Bump tensorflow from 2.15.0 to 2.17.1

[dependabot]

Bumps tensorflow from 2.15.0 to 2.17.1.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.17.1

Release 2.17.1

Bug Fixes and Other Changes

• Add necessary header files in the aar library. These are needed if developers build apps with header files unpacked from tflite aar files from maven.
• Implement Name() for GCSWritableFile to fix the profiler trace viewer cache file generation.
• Fix cstring.h missing file issue with the Libtensorflow archive.

TensorFlow 2.17.0

Release 2.17.0

TensorFlow

Breaking Changes

• GPU
  • Support for NVIDIA GPUs with compute capability 5.x (Maxwell generation) has been removed from TF binary distributions (Python wheels).

Major Features and Improvements

• Add is_cpu_target_available, which indicates whether or not TensorFlow was built with support for a given CPU target. This can be useful for skipping target-specific tests if a target is not supported.

• tf.data

  • Support data.experimental.distribued_save. distribued_save uses tf.data service (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service) to write distributed dataset snapshots. The call is non-blocking and returns without waiting for the snapshot to finish. Setting wait=True to tf.data.Dataset.load allows the snapshots to be read while they are being written.

Bug Fixes and Other Changes

• GPU

  • Support for NVIDIA GPUs with compute capability 8.9 (e.g. L4 & L40) has been added to TF binary distributions (Python wheels).

• Replace DebuggerOptions of TensorFlow Quantizer, and migrate to DebuggerConfig of StableHLO Quantizer.

• Add TensorFlow to StableHLO converter to TensorFlow pip package.

• TensorRT support: this is the last release supporting TensorRT. It will be removed in the next release.

• NumPy 2.0 support: TensorFlow is going to support NumPy 2.0 in the next release. It may break some edge cases of TensorFlow API usage.

• tf.lite

  • Quantization for FullyConnected layer is switched from per-tensor to per-channel scales for dynamic range quantization use case (float32 inputs / outputs and int8 weights). The change enables new quantization schema globally in the converter and inference engine. The new behaviour can be disabled via experimental flag converter._experimental_disable_per_channel_quantization_for_dense_layers = True.
  • C API:
    • The experimental TfLiteRegistrationExternal type has been renamed as TfLiteOperator, and likewise for the corresponding API functions.
  • The Python TF Lite Interpreter bindings now have an option experimental_default_delegate_latest_features to enable all default delegate features.
  • Flatbuffer version update:
    • GetTemporaryPointer() bug fixed.

• tf.data

  • Add wait to tf.data.Dataset.load. If True, for snapshots written with distributed_save, it reads the snapshot while it is being written. For snapshots written with regular save, it waits for the snapshot until it's finished. The default is False for backward compatibility. Users of distributed_save are recommended to set it to True.

• tf.tpu.experimental.embedding.TPUEmbeddingV2

  • Add compute_sparse_core_stats for sparse core users to profile the data with this API to get the max_ids and max_unique_ids. These numbers will be needed to configure the sparse core embedding mid level api.
  • Remove the preprocess_features method since that's no longer needed.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.17.1

Bug Fixes and Other Changes

• Add necessary header files in the aar library. These are needed if developers build apps with header files unpacked from tflite aar files from maven.
• Implement Name() for GCSWritableFile to fix the profiler trace viewer cache file generation.
• Fix cstring.h missing file issue with the Libtensorflow archive.

Release 2.17.0

TensorFlow

Breaking Changes

• GPU
  • Support for NVIDIA GPUs with compute capability 5.x (Maxwell generation) has been removed from TF binary distributions (Python wheels).

Major Features and Improvements

• Add is_cpu_target_available, which indicates whether or not TensorFlow was built with support for a given CPU target. This can be useful for skipping target-specific tests if a target is not supported.

• tf.data

  • Support data.experimental.distribued_save. distribued_save uses tf.data service (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service) to write distributed dataset snapshots. The call is non-blocking and returns without waiting for the snapshot to finish. Setting wait=True to tf.data.Dataset.load allows the snapshots to be read while they are being written.

Bug Fixes and Other Changes

• GPU

  • Support for NVIDIA GPUs with compute capability 8.9 (e.g. L4 & L40) has been added to TF binary distributions (Python wheels).

• Replace DebuggerOptions of TensorFlow Quantizer, and migrate to DebuggerConfig of StableHLO Quantizer.

• Add TensorFlow to StableHLO converter to TensorFlow pip package.

• TensorRT support: this is the last release supporting TensorRT. It will be removed in the next release.

• NumPy 2.0 support: TensorFlow is going to support NumPy 2.0 in the next release. It may break some edge cases of TensorFlow API usage.

• tf.lite

  • Quantization for FullyConnected layer is switched from per-tensor to per-channel scales for dynamic range quantization use case (float32 inputs / outputs and int8 weights). The change enables new quantization schema globally in the converter and inference engine. The new behaviour can be disabled via experimental flag converter._experimental_disable_per_channel_quantization_for_dense_layers = True.
  • C API:
    • The experimental TfLiteRegistrationExternal type has been renamed as TfLiteOperator, and likewise for the corresponding API functions.
  • The Python TF Lite Interpreter bindings now have an option experimental_default_delegate_latest_features to enable all default delegate features.
  • Flatbuffer version update:
    • GetTemporaryPointer() bug fixed.

• tf.data

  • Add wait to tf.data.Dataset.load. If True, for snapshots written with distributed_save, it reads the snapshot while it is being written. For snapshots written with regular save, it waits for the snapshot until it's finished. The default is False for backward compatibility. Users of distributed_save are recommended to set it to True.

• tf.tpu.experimental.embedding.TPUEmbeddingV2

  • Add compute_sparse_core_stats for sparse core users to profile the data with this API to get the max_ids and max_unique_ids. These numbers will be needed to configure the sparse core embedding mid level api.
  • Remove the preprocess_features method since that's no longer needed.

Thanks to our Contributors

... (truncated)

Commits

• 3c92ac0 Merge pull request #72195 from tensorflow/r2.17-314385ab894
• e94e729 Merge pull request #76600 from tensorflow/rtg0795-patch-1
• 9968f2e Update RELEASE.md for 2.17.1
• 9e4fc3d Merge pull request #76506 from tensorflow-jenkins/version-numbers-2.17.1-24888
• ac4cc89 Update version numbers to 2.17.1
• 30ed8fc Merge pull request #76497 from tensorflow/r2.17-364b0cae088
• 4766028 Use remap_paths instead of strip_prefix for `//tensorflow/tools/lib_packa...
• 999767f Merge pull request #73658 from zzzaries/cherrypicks_3OGX4
• 5cfc525 Implement Name() for GCSWritableFile.
• 5183651 Merge pull request #72594 from tensorflow/r2.17-3735c15ed47
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