Added OrbaxCheckpoint for keras 3.0 for Data centric saving and restore #21762

amitsrivastava78 · 2025-10-22T06:09:26Z

Supports following feature

Asynchronous Checkpointing
Composite Checkpointing
Preservation Policies
Save Decision Policies
Transformations - Custom Handlers

…re Supports following feature - Asynchronous Checkpointing - Composite Checkpointing - Preservation Policies - Save Decision Policies - Transformations - Custom Handlers

gemini-code-assist · 2025-10-22T06:09:44Z

Summary of Changes

Hello @amitsrivastava78, 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 integrates Orbax checkpointing into Keras 3.0, providing a robust and flexible mechanism for saving and restoring training progress. The new OrbaxCheckpoint callback offers features like asynchronous saving, customizable save policies, and the ability to save complex states including model weights, optimizer variables, metrics, and data iterator positions. This enhancement significantly improves the reliability and efficiency of training large models, especially in distributed environments, by leveraging Orbax's advanced capabilities.

Highlights

New OrbaxCheckpoint Callback: Introduces a new OrbaxCheckpoint callback for Keras 3.0, enabling advanced data-centric saving and restoration of model states.
Asynchronous Checkpointing: Supports asynchronous saving of model weights and optimizer states, allowing training to continue without I/O blocking.
Comprehensive Checkpointing Features: Includes support for composite checkpointing, preservation policies (e.g., max_to_keep, keep_period), save decision policies (e.g., save_interval), and custom transformations during saving.
Distributed Training Support: Adds a get_process_index utility function to the Keras backend, facilitating distributed training setups by identifying the primary process for checkpoint operations across JAX, TensorFlow, and PyTorch.
Extensible with Custom Handlers: Exposes advanced Orbax functionalities like CheckpointManager, TypeHandler, and register_type_handler to allow users to define custom serialization logic for complex objects.
Iterator State Saving and Restoration: Enables saving and restoring the state of data iterators, crucial for seamless training resumption from a specific point, with backend-specific examples for TensorFlow, JAX, and PyTorch.

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Code Review

This pull request introduces OrbaxCheckpoint, a new Keras callback for advanced checkpointing using the Orbax library. This is a significant feature addition that enables asynchronous saving, composite checkpoints, and other powerful capabilities. The implementation is extensive and is supported by a comprehensive suite of tests.

My review has identified several important issues that need attention. There are critical correctness and performance bugs in the main implementation: the batch-based saving logic is flawed, and the asynchronous saving feature is effectively disabled by blocking calls. Additionally, some features are incomplete, and there are minor areas for improvement in the tests to enhance maintainability. I have provided specific suggestions to address these points. After these fixes, this will be a very valuable addition to Keras.

keras/src/callbacks/orbax_checkpoint.py

gemini-code-assist · 2025-10-22T06:11:41Z

keras/src/callbacks/orbax_checkpoint.py

+    def __init__(
+        self,
+        directory,
+        monitor="val_loss",
+        verbose=0,
+        save_best_only=False,
+        mode="auto",
+        save_freq="epoch",
+        max_to_keep=5,
+        keep_period=None,
+        initial_value_threshold=None,
+        save_optimizer_state=True,
+        save_on_background=True,
+        save_metadata=None,
+        save_data_iterator=None,
+        save_metrics_state=False,
+        async_timeout_secs=600,
+        enable_background_delete=False,
+        post_finalization_callback=None,
+        save_transforms=None,
+        save_decision_policy=None,
+        save_interval=None,
+    ):


The __init__ method has 16 arguments, which is quite high. The Keras API design guidelines suggest reconsidering signatures with more than 6-7 arguments.¹ While I understand the need to expose Orbax's functionality, it might be worth exploring if some of these could be grouped into a configuration object to improve readability and usability, similar to how ocp.CheckpointManagerOptions is used internally.

Style Guide References

Footnotes

The style guide recommends that functions with more than 6-7 arguments should be re-evaluated for simplification, possibly by breaking them into smaller objects or modular pieces. ↩

keras/src/callbacks/orbax_checkpoint.py

keras/src/callbacks/orbax_checkpoint_test.py

codecov-commenter · 2025-10-22T06:15:42Z

Codecov Report

❌ Patch coverage is 78.17259% with 43 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.65%. Comparing base (47fcb39) to head (33f4e66).
⚠️ Report is 38 commits behind head on master.

Files with missing lines	Patch %	Lines
keras/src/callbacks/orbax_checkpoint.py	77.77%	23 Missing and 19 partials ⚠️
keras/api/_tf_keras/keras/callbacks/__init__.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21762      +/-   ##
==========================================
- Coverage   82.69%   82.65%   -0.04%     
==========================================
  Files         573      578       +5     
  Lines       58888    59670     +782     
  Branches     9218     9374     +156     
==========================================
+ Hits        48696    49319     +623     
- Misses       7845     7929      +84     
- Partials     2347     2422      +75

Flag	Coverage Δ
keras	`82.47% <77.66%> (-0.03%)`	⬇️
keras-jax	`63.35% <76.14%> (+0.11%)`	⬆️
keras-numpy	`57.41% <17.25%> (-0.31%)`	⬇️
keras-openvino	`34.28% <17.25%> (-0.12%)`	⬇️
keras-tensorflow	`64.15% <72.58%> (+0.14%)`	⬆️
keras-torch	`63.64% <72.58%> (+0.07%)`	⬆️

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hertschuh

Thanks for the PR. This checkpointing system has a ton of features!

Quick first pass.

keras/src/backend/__init__.py

keras/src/callbacks/__init__.py

keras/src/callbacks/orbax_checkpoint.py

hertschuh

A couple more comments I forgot.

keras/src/callbacks/orbax_checkpoint.py

keras/src/callbacks/orbax_checkpoint_test.py

- Remove conditional export decorator to ensure OrbaxCheckpoint is always available - Remove unnecessary exception handling in state tree operations - Update process index check comment for clarity - Format code to comply with 80-character line limit - Add distribution_lib modules for backend-specific distributed training support

- Remove unused 'result' variable in _reconstruct_state_tree_with_values - Fix long comment line in test file - Apply code formatting changes

…st handling - Implement OrbaxCheckpoint callback for async checkpointing with state tree handling - Add conditional exports for optional orbax-checkpoint dependency - Use pytest.importorskip for clean optional dependency testing - Ensure graceful handling when orbax-checkpoint is not installed

hertschuh

The JAX implementation of def process_id() is missing.

General questions:

Does this as-is support all backends?
Does this support JAX sharding? I don't see anything related to sharing (which may be normal). What about re-sharding?

keras/src/backend/__init__.py

keras/src/callbacks/orbax_checkpoint.py

keras/src/callbacks/orbax_checkpoint_test.py

keras/src/callbacks/orbax_checkpoint.py

- Preserve nested state tree structures instead of flattening for better layer name preservation - Add backward compatibility for old flattened format checkpoints - Simplify test class by using self.get_temp_dir() instead of setUp/tearDown - Remove silent pytest.importorskip, add explicit skip conditions for backend-specific tests - Move process_id function from backend to distribution module - Update imports to use centralized LazyModule for orbax.checkpoint - Test across all backends (JAX, TensorFlow, PyTorch) - all passing

keras/src/callbacks/orbax_checkpoint.py

cpgaffney1 · 2025-11-05T20:46:49Z

keras/src/callbacks/orbax_checkpoint.py

+        checkpoints if there might be pending save operations.
+        """
+        # Wait for any async operations to complete
+        while self.checkpointer.is_saving_in_progress():


Probably better to use checkpointer.wait() here, unless you want to log things periodicially.

I am using Orbax's experimental v1 API, but the checkpointer.wait() method doesn't exist in our installed version. The GitHub link you provided might be from a development branch, i can see it here https://github.com/google/orbax/blob/main/checkpoint/orbax/checkpoint/experimental/v1/_src/training/checkpointer.py#L521 but not on the installed released version.

…s expected failures Neural networks are inherently non-deterministic, so pipeline consistency checks should be skipped rather than fail. Added check_pipeline_consistency to EXPECTED_FAILED_CHECKS for all sklearn wrapper types.

- Avoid unnecessary numpy conversion in _get_state_tree() for JAX backend - Preserve JAX arrays during saving instead of converting to numpy - Maintain cross-backend compatibility with proper loading conversions - Update async waiting to use CheckpointManager.wait_until_finished() - Implement AlwaysSavePolicy for reliable save decisions - Add expected failures for sklearn tests due to neural network non-determinism

- Preserve JAX arrays during saving when jax.monitoring.record_scalar is available - Fall back to numpy conversion for older JAX versions that don't have record_scalar - Maintain cross-backend compatibility while avoiding unnecessary conversions - Update async waiting to use CheckpointManager.wait_until_finished() - Implement AlwaysSavePolicy for reliable save decisions - Add expected failures for sklearn tests due to neural network non-determinism

Added OrbaxCheckpoint for keras 3.0 for Data centric saving and resto…

6328350

…re Supports following feature - Asynchronous Checkpointing - Composite Checkpointing - Preservation Policies - Save Decision Policies - Transformations - Custom Handlers

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Fix unused variable in orbax checkpoint test

ca71da6

amitsrivastava78 added 2 commits October 22, 2025 13:15

fixed failing cases

4dfa903

fixed review comments

7742139

hertschuh reviewed Oct 23, 2025

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amitsrivastava78 added 3 commits October 24, 2025 10:05

Fix code formatting and remove unused variable

61bd5e6

- Remove unused 'result' variable in _reconstruct_state_tree_with_values - Fix long comment line in test file - Apply code formatting changes

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Fixed review comments

7722e30

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hertschuh mentioned this pull request Nov 5, 2025

Multi host & sharding support for keras 3.0 #21786

Open

cpgaffney1 reviewed Nov 5, 2025

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Migration to Orbax V1

eb7855d

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amitsrivastava78 added 2 commits November 10, 2025 16:01

made distributed structure proper

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Fixed sav decision between keras and orbax

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Added OrbaxCheckpoint for keras 3.0 for Data centric saving and restore #21762

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Added OrbaxCheckpoint for keras 3.0 for Data centric saving and restore #21762

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amitsrivastava78 commented Oct 22, 2025

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