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digaobarbosa
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digaobarbosa
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⚡️ Codeflash found optimizations for this PR📄 16% (0.16x) speedup for
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![codeflash-ai[bot]](https://avatars.githubusercontent.com/in/413177?s=60&v=4){kind=small}
...e/core/workflows/execution_engine/v1/executor/execution_data_manager/step_input_assembler.py
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The optimization replaced `all(k not in prediction for k in ["top", "predicted_classes"])` with a direct boolean expression `("top" not in prediction and "predicted_classes" not in prediction)` in `is_prediction_registration_forbidden`, eliminating generator overhead and reducing that function's profiled time from 70.7% to 24% of its own cost. This hot helper is called on every datapoint registration (1125 calls in the profiler), so removing the generator iteration yields a cumulative ~4.7 ms speedup across all invocations, driving the overall 15% runtime improvement from 6.24 ms to 5.39 ms.
Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com>
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This PR is now faster! 🚀 @digaobarbosa accepted my optimizations from: |
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This PR is now faster! 🚀 codeflash-ai[bot] accepted my code suggestion above. |
dkosowski87
previously approved these changes
Mar 5, 2026
inference/core/workflows/core_steps/sinks/roboflow/dataset_upload/v2.py
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PawelPeczek-Roboflow
requested changes
Mar 6, 2026
| @classmethod | ||
| def get_parameters_accepting_batches(cls) -> List[str]: | ||
| return ["images", "predictions", "image_name"] | ||
| return ["images", "predictions", "image_name", "metadata"] |
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Looking briefly I am not sure if that would work - here you mark as batch, but the metadata typing in run method speaks about scalar dict
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Will look on this. It's working, but I will dig deeper
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What does this PR do?
Create a metadata field for dataset upload block
And with variables in metadata
Type of Change
Testing
Test details:
Uploaded images with metadata using UI
Checklist
Additional Context