7880ea8d79
Organize podman/ directory into logical subdirectories: New structure: - docs/ - ML_TOOLS_GUIDE.md, jupyter_workflow.md - configs/ - environment*.yml, security_policy.json - containers/ - *.dockerfile, *.podfile - scripts/ - *.sh, *.py (secure_runner, cli_integration, etc.) - jupyter/ - jupyter_cookie_secret (flattened from jupyter_runtime/runtime/) - workspace/ - Example projects (cleaned of temp files) Cleaned workspace: - Removed .DS_Store, mlflow.db, cache/ - Removed duplicate cli_integration.py Removed unnecessary nesting: - Flattened jupyter_runtime/runtime/ to just jupyter/ Improves maintainability by grouping files by purpose and eliminating root directory clutter.
59 lines
1.6 KiB
Text
59 lines
1.6 KiB
Text
# Fast Secure ML Runner
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# Optimized for data scientists with maximum speed
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FROM continuumio/miniconda3:latest
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# Install mamba for lightning-fast package resolution
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RUN conda install -n base -c conda-forge mamba -y && \
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conda clean -afy
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# Security: Create non-root user
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RUN groupadd -r mlrunner && useradd -r -g mlrunner mlrunner
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# Create secure workspace
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WORKDIR /workspace
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RUN chown mlrunner:mlrunner /workspace
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# Create conda environment with mamba (much faster than pip)
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RUN mamba create -n ml_env python=3.10 -y && \
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chown -R mlrunner:mlrunner /opt/conda/envs/ml_env
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# Pre-install ML packages with mamba (super fast!)
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RUN mamba install -n ml_env \
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pytorch>=1.9.0 \
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torchvision>=0.10.0 \
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numpy>=1.21.0 \
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pandas>=1.3.0 \
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scikit-learn>=1.0.0 \
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xgboost>=1.5.0 \
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matplotlib>=3.5.0 \
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seaborn>=0.11.0 \
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jupyter>=1.0.0 \
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-c pytorch -c conda-forge -y && \
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conda clean -afy
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# Poetry (for pyproject.toml + poetry.lock projects)
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RUN mamba install -n ml_env poetry -c conda-forge -y && \
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conda clean -afy
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# Copy security wrapper
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COPY secure_runner.py /usr/local/bin/secure_runner.py
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COPY security_policy.json /etc/ml_runner/security_policy.json
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# Set permissions
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RUN chmod +x /usr/local/bin/secure_runner.py && \
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chown mlrunner:mlrunner /usr/local/bin/secure_runner.py
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# Switch to non-root user
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USER mlrunner
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# Set conda environment
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SHELL ["/bin/bash", "-c"]
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ENTRYPOINT ["conda", "run", "-n", "ml_env", "python", "/usr/local/bin/secure_runner.py"]
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# Labels
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LABEL package_manager="mamba" \
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speed="optimized" \
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ml_frameworks="pytorch,sklearn,xgboost" \
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security="enabled"
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