Bump the tensorflow group in /tensorflow with 2 updates

[dependabot]

Bumps the tensorflow group in /tensorflow with 2 updates: tensorflow and numpy.

Updates tensorflow from 2.15.0 to 2.17.0

Release notes

Sourced from tensorflow's releases.

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.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abdulaziz Aloqeely, Ahmad-M-Al-Khateeb, Akhil Goel, akhilgoe, Alexander Pivovarov, Amir Samani, Andrew Goodbody, Andrey Portnoy, Ashiq Imran, Ben Olson, Chao, Chase Riley Roberts, Clemens Giuliani, dependabot[bot], Dimitris Vardoulakis, Dragan Mladjenovic, ekuznetsov139, Elfie Guo, Faijul Amin, Gauri1 Deshpande, Georg Stefan Schmid, guozhong.zhuang, Hao Wu, Haoyu (Daniel), Harsha H S, Harsha Hs, Harshit Monish, Ilia Sergachev, Jane Liu, Jaroslav Sevcik, Jinzhe Zeng, Justin Dhillon, Kaixi Hou, Kanvi Khanna, LakshmiKalaKadali, Learning-To-Play, lingzhi98, Lu Teng, Matt Bahr, Max Ren, Meekail Zain, Mmakevic-Amd, mraunak, neverlva, nhatle, Nicola Ferralis, Olli Lupton, Om Thakkar, orangekame3, ourfor, pateldeev, Pearu Peterson, pemeliya, Peng Sun, Philipp Hack, Pratik Joshi, prrathi, rahulbatra85, Raunak, redwrasse, Robert Kalmar, Robin Zhang, RoboSchmied, Ruturaj Vaidya, sachinmuradi, Shawn Wang, Sheng Yang, Surya, Thibaut Goetghebuer-Planchon, Thomas Preud'Homme, tilakrayal, Tj Xu, Trevor Morris, wenchenvincent, Yimei Sun, zahiqbal, Zhu Jianjiang, Zoranjovanovic-Ns

TensorFlow 2.17.0-rc1

Release 2.17.0

... (truncated)

Changelog

Sourced from tensorflow's changelog.

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

This release contains contributions from many people at Google, as well as:

Abdulaziz Aloqeely, Ahmad-M-Al-Khateeb, Akhil Goel, akhilgoe, Alexander Pivovarov, Amir Samani, Andrew Goodbody, Andrey Portnoy, Ashiq Imran, Ben Olson, Chao, Chase Riley Roberts, Clemens Giuliani, dependabot[bot], Dimitris Vardoulakis, Dragan Mladjenovic, ekuznetsov139, Elfie Guo, Faijul Amin, Gauri1 Deshpande, Georg Stefan Schmid, guozhong.zhuang, Hao Wu, Haoyu (Daniel), Harsha H S, Harsha Hs, Harshit Monish, Ilia Sergachev, Jane Liu, Jaroslav Sevcik, Jinzhe Zeng, Justin Dhillon, Kaixi Hou, Kanvi Khanna, LakshmiKalaKadali, Learning-To-Play, lingzhi98, Lu Teng, Matt Bahr, Max Ren, Meekail Zain, Mmakevic-Amd, mraunak, neverlva, nhatle, Nicola Ferralis, Olli Lupton, Om Thakkar, orangekame3, ourfor, pateldeev, Pearu Peterson, pemeliya, Peng Sun, Philipp Hack, Pratik Joshi, prrathi, rahulbatra85, Raunak, redwrasse, Robert Kalmar, Robin Zhang, RoboSchmied, Ruturaj Vaidya, sachinmuradi, Shawn Wang, Sheng Yang, Surya, Thibaut Goetghebuer-Planchon, Thomas Preud'Homme, tilakrayal, Tj Xu, Trevor Morris, wenchenvincent, Yimei Sun, zahiqbal, Zhu Jianjiang, Zoranjovanovic-Ns

Release 2.16.2

Bug Fixes and Other Changes

... (truncated)

Commits

• ad6d8cc Merge pull request #71345 from tensorflow-jenkins/version-numbers-2.17.0-6959
• 8ca87bf Update version numbers to 2.17.0
• b3dcff9 Merge pull request #70600 from tensorflow/r2.17-2d72742d40f
• 742ccbb Add tensorflow support for 16k page sizes on arm64
• 8581151 Merge pull request #70475 from tensorflow-jenkins/version-numbers-2.17.0rc1-8204
• d6b2aa0 Update version numbers to 2.17.0-rc1
• bb8057c Merge pull request #70454 from vladbelit/gcs_trailing_dot_undo
• 72f4b02 Fix issues with TF GCS operations not working in certain environments.
• 6ed0a1a Merge pull request #70358 from tensorflow/r2.17-b24db0b2a85
• ffca2f5 Add back xla/stream_executor:cuda_platform to tf_additional_binary_deps.
• Additional commits viewable in compare view

Updates numpy from 2.0.1 to 2.1.0

Release notes

Sourced from numpy's releases.

2.1.0 (Aug 18, 2024)

NumPy 2.1.0 Release Notes

NumPy 2.1.0 provides support for the upcoming Python 3.13 release and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get us back into our usual release cycle after the extended development of 2.0. The highlights for this release are:

• Support for the array-api 2023.12 standard.
• Support for Python 3.13.
• Preliminary support for free threaded Python 3.13.

Python versions 3.10-3.13 are supported in this release.

New functions

New function numpy.unstack

A new function np.unstack(array, axis=...) was added, which splits an array into a tuple of arrays along an axis. It serves as the inverse of [numpy.stack]{.title-ref}.

(gh-26579)

Deprecations

• The fix_imports keyword argument in numpy.save is deprecated. Since NumPy 1.17, numpy.save uses a pickle protocol that no longer supports Python 2, and ignored fix_imports keyword. This keyword is kept only for backward compatibility. It is now deprecated.

  (gh-26452)

• Passing non-integer inputs as the first argument of [bincount]{.title-ref} is now deprecated, because such inputs are silently cast to integers with no warning about loss of precision.

  (gh-27076)

Expired deprecations

• Scalars and 0D arrays are disallowed for numpy.nonzero and numpy.ndarray.nonzero.

  (gh-26268)

• set_string_function internal function was removed and PyArray_SetStringFunction was stubbed out.

... (truncated)

Commits

• 2f7fe64 Merge pull request #27236 from charris/prepare-2.1.0
• b6f434f REL: Prepare for the NumPy 2.1.0 release [wheel build]
• 3cf9394 Merge pull request #27234 from charris/backport-25984
• 7443dcc Merge pull request #27233 from charris/backport-27223
• 85b1cab BUG: Allow fitting of degree zero polynomials with Polynomial.fit
• 395a81d DOC: reword discussion about shared arrays to hopefully be clearer
• 5af2e96 Move NUMUSERTYPES thread safety discussion to legacy DType API docs
• d902c24 DOC: add docs on thread safety in NumPy
• c080180 Merge pull request #27229 from charris/backport-27226
• 44ce7e8 BUG: Fix PyArray_ZeroContiguousBuffer (resize) with struct dtypes
• Additional commits viewable in compare view

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[github-actions]

Dependency Review

✅ No vulnerabilities or license issues or OpenSSF Scorecard issues found.

OpenSSF Scorecard

Package	Version	Score	Details
pip/tensorflow	2.17.0	🟢 7.9	Details Check Score Reason Binary-Artifacts 🟢 7 binaries present in source code Branch-Protection ⚠️ -1 internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration CI-Tests 🟢 9 22 out of 23 merged PRs checked by a CI test -- score normalized to 9 CII-Best-Practices 🟢 5 badge detected: Passing Code-Review ⚠️ 2 Found 7/30 approved changesets -- score normalized to 2 Contributors 🟢 10 project has 21 contributing companies or organizations Dangerous-Workflow 🟢 10 no dangerous workflow patterns detected Dependency-Update-Tool 🟢 10 update tool detected Fuzzing 🟢 10 project is fuzzed License 🟢 10 license file detected Maintained 🟢 10 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10 Packaging 🟢 10 packaging workflow detected Pinned-Dependencies ⚠️ 1 dependency not pinned by hash detected -- score normalized to 1 SAST 🟢 3 SAST tool is not run on all commits -- score normalized to 3 Security-Policy 🟢 10 security policy file detected Signed-Releases ⚠️ -1 no releases found Token-Permissions 🟢 9 detected GitHub workflow tokens with excessive permissions Vulnerabilities 🟢 10 0 existing vulnerabilities detected
pip/numpy	2.1.0	🟢 8	Details Check Score Reason Binary-Artifacts 🟢 10 no binaries found in the repo Branch-Protection 🟢 3 branch protection is not maximal on development and all release branches CI-Tests 🟢 10 17 out of 17 merged PRs checked by a CI test -- score normalized to 10 CII-Best-Practices ⚠️ 0 no effort to earn an OpenSSF best practices badge detected Code-Review 🟢 10 all changesets reviewed Contributors 🟢 10 project has 93 contributing companies or organizations Dangerous-Workflow 🟢 10 no dangerous workflow patterns detected Dependency-Update-Tool 🟢 10 update tool detected Fuzzing 🟢 10 project is fuzzed License 🟢 9 license file detected Maintained 🟢 10 30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10 Packaging ⚠️ -1 packaging workflow not detected Pinned-Dependencies 🟢 3 dependency not pinned by hash detected -- score normalized to 3 SAST 🟢 9 SAST tool detected but not run on all commits Security-Policy 🟢 9 security policy file detected Signed-Releases ⚠️ 0 Project has not signed or included provenance with any releases. Token-Permissions 🟢 10 GitHub workflow tokens follow principle of least privilege Vulnerabilities 🟢 10 0 existing vulnerabilities detected
pip/tensorflow	2.17.0	🟢 7.9	Details Check Score Reason Binary-Artifacts 🟢 7 binaries present in source code Branch-Protection ⚠️ -1 internal error: error during branchesHandler.setup: internal error: githubv4.Query: Resource not accessible by integration CI-Tests 🟢 9 22 out of 23 merged PRs checked by a CI test -- score normalized to 9 CII-Best-Practices 🟢 5 badge detected: Passing Code-Review ⚠️ 2 Found 7/30 approved changesets -- score normalized to 2 Contributors 🟢 10 project has 21 contributing companies or organizations Dangerous-Workflow 🟢 10 no dangerous workflow patterns detected Dependency-Update-Tool 🟢 10 update tool detected Fuzzing 🟢 10 project is fuzzed License 🟢 10 license file detected Maintained 🟢 10 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10 Packaging 🟢 10 packaging workflow detected Pinned-Dependencies ⚠️ 1 dependency not pinned by hash detected -- score normalized to 1 SAST 🟢 3 SAST tool is not run on all commits -- score normalized to 3 Security-Policy 🟢 10 security policy file detected Signed-Releases ⚠️ -1 no releases found Token-Permissions 🟢 9 detected GitHub workflow tokens with excessive permissions Vulnerabilities 🟢 10 0 existing vulnerabilities detected

Scanned Manifest Files

tensorflow/requirements.txt

• tensorflow@2.17.0
• tensorflow@2.15.1

tensorflow/serving/requirements.txt

• numpy@2.1.0
• numpy@2.0.1

tensorflow/xpu-requirements.txt

• tensorflow@2.17.0
• tensorflow@2.15.0

[dependabot]

Looks like these dependencies are updatable in another way, so this is no longer needed.