Non cumulative model.predict #21673
Non cumulative model.predict #21673
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Introduces an 'accumulate' argument to the predict method in all backend trainers and the base Trainer class. When set to False, predictions are not accumulated in memory and must be handled via callbacks, helping to avoid memory issues with large datasets. Updates method signatures, docstrings, and internal logic accordingly.
Standardizes the handling of outputs in the predict methods across all backend trainers by always initializing outputs as None and returning None when no outputs are accumulated. This simplifies the logic and ensures consistent behavior when accumulate is False or when no predictions are made.
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Summary of Changes
Hello @pctablet505, 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 implements a highly requested feature for non-cumulative prediction within the model.predict function. By adding an accumulate boolean parameter, it allows users to prevent the accumulation of all prediction outputs in memory. This is particularly beneficial for scenarios involving very large datasets or environments with limited memory, as it enables processing of outputs through callbacks without storing the entire result set, thereby enhancing the scalability and efficiency of the prediction process.
Highlights
- Non-cumulative Prediction: Introduced an
accumulateparameter to thepredictmethod across all backend trainers (JAX, NumPy, OpenVINO, TensorFlow, Torch) and the basetrainer.py. - Memory Optimization: When
accumulateis set toFalse, thepredictmethod will no longer store all batch outputs in memory, addressing memory limitations for very large datasets. - Callback Integration: Users can now handle prediction outputs via callbacks when
accumulate=False, allowing for custom storage or processing without accumulating results in the main return value. - Docstring Updates: The docstring for the
predictmethod inkeras/src/trainers/trainer.pyhas been updated to clearly explain the newaccumulateparameter and its effect on the method's return value.
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Code Review
This pull request introduces a new accumulate parameter to the model.predict() method across all backends. This is a valuable feature for users working with very large datasets, as it allows them to process predictions batch by batch using callbacks without storing all outputs in memory, thus preventing potential out-of-memory errors. The implementation is consistent across the JAX, NumPy, TensorFlow, and Torch backends. However, I've found a critical issue in the OpenVINO backend implementation that needs to be addressed.
Refactored the predict method signatures in all backend trainer classes and the base Trainer to use one argument per line. Also reformatted long return statements for better readability. No functional changes were made.
…ablet505/keras into non_cumulative_model_predict
Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## master #21673 +/- ##
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- Coverage 82.53% 82.51% -0.02%
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Files 571 571
Lines 57900 57925 +25
Branches 9056 9071 +15
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+ Hits 47785 47797 +12
- Misses 7800 7809 +9
- Partials 2315 2319 +4
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there was a feature request for non cumulative model.predict, where we don't store the outputs and return it directly.
#21642 In case of very large datasets and limited memory, it is more feasible to retrieve the outputs using a callback and store to storage, instead of storing everything in memory.
This PR fixes #21642.