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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.
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 thefunction_type
property oftf.function’s
andConcreteFunctions
. See thetf.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 theinference_fn
property ofConcreteFunctions
. (Does not support gradients.) See thetf.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.
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