What’s new in TensorFlow 2.15

by
Tensorflow

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. This capability can be accessed through the inference_fn property of ConcreteFunctions. (Does not support gradients.) See the tf.types.experimental.AtomicFunction documentation for how to call and use it.

Upgrade to Clang 17.0.1 and CUDA 12.2

TensorFlow PIP packages are now being built with Clang 17 and CUDA 12.2 to improve performance for NVIDIA Hopper-based GPUs. Moving forward, Clang 17 will be the default C++ compiler for TensorFlow. We recommend upgrading your compiler to Clang 17 when building TensorFlow from source.

Read More