gemini-code-assist[bot] commented on PR #36677:
URL: https://github.com/apache/beam/pull/36677#issuecomment-3467075172
## Summary of Changes
Hello @Amar3tto, I'm Gemini Code Assist[^1]! I'm currently reviewing this
pull request and will post my feedback shortly. In the meantime, here's a
summary to help you and other reviewers quickly get up to speed!
This pull request is a cherry-pick to the post-release branch for version
2.69.0, primarily aimed at refining the Python SDK container's machine learning
environment. It introduces a dedicated 'ml_cpu' dependency configuration to
ensure that PyTorch and other ML libraries are installed using CPU-specific
wheels. This change addresses potential licensing conflicts by preventing the
inclusion of GPU-dependent packages and standardizes the ML dependency
management across various Python versions within the Beam ecosystem.
### Highlights
* **Introduction of ml_cpu Extra**: A new 'ml_cpu' dependency group has been
added to 'setup.py' to specifically manage CPU-only machine learning
dependencies, providing a clear separation from GPU-enabled configurations.
* **CPU-only PyTorch Installation Enforcement**: Build scripts
('Dockerfile', 'run_generate_requirements.sh') are updated to explicitly pull
PyTorch dependencies from the PyTorch CPU wheel repository, preventing the
inclusion of GPU-specific dependencies that might have licensing restrictions.
* **Updated ML Requirements Files**: New 'ml_image_requirements.txt' files
are generated for Python versions 3.9, 3.10, 3.11, and 3.12, and the existing
one for 3.13 is updated. All these files now reflect the 'ml_cpu' dependencies
and specifically list 'torch==2.8.0+cpu'.
* **Dependency Version Bumps**: Several ML-related package versions have
been updated across the requirements files, including 'cryptography', 'grpcio',
'hypothesis', 'idna', 'referencing', 'regex', 'wrapt', and 'yarl', ensuring
compatibility and incorporating the latest stable versions.
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