tensorflow 2.9.1 for Nvidia GPU on macOS

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As officially Tensorflow doesn't support for macOS cuda, I used this repository to build tensorflow 2.8+ on macOS cuda. This branch v2.9.1-fixed branch is the current investigation branch. Though TomHeaven's Tensorflow OSX Build didn't support TF 1.15, 2.0.0, 2.1.0 and 2.2.0 as well as 2.4.0. After checkup with him via ticket 25 on tensorflow-osx-build, knowing that he won't continue to crack for higher version, I decided to try on my own similar to pytorch-macOS-cuda, nccl-osx, as well as jax-macOS-cuda.

The main development environment settings as follow:

• macOS 10.13.6, cuda 10.1, cudnn 7.6.5 (cuda and cudnn is the last official version which Nvidia released to support macOS)
• NCCL on macOS 2.9.6.1 and test suite

Consolidating tensorflow-2.9.0-mac.patch by

git format-patch -17 --stdout > tensorflow-2.9.0-mac.patch

from tensorflow branch https://github.com/llv22/tensorflow-macOS-cuda/tree/v2.9.0-fixed

Consolidating tf2.9.0_to_2.9.1.patch by

git diff v2.9.0..v2.9.1 > tf2.9.0_to_2.9.1.patch

apply those two patches and building via the cracked bazel 5.2.1

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Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

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Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

Continuous build status

You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.

Official Builds

Build Type	Status	Artifacts
Linux CPU		PyPI
Linux GPU		PyPI
Linux XLA		TBA
macOS		PyPI
Windows CPU		PyPI
Windows GPU		PyPI
Android		Download
Raspberry Pi 0 and 1		Py3
Raspberry Pi 2 and 3		Py3
Libtensorflow MacOS CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Linux CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Linux GPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Windows CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Windows GPU	Status Temporarily Unavailable	Nightly Binary Official GCS

Resources

• TensorFlow.org
• TensorFlow Tutorials
• TensorFlow Official Models
• TensorFlow Examples
• DeepLearning.AI TensorFlow Developer Professional Certificate
• TensorFlow: Data and Deployment from Coursera
• Getting Started with TensorFlow 2 from Coursera
• TensorFlow: Advanced Techniques from Coursera
• TensorFlow 2 for Deep Learning Specialization from Coursera
• Intro to TensorFlow for A.I, M.L, and D.L from Coursera
• Intro to TensorFlow for Deep Learning from Udacity
• Introduction to TensorFlow Lite from Udacity
• Machine Learning with TensorFlow on GCP
• TensorFlow Codelabs
• TensorFlow Blog
• Learn ML with TensorFlow
• TensorFlow Twitter
• TensorFlow YouTube
• TensorFlow model optimization roadmap
• TensorFlow White Papers
• TensorBoard Visualization Toolkit

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0