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Summaries are not the original full articles. ## TensorFlow 2.15 update: hot-fix for Linux installation issue DevFeed: [TensorFlow 2.15 update: hot-fix for Linux installation issue]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-12-05T22:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [pip](), [Linux](), [TensorRT](), [CUDA](), [Python]() Tags: [announcement](), [dependencies](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [nvidia](), [packages](), [release](), [tensorflow](), [tensorflow-core](), [tensorrt](), [update](), [x86-64]() ### AI overview TensorFlow released version 2.15.0.post1 to fix Linux x86_64 installation problems caused by missing TensorRT-related Python dependencies when installing TensorFlow 2.15 with NVIDIA CUDA dependencies via pip. The hotfix restores the intended installation behavior, while users pinning versions must specify 2.15.0.post1 explicitly. ### Source excerpt Posted by the TensorFlow team We are releasing a hot-fix for an installation issue affecting the TensorFlow installation process. The TensorFlow 2.15.0 Python package was released such that it requested tensorrt-related packages that cannot be found unless the user installs them beforehand or provides additional installation flags. This dependency affected anyone installing TensorFlow 2.15 alongside NVIDIA CUDA dependencies via pip install tensorflow[and-cuda]. Depending on the installation method, TensorFlow 2.14 would be installed instead of 2.15, or users could receive an installation error due to those missing dependencies. To solve this issue as quickly as possible, we have released TensorFlow 2.15.0.post1 for the Linux x86_64 platform. This version removes the tensorrt Python package dependencies from the tensorflow[and-cuda] installation method. Support for TensorRT is otherwise unaffected as long as TensorRT is already installed on the system. Now, pip install tensorflow[and-cuda] works as originally intended for TensorFlow 2.15. Using .post1 instead of a full minor release allowed us to push this release out quickly. However, please be aware of the following caveat: for users wishing to pin their Python dependency in a requirements file or other situation, under Python's version specification rules, tensorflow[and-cuda]==2.15.0 will not install this fixed version. Please use ==2.15.0.post1 to specify this exact version on Linux platforms, or a fuzzy version specification, such as ==2.15.*, to specify the most recent compatible version of TensorFlow 2.15 on all platforms. ## What's new in TensorFlow 2.15 DevFeed: [What's new in TensorFlow 2.15]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-11-17T18:55:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [CUDA](), [cpu](), [Linux](), [pip](), [Windows](), [Compiler]() Tags: [announcement](), [compiler](), [cpu](), [cuda](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [performance](), [release](), [tensorflow](), [tensorflow-core](), [windows]() ### AI overview TensorFlow 2.15 introduces simpler optional installation of NVIDIA CUDA libraries through pip on Linux, provided the NVIDIA driver is already installed. It also enables oneDNN CPU optimizations by default for Windows x64 and x86 packages, adds full tf.function type support, upgrades CUDA to 12.2, and uses Clang 17 for package builds and source compilation. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.15 has been released! Highlights of this release (and 2.14) include a much simpler installation method for NVIDIA CUDA libraries for Linux, oneDNN CPU performance optimizations for Windows x64 and x86, full availability of tf.function types, an upgrade to Clang 17.0.1, and much more! For the full release note, please check here. Note: Release updates on the new multi-backend Keras will be published on keras.io starting with Keras 3.0. For more information, please check here. TensorFlow Core NVIDIA CUDA libraries for Linux The tensorflow pip package has a new, optional installation method for Linux that installs necessary NVIDIA CUDA libraries through pip. As long as the NVIDIA driver is already installed on the system, you may now run pip install tensorflow[and-cuda] to install TensorFlow's NVIDIA CUDA library dependencies in the Python environment. Aside from the NVIDIA driver, no other pre-existing NVIDIA CUDA packages are necessary. In TensorFlow 2.15, CUDA has been upgraded to version 12.2. oneDNN CPU performance optimizations For Windows x64 & x86 packages, oneDNN optimizations are now enabled by default on X86 CPUs. These optimizations can be enabled or disabled by setting the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, simply unset the environment variable. tf.function tf.function types are now fully available. tf.types.experimental.TraceType now allows custom tf.function inputs to declare Tensor decomposition and type casting support. Introducing tf.types.experimental.FunctionType as the comprehensive representation of the signature of tf.function callables. It can be accessed through the function_type property of tf.function's and ConcreteFunctions. See the tf.types.experimental.FunctionType documentation for more details. Introducing tf.types.experimental.AtomicFunction as the fastest way to perform TF computations in Python.