[sdk/python] Rename flamepy.rl module to flamepy.runner#341
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Summary of ChangesHello @k82cn, I'm Gemini Code Assist1! 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 streamlines the Highlights
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Code Review
This pull request is a comprehensive refactoring that renames the flamepy.rl module to flamepy.run for a more intuitive API. The changes span across the Python SDK, Rust code, tests, examples, and documentation, ensuring consistency throughout the project. The renaming is well-executed, and all references appear to be updated correctly. I've found one minor typo in the documentation that could be corrected for clarity.
| # Parameter Server Example | ||
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| In AI-related distributed workloads, large amounts of data often need to be transferred between multiple worker nodes. Flame provides an object cache mechanism to facilitate the transfer of data objects between nodes. This example demonstrates the use of `Flame.rl.Runner` in a training scenario through a simple case based on the `Parameter Server` (ps-worker) pattern. | ||
| In AI-related distributed workloads, large amounts of data often need to be transferred between multiple worker nodes. Flame provides an object cache mechanism to facilitate the transfer of data objects between nodes. This example demonstrates the use of `Flame.run.Runner` in a training scenario through a simple case based on the `Parameter Server` (ps-worker) pattern. |
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There's a small typo in the documentation. Flame.run.Runner should likely be flamepy.run.Runner to match the Python module path and for consistency with other parts of the documentation.
| In AI-related distributed workloads, large amounts of data often need to be transferred between multiple worker nodes. Flame provides an object cache mechanism to facilitate the transfer of data objects between nodes. This example demonstrates the use of `Flame.run.Runner` in a training scenario through a simple case based on the `Parameter Server` (ps-worker) pattern. | |
| In AI-related distributed workloads, large amounts of data often need to be transferred between multiple worker nodes. Flame provides an object cache mechanism to facilitate the transfer of data objects between nodes. This example demonstrates the use of `flamepy.run.Runner` in a training scenario through a simple case based on the `Parameter Server` (ps-worker) pattern. |
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Rename the Runner module from flamepy.rl to flamepy.runner for a cleaner and more intuitive API. This avoids conflict with the existing flamepy.run() function used for running services. This change updates all references across: - SDK/Python source files - Rust references in common and session_manager - E2E tests - Examples (ps, pi) - Documentation (tutorials, blogs, design docs)
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Summary
flamepy.rlmodule toflamepy.runnerfor a cleaner and more intuitive APIChanges
SDK/Python
sdk/python/src/flamepy/rl/→sdk/python/src/flamepy/runner/runpy.py,runner.py,__main__.pyflamepy/__init__.pyto exportrunnerinstead ofrlRust
common/src/lib.rs- changedflamepy.rl.runpytoflamepy.runner.runpysession_manager/src/storage/engine/sqlite.rsTests
e2e/src/e2e/helpers.pye2e/tests/test_flmrun.pye2e/tests/test_runner.pyExamples
examples/ps/main.pyandREADME.mdexamples/pi/python/main.pyDocumentation
docs/tutorials/runner-setup.mddocs/blogs/run-pi-with-runner.mddocs/designs/FRE280-runner/anddocs/designs/RFE284-flmrun/Usage
Testing
All existing tests should pass after updating imports.