tensorflow
TensorFlow is an open source machine learning framework for everyone.
Description
[](https://badge.fury.io/py/tensorflow) [](https://badge.fury.io/py/tensorflow) TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google's AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains. TensorFlow is licensed under [Apache 2.0](https://github.com/tensorflow/tensorflow/blob/master/LICENSE).
Release History
| Version | Changes | Urgency | Date |
|---|---|---|---|
| 2.21.0 | Imported from PyPI (2.21.0) | Low | 4/21/2026 |
| v2.21.0 | # Release 2.21.0 ## TensorFlow ### Breaking Changes * Support for Python 3.9 has been removed starting with TF 2.21. * The TensorBoard (TB) dependency has been removed starting with TF 2.21. ### Major Features and Improvements * `tf.lite` * Adds int8 and int16x8 support for SQRT operator. * Adds int16x8 support for EQUAL and NOT_EQUAL operators. * Adds support for int2 type. * Adds support for int2/int4 in tfl.cast . * Adds support for SRQ int2 in tfl.ful | Low | 3/6/2026 |
| v2.21.0-rc1 | # Release 2.21.0 ## TensorFlow ### Breaking Changes * Support for Python 3.9 has been removed starting with TF 2.21. ### Major Features and Improvements * `tf.lite` * Adds int8 and int16x8 support for SQRT operator. * Adds int16x8 support for EQUAL and NOT_EQUAL operators. * Adds support for int2 type. * Adds support for int2/int4 in tfl.cast . * Adds support for SRQ int2 in tfl.fully_connected. * Adds support for int4 in tfl.slice. * Adds suppor | Low | 3/2/2026 |
| v2.21.0-rc0 | # Release 2.21.0 ## TensorFlow ### Breaking Changes * Support for Python 3.9 has been removed starting with TF 2.21. ### Major Features and Improvements * `tf.lite` * Adds int8 and int16x8 support for SQRT operator. * Adds int16x8 support for EQUAL and NOT_EQUAL operators. * Adds support for int2 type. * Adds support for int2/int4 in tfl.cast . * Adds support for SRQ int2 in tfl.fully_connected. * Adds support for int4 in tfl.slice. * Adds suppor | Low | 2/9/2026 |
| v2.20.0 | # Release 2.20.0 ## TensorFlow ### Breaking Changes * The `tensorflow-io-gcs-filesystem` package is now optional, due its uncertain, and limited support. To install it alongside `tensorflow`, run `pip install "tensorflow[gcs-filesystem]"`. ### Major Features and Improvements * `tf.data` * Adds `autotune.min_parallelism` to `tf.data.Options` to enable faster input pipeline warm up. * `tf.lite` * tf.lite will be deprecated, in favor of the new repo https://github.com/goog | Low | 8/13/2025 |
| v2.19.1 | # Release 2.19.1 ### Bug Fixes and Other Changes * Fix save_model.save for Serving embedding and add SparseCore Reshard. | Low | 8/13/2025 |
| v2.20.0-rc0 | # Release 2.20.0 ## TensorFlow ### Breaking Changes * The `tensorflow-io-gcs-filesystem` package is now optional, due its uncertain, and limited support. To install it alongside `tensorflow`, run `pip install "tensorflow[gcs-filesystem]"`. ### Major Features and Improvements * `tf.data` * Adds `autotune.min_parallelism` to `tf.data.Options` to enable faster input pipeline warm up. * `tf.lite` * tf.lite will be deprecated, in favor of the new repo https://github.com/goog | Low | 7/28/2025 |
| v2.19.0 | # Release 2.19.0 ## TensorFlow ### Breaking Changes * `LiteRT`, a.k.a. `tf.lite`: * C++ API: * The public constants `tflite::Interpreter:kTensorsReservedCapacity` and `tflite::Interpreter:kTensorsCapacityHeadroom` are now const references, rather than `constexpr` compile-time constants. (This is to enable better API compatibility for TFLite in Play services while preserving the implementation flexibility to change the values of these constants in the future.) * Python A | Low | 3/12/2025 |
| v2.18.1 | # Release 2.18.1 ### Security * Updates curl to `8.11.0` to handle [CVE-2024-2004](https://github.com/advisories/GHSA-97xx-95pm-5qv6), [CVE-2024-2379](https://github.com/advisories/GHSA-wr4c-gwg7-p734), [CVE-2024-2398](https://github.com/advisories/GHSA-mq8w-c2j9-rqxc), [CVE-2024-2466](https://github.com/advisories/GHSA-9xr6-qf7m-2jv5), [CVE-2024-6197](https://github.com/advisories/GHSA-x3h8-3mf2-v794), [CVE-2024-7264](https://github.com/advisories/GHSA-97c4-2w4v-c7r8), [CVE-2024-8096](https | Low | 3/11/2025 |
| v2.19.0-rc0 | # Release 2.19.0 ## TensorFlow ### Breaking Changes * `LiteRT`, a.k.a. `tf.lite`: * C++ API: * The public constants `tflite::Interpreter:kTensorsReservedCapacity` and `tflite::Interpreter:kTensorsCapacityHeadroom` are now const references, rather than `constexpr` compile-time constants. (This is to enable better API compatibility for TFLite in Play services while preserving the implementation flexibility to change the values of these constants in the future.) * Interpreter: | Low | 2/24/2025 |
| v2.18.0 | # Release 2.18.0 ## TensorFlow ### Breaking Changes * `tf.lite` * C API: * An optional, fourth parameter was added `TfLiteOperatorCreate` as a step forward towards a cleaner API for `TfLiteOperator`. Function `TfLiteOperatorCreate` was added recently, in TensorFlow Lite version 2.17.0, released on 7/11/2024, and we do not expect there will be much code using this function yet. Any code breakages can be easily resolved by passing nullptr as the new, 4th parameter. * Tensor | Low | 10/24/2024 |
| v2.17.1 | # 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. | Low | 10/24/2024 |
| v2.18.0-rc2 | # Release 2.18.0 ## TensorFlow ### Breaking Changes * `tf.lite` * C API: * An optional, fourth parameter was added `TfLiteOperatorCreate` as a step forward towards a cleaner API for `TfLiteOperator`. Function `TfLiteOperatorCreate` was added recently, in TensorFlow Lite version 2.17.0, released on 7/11/2024, and we do not expect there will be much code using this function yet. Any code breakages can be easily resolved by passing nullptr as the new, 4th parameter. * Sign | Low | 10/16/2024 |
| v2.18.0-rc1 | # Release 2.18.0 ## TensorFlow ### Breaking Changes * `tf.lite` * C API: * An optional, fourth parameter was added `TfLiteOperatorCreate` as a step forward towards a cleaner API for `TfLiteOperator`. Function `TfLiteOperatorCreate` was added recently, in TensorFlow Lite version 2.17.0, released on 7/11/2024, and we do not expect there will be much code using this function yet. Any code breakages can be easily resolved by passing nullptr as the new, 4th parameter. * Sign | Low | 10/7/2024 |
| v2.18.0-rc0 | # Release 2.18.0 ## TensorFlow ### Breaking Changes * `tf.lite` * C API: * An optional, fourth parameter was added `TfLiteOperatorCreate` as a step forward towards a cleaner API for `TfLiteOperator`. Function `TfLiteOperatorCreate` was added recently, in TensorFlow Lite version 2.17.0, released on 7/11/2024, and we do not expect there will be much code using this function yet. Any code breakages can be easily resolved by passing nullptr as the new, 4th parameter. * Sign | Low | 9/30/2024 |
| v2.17.0 | # 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.ex | Low | 7/11/2024 |
| v2.17.0-rc1 | # 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.ex | Low | 7/2/2024 |
| v2.16.2 | # Release 2.16.2 ### Bug Fixes and Other Changes * Fixed: Incorrect dependency metadata in TensorFlow Python packages causing installation failures with certain package managers such as Poetry. | Low | 6/28/2024 |
| v2.17.0-rc0 | # 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.ex | Low | 6/18/2024 |
| v2.15.1 | # Release 2.15.1 ### Bug Fixes and Other Changes * `ml_dtypes` runtime dependency is updated to `0.3.1` to fix package conflict issues | Low | 3/8/2024 |
| v2.16.1 | # Release 2.16.1 ## TensorFlow * TensorFlow Windows Build: * Clang is now the default compiler to build TensorFlow CPU wheels on the Windows Platform starting with this release. The currently supported version is LLVM/clang 17. The official Wheels-published on PyPI will be based on Clang; however, users retain the option to build wheels using the MSVC compiler following the steps mentioned in https://www.tensorflow.org/install/source_windows as has been the case before * Tenso | Low | 3/7/2024 |
| v2.16.0-rc0 | # Release 2.16.0 ## TensorFlow * TensorFlow Windows Build: * Clang is now the default compiler to build TensorFlow CPU wheels on the Windows Platform starting with this release. The currently supported version is LLVM/clang 17. The official Wheels-published on PyPI will be based on Clang; however, users retain the option to build wheels using the MSVC compiler following the steps mentioned in https://www.tensorflow.org/install/source_windows as has been the case before ### Brea | Low | 2/26/2024 |
| v2.15.0 | # Release 2.15.0 ## TensorFlow ### Breaking Changes * `tf.types.experimental.GenericFunction` has been renamed to `tf.types.experimental.PolymorphicFunction`. ### Major Features and Improvements * [oneDNN CPU performance optimizations](https://github.com/tensorflow/community/blob/master/rfcs/20210930-enable-onednn-ops.md) Windows x64 & x86. * **Windows x64 & x86 packages:** * oneDNN optimizations are *enabled by default* on X86 CPUs * To explicitly ena | Low | 11/14/2023 |
| v2.14.1 | # Release 2.14.1 ## Security * Updates `curl` to `8.4.0` to handle [CVE-2023-38545](https://curl.se/docs/CVE-2023-38545.html) and [CVE-2023-38546](https://curl.se/docs/CVE-2023-38546.html). | Low | 11/14/2023 |
| v2.15.0-rc1 | # Release 2.15.0 ## TensorFlow ### Breaking Changes * `tf.types.experimental.GenericFunction` has been renamed to `tf.types.experimental.PolymorphicFunction`. ### Known Caveats ### Major Features and Improvements * [oneDNN CPU performance optimizations](https://github.com/tensorflow/community/blob/master/rfcs/20210930-enable-onednn-ops.md) Windows x64 & x86. * **Windows x64 & x86 packages:** * oneDNN optimizations are *enabled by default* on X86 CPUs | Low | 11/3/2023 |
| v2.15.0-rc0 | # Release 2.15.0 ## TensorFlow ### Breaking Changes * `tf.types.experimental.GenericFunction` has been renamed to `tf.types.experimental.PolymorphicFunction`. ### Major Features and Improvements * [oneDNN CPU performance optimizations](https://github.com/tensorflow/community/blob/master/rfcs/20210930-enable-onednn-ops.md) Windows x64 & x86. * **Windows x64 & x86 packages:** * oneDNN optimizations are *enabled by default* on X86 CPUs * To explicitly ena | Low | 10/25/2023 |
| v2.14.0 | # Release 2.14.0 ## Tensorflow ### Breaking Changes * Support for Python 3.8 has been removed starting with TF 2.14. The TensorFlow 2.13.1 patch release will still have Python 3.8 support. * `tf.Tensor` * The class hierarchy for `tf.Tensor` has changed, and there are now explicit `EagerTensor` and `SymbolicTensor` classes for eager and tf.function respectively. Users who relied on the exact type of Tensor (e.g. `type(t) == tf.Tensor`) will need to update their code to use `is | Low | 9/26/2023 |
| v2.13.1 | # Release 2.13.1 ### Bug Fixes and Other Changes * Refactor CpuExecutable to propagate LLVM errors. | Low | 9/26/2023 |
| v2.14.0-rc1 | # Release 2.14.0 ## Tensorflow ### Breaking Changes * Support for Python 3.8 has been removed starting with TF 2.14. The TensorFlow 2.13.1 patch release will still have Python 3.8 support. * `tf.Tensor` * The class hierarchy for `tf.Tensor` has changed, and there are now explicit `EagerTensor` and `SymbolicTensor` classes for eager and tf.function respectively. Users who relied on the exact type of Tensor (e.g. `type(t) == tf.Tensor`) will need to update their code to use `is | Low | 8/31/2023 |
| v2.14.0-rc0 | # Release 2.14.0 ## Tensorflow ### Breaking Changes * `tf.Tensor` * The class hierarchy for `tf.Tensor` has changed, and there are now explicit `EagerTensor` and `SymbolicTensor` classes for eager and tf.function respectively. Users who relied on the exact type of Tensor (e.g. `type(t) == tf.Tensor`) will need to update their code to use `isinstance(t, tf.Tensor)`. The `tf.is_symbolic_tensor` helper added in 2.13 may be used when it is necessary to determine if a value is specific | Low | 8/17/2023 |
| v2.12.1 | # Release 2.12.1 ### Bug Fixes and Other Changes * The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance. | Low | 7/5/2023 |
| v2.13.0 | # Release 2.13.0 ## TensorFlow ### Breaking Changes * The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure. ### Major Features and Improvements * `tf.lite` * Added 16-bit and 64-bit float type support for built-in op `cast`. * The Python TF Lite Interpreter | Low | 7/5/2023 |
| v2.13.0-rc2 | # Release 2.13.0 ## TensorFlow ### Breaking Changes * The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure. ### Major Features and Improvements * `tf.lite` * Added 16-bit and 64-bit float type support for built-in op `cast`. * The Python TF Lite Interpreter | Low | 6/22/2023 |
| v2.13.0-rc1 | # Release 2.13.0 ## TensorFlow ### Breaking Changes * The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure. ### Major Features and Improvements * `tf.lite` * Added 16-bit and 64-bit float type support for built-in op `cast`. * The Python TF Lite Interpreter | Low | 5/30/2023 |
| v2.13.0-rc0 | # Release 2.13.0 ## TensorFlow ### Breaking Changes * The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure. ### Major Features and Improvements * `tf.lite` * Add 16-bit and 64-bit float type support for built-in op `cast`. * The Python TF Lite Interpreter bi | Low | 5/9/2023 |
| v2.12.0 | # Release 2.12.0 ## TensorFlow ### Breaking Changes * Build, Compilation and Packaging * Removed redundant packages `tensorflow-gpu` and `tf-nightly-gpu`. These packages were removed and replaced with packages that direct users to switch to `tensorflow` or `tf-nightly` respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for m | Low | 3/23/2023 |
| v2.11.1 | # Release 2.11.1 **Note**: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin. * Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security | Low | 3/20/2023 |
| v2.12.0-rc1 | # Release 2.12.0 ## Breaking Changes * Build, Compilation and Packaging * Removal of redundant packages: the `tensorflow-gpu` and `tf-nightly-gpu` packages have been effectively removed and replaced with packages that direct users to switch to `tensorflow` or `tf-nightly` respectively. The naming difference was the only difference between the two sets of packages ever since TensorFlow 2.1, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorfl | Low | 3/7/2023 |
| v2.12.0-rc0 | # Release 2.12.0 ## Breaking Changes * Build, Compilation and Packaging * Removal of redundant packages: the `tensorflow-gpu` and `tf-nightly-gpu` packages have been effectively removed and replaced with packages that direct users to switch to `tensorflow` or `tf-nightly` respectively. The naming difference was the only difference between the two sets of packages ever since TensorFlow 2.1, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorfl | Low | 2/15/2023 |
| v2.11.0 | # Release 2.11.0 ## Breaking Changes * The `tf.keras.optimizers.Optimizer` base class now points to the new Keras optimizer, while the old optimizers have been moved to the `tf.keras.optimizers.legacy` namespace. If you find your workflow failing due to this change, you may be facing one of the following issues: * **Checkpoint loading failure.** The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/lo | Low | 11/18/2022 |
| v2.10.1 | # Release 2.10.1 This release introduces several vulnerability fixes: * Fixes an OOB seg fault in `DynamicStitch` due to missing validation ([CVE-2022-41883](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41883)) * Fixes an overflow in `tf.keras.losses.poisson` ([CVE-2022-41887](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41887)) * Fixes a heap OOB failure in `ThreadUnsafeUnigramCandidateSampler` caused by missing validation ([CVE-2022-41880](https://cve.mitre.or | Low | 11/16/2022 |
| v2.9.3 | # Release 2.9.3 This release introduces several vulnerability fixes: * Fixes an overflow in `tf.keras.losses.poisson` ([CVE-2022-41887](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41887)) * Fixes a heap OOB failure in `ThreadUnsafeUnigramCandidateSampler` caused by missing validation ([CVE-2022-41880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41880)) * Fixes a segfault in `ndarray_tensor_bridge` ([CVE-2022-41884](https://cve.mitre.org/cgi-bin/cvename.cgi?n | Low | 11/16/2022 |
| v2.8.4 | # Release 2.8.4 This release introduces several vulnerability fixes: * Fixes a heap OOB failure in `ThreadUnsafeUnigramCandidateSampler` caused by missing validation ([CVE-2022-41880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41880)) * Fixes a segfault in `ndarray_tensor_bridge` ([CVE-2022-41884](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41884)) * Fixes an overflow in `FusedResizeAndPadConv2D` ([CVE-2022-41885](https://cve.mitre.org/cgi-bin/cvename.cgi?n | Low | 11/16/2022 |
| v2.11.0-rc2 | # Release 2.11.0 ## Breaking Changes * `tf.keras.optimizers.Optimizer` now points to the new Keras optimizer, and old optimizers have moved to the `tf.keras.optimizers.legacy` namespace. If you find your workflow failing due to this change, you may be facing one of the following issues: * **Checkpoint loading failure.** The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/loading, but at the cost of brea | Low | 11/2/2022 |
| v2.11.0-rc1 | # Release 2.11.0 ## Breaking Changes * `tf.keras.optimizers.Optimizer` now points to the new Keras optimizer, and old optimizers have moved to the `tf.keras.optimizers.legacy` namespace. If you find your workflow failing due to this change, you may be facing one of the following issues: * **Checkpoint loading failure.** The new optimizer handles optimizer state differently from the old optimizer, which simplies the logic of checkpoint saving/loading, but at the cost of | Low | 10/19/2022 |
| v2.11.0-rc0 | # Release 2.11.0 ## Breaking Changes * `tf.keras.optimizers.Optimizer` now points to the new Keras optimizer, and old optimizers have moved to the `tf.keras.optimizers.legacy` namespace. If you find your workflow failing due to this change, you may be facing one of the following issues: * **Checkpoint loading failure.** The new optimizer handles optimizer state differently from the old optimizer, which simplies the logic of checkpoint saving/loading, but at the cost of | Low | 10/18/2022 |
| v2.10.0 | # Release 2.10.0 ## Breaking Changes * Causal attention in `keras.layers.Attention` and `keras.layers.AdditiveAttention` is now specified in the `call()` method via the `use_causal_mask` argument (rather than in the constructor), for consistency with other layers. * Some files in `tensorflow/python/training` have been moved to `tensorflow/python/tracking` and `tensorflow/python/checkpoint`. Please update your imports accordingly, the old files will be removed in Release 2.11. * `tf | Low | 9/6/2022 |
| v2.9.2 | # Release 2.9.2 This releases introduces several vulnerability fixes: * Fixes a `CHECK` failure in tf.reshape caused by overflows ([CVE-2022-35934](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35934)) * Fixes a `CHECK` failure in `SobolSample` caused by missing validation ([CVE-2022-35935](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35935)) * Fixes an OOB read in `Gather_nd` op in TF Lite ([CVE-2022-35937](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-20 | Low | 9/3/2022 |
| v2.8.3 | # Release 2.8.3 This releases introduces several vulnerability fixes: * Fixes a `CHECK` failure in tf.reshape caused by overflows ([CVE-2022-35934](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35934)) * Fixes a `CHECK` failure in `SobolSample` caused by missing validation ([CVE-2022-35935](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35935)) * Fixes an OOB read in `Gather_nd` op in TF Lite ([CVE-2022-35937](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022 | Low | 9/2/2022 |
| v2.7.4 | # Release 2.7.4 **Note**: This is the last release in the 2.7.x series This releases introduces several vulnerability fixes: * Fixes a `CHECK` failure in tf.reshape caused by overflows ([CVE-2022-35934](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35934)) * Fixes a `CHECK` failure in `SobolSample` caused by missing validation ([CVE-2022-35935](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35935)) * Fixes an OOB read in `Gather_nd` op in TF Lite ([CVE-2022-35 | Low | 9/2/2022 |
| v2.10.0-rc3 | # Release 2.10.0 ## Breaking Changes * Causal attention in `keras.layers.Attention` and `keras.layers.AdditiveAttention` is now specified in the `call()` method via the `use_causal_mask` argument (rather than in the constructor), for consistency with other layers. * Some files in `tensorflow/python/training` have been moved to `tensorflow/python/tracking` and `tensorflow/python/checkpoint`. Please update your imports accordingly, the old files will be removed in Release 2.11. * `tf | Low | 8/29/2022 |
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