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Add configurable GPU sharing for pipeline workers - #72
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 7 remain after this review. 📝 WalkthroughWalkthroughThe pipeline adds local process and thread executors, supports multiple Dragon workers per GPU, and validates worker GPU assignments. Process workers can optionally require an MPS client connection. CLI options, documentation, and tests cover these changes. ChangesGPU Worker Sharing
Priority: ➖ Normal Merge Risk: ⚪ Minimal · up to Closing GPU workers after a failed run no longer depends on every worker pipe being healthy. A close failure on one exited worker does not stop the others from receiving the close message. No concrete merge-blocking risk remains in the reviewed changes. Pre-merge checks |
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Review comments at @src/cuphoton/xscan/sharing.py:
- Around line 569-570: Update the shutdown loop over parents so each
connection’s close send handles pipe failures independently; record the failed
worker’s error using the existing error-reporting path, then continue sending
close to the remaining workers in order.
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docs/components/xscan.mddocs/dragon.mdsrc/cuphoton/core/cli/executor.pysrc/cuphoton/core/dragon.pysrc/cuphoton/core/execution.pysrc/cuphoton/core/mps.pysrc/cuphoton/xscan/commands.pysrc/cuphoton/xscan/pipeline_executor.pysrc/cuphoton/xscan/sharing.pytests/core/test_cli_contract.pytests/core/test_dragon.pytests/core/test_execution.pytests/core/test_executor_options.pytests/core/test_mps.pytests/xscan/_sharing_support.pytests/xscan/test_pipeline_executor.pytests/xscan/test_sharing.py
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@coderabbitai review Please review the updated head, 49530f1, including the worker failure and cleanup fixes. |
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melo-gonzo
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LGTM, with a nonblocking pool-ownership test suggestion inline. Focused CPU lifecycle checks found no introduced runtime defect; actual GPU/MPS execution was not rerun, and the cancelled required GPU job needs a successful rerun before merge.
Allow independent image pairs to share a GPU through Dragon processes or local process and thread workers. Expose worker counts, MPS service selection and free-threading checks through the CLI and Python API. Preserve explicit placement, retained outputs and worker cleanup across repeated rounds. Signed-off-by: Trent Nelson <trentn@nvidia.com>
Keep successful first-item results when another thread breaks the barrier. Close each started worker independently, drain pending receipts, and allow bounded graceful shutdown after failures. Cover isolated worker and partial-start failures, and clarify the sharing options in the CLI and component documentation. Signed-off-by: Trent Nelson <trentn@nvidia.com>
Signed-off-by: Trent Nelson <trentn@nvidia.com>
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Image-pair workloads can leave GPU capacity unused while the executor permits only one worker per device. This adds configurable sharing for independent pairs, with one worker per GPU remaining the default.
--workers-per-gpuconfigures the shared Dragon executor and localxscan run-pipelineworkers.--executor processesor--executor threadsselects local execution; the same options reach the Python API.--mps-pipe-directoryselects an existing MPS v2 service and requires verified client connections. Dragon binds by GPU UUID to avoid ordinal remapping. Service lifetime and GPU policy remain externally managed.--require-free-threadedchecks that the GIL remains disabled. Thread mode accepts prepared NPY inputs; native-extension compatibility still needs qualification for the chosen environment.Workers persist across rounds, retain ordinary outputs, and enforce placement, deadlines and cleanup. CLI examples and Python usage are documented.
Validation: 2,830 CPU tests passed; public hooks, typing, lint and source build passed. Actual RTX PRO 6000 CLI runs covered one/two processes, threads with the GIL on/off, MPS, and local Dragon with/without MPS. All 96 retained image-pair outputs passed independent numerical comparison. Missing MPS and enabled-GIL requirements failed as expected, with clean GPU shutdown. These small runs validate behavior; the prior experimental performance results are not asserted for this implementation. Multi-node validation of this implementation remains outstanding.
Independent of the solver optimization in #71.