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FROM data-analytics-python as data-analytics-jupyter
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‎preset/data-analytics/docker-compose.yaml

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

‎preset/data-analytics/requirements.txt

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cloud-data-connector==1.0.3

‎preset/deep-learning/.actions.json

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{
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"PYTHON_VERSION": ["3.9", "3.10"],
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"experimental": [true],
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"RELEASE": ["2024.1.0"],
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"runner_label": ["PVC"]
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}

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