Add documentation for installing additional packages #29
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Clarifies that ray[default] doesn't include deep learning libraries and shows how to install PyTorch/TensorFlow for GPU workloads.
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Pull request overview
This PR adds documentation to help users install deep learning libraries (PyTorch and TensorFlow) in their Ray virtual environment, since the default ray[default] installation doesn't include them.
Changes:
- Added a new "Installing Additional Packages" section explaining how to install PyTorch or TensorFlow
- Clarified that
examples/gpu_workload.pyworks without PyTorch but displays "PyTorch not available" messages unless PyTorch is installed
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| # Or install TensorFlow | ||
| uv pip install --python ~/pw/activate-ray/ray_venv/bin/python tensorflow |
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The comment says "Install PyTorch with CUDA support" but the command pip install torch will install the CPU-only version by default. To install PyTorch with CUDA support, you need to specify the appropriate PyTorch index URL or follow PyTorch's installation instructions for CUDA-enabled versions. Consider either:
- Updating the command to include CUDA support (e.g., using PyTorch's recommended installation command with the correct index URL)
- Changing the comment to clarify that this installs the default version and users should consult PyTorch documentation for CUDA-specific installations
- Removing the "with CUDA support" phrase from the comment if the intention is just to install the default version
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Summary
ray[default]doesn't include deep learning librariesgpu_workload.pyshows "PyTorch not available" unless installedTest plan