tensorflow-intel2.18.0
Published
TensorFlow is an open source machine learning framework for everyone.
pip install tensorflow-intel
Package Downloads
Authors
Requires Python
>=3.9
Dependencies
- absl-py
>=1.0.0 - astunparse
>=1.6.0 - flatbuffers
>=24.3.25 - gast
!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 - google-pasta
>=0.1.1 - libclang
>=13.0.0 - opt-einsum
>=2.3.2 - packaging
- protobuf
!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 - requests
<3,>=2.21.0 - setuptools
- six
>=1.12.0 - termcolor
>=1.1.0 - typing-extensions
>=3.6.6 - wrapt
>=1.11.0 - grpcio
<2.0,>=1.24.3 - tensorboard
<2.19,>=2.18 - keras
>=3.5.0 - numpy
<2.1.0,>=1.26.0 - h5py
>=3.11.0 - ml-dtypes
<0.5.0,>=0.4.0 - tensorflow-io-gcs-filesystem
>=0.23.1; python_version < "3.12" - nvidia-cublas-cu12
==12.5.3.2; extra == "and-cuda" - nvidia-cuda-cupti-cu12
==12.5.82; extra == "and-cuda" - nvidia-cuda-nvcc-cu12
==12.5.82; extra == "and-cuda" - nvidia-cuda-nvrtc-cu12
==12.5.82; extra == "and-cuda" - nvidia-cuda-runtime-cu12
==12.5.82; extra == "and-cuda" - nvidia-cudnn-cu12
==9.3.0.75; extra == "and-cuda" - nvidia-cufft-cu12
==11.2.3.61; extra == "and-cuda" - nvidia-curand-cu12
==10.3.6.82; extra == "and-cuda" - nvidia-cusolver-cu12
==11.6.3.83; extra == "and-cuda" - nvidia-cusparse-cu12
==12.5.1.3; extra == "and-cuda" - nvidia-nccl-cu12
==2.21.5; extra == "and-cuda" - nvidia-nvjitlink-cu12
==12.5.82; extra == "and-cuda"
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, and from desktops to clusters of servers to mobile and edge devices.
Originally developed by researchers and engineers from the Google Brain team (https://research.google/teams/brain/) within Google's AI organization, TensorFlow comes with strong support for machine learning and deep learning with a flexible numerical computation core that can be used across many other scientific domains. TensorFlow is licensed under Apache 2.0.
This optimized version of TensorFlow for Windows OS has been produced by Intel. In order to take full advantage of Intel architecture and to extract maximum performance, the TensorFlow framework has been optimized using oneAPI Deep Neural Network Library (oneDNN) primitives, a popular performance library for deep learning applications. For more information on the optimizations as well as performance data, see this blog post Faster AI Inference with Intel Optimization for TensorFlow. To activate oneDNN, execute the following in the Windows command prompt: set TF_ENABLE_ONEDNN_OPTS=1 before running your AI/ML workload.