# What's new in TensorFlow 2.15

DevFeed: [What's new in TensorFlow 2.15](<https://devfeed.tech/articles/what-s-new-in-tensorflow-2-15-7401.md>)

Original publisher: [Read original article](<https://blog.tensorflow.org/2023/11/whats-new-in-tensorflow-2-15.html>)

Author: TensorFlow Blog (noreply@blogger.com)

Published: 2023-11-17T18:55:00Z

Content type: release

Language: en

Sources: [The TensorFlow Blog](<https://devfeed.tech/sources/the-tensorflow-blog.md>)

Topics: [TensorFlow Core](<https://devfeed.tech/topics/tensorflow-core.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [pip](<https://devfeed.tech/topics/pip.md>), [Windows](<https://devfeed.tech/topics/windows.md>), [Compiler](<https://devfeed.tech/topics/compiler.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [compiler](<https://devfeed.tech/tags/compiler.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [explore](<https://devfeed.tech/tags/explore.md>), [installation](<https://devfeed.tech/tags/installation.md>), [keras](<https://devfeed.tech/tags/keras.md>), [linux](<https://devfeed.tech/tags/linux.md>), [nvdia](<https://devfeed.tech/tags/nvdia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [release](<https://devfeed.tech/tags/release.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tensorflow-core](<https://devfeed.tech/tags/tensorflow-core.md>), [windows](<https://devfeed.tech/tags/windows.md>)

## 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.