Skip to content

Latest commit

 

History

History
206 lines (163 loc) · 12.9 KB

README.md

File metadata and controls

206 lines (163 loc) · 12.9 KB
Raw

Python PyPI DOI CII Best Practices OpenSSF Scorecard Fuzzing Status Fuzzing Status OSSRank Contributor Covenant TF Official Continuous TF Official Nightly

Documentation
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 within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the framework is versatile enough to be used in other areas as well.

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

Keep up-to-date with release announcements and security updates by subscribing to [email protected]. See all the mailing lists.

Tensorflow ROCm port

Please follow the instructions here to set up your ROCm stack. A docker container: rocm/tensorflow:latest(https://hub.docker.com/r/rocm/tensorflow/) is readily available to be used:

alias drun='sudo docker run \
      -it \
      --network=host \
      --device=/dev/kfd \
      --device=/dev/dri \
      --ipc=host \
      --shm-size 16G \
      --group-add video \
      --cap-add=SYS_PTRACE \
      --security-opt seccomp=unconfined \
      -v $HOME/dockerx:/dockerx'

drun rocm/tensorflow:latest

We maintain tensorflow-rocm whl packages on PyPI here, to install tensorflow-rocm package using pip:

# Install some ROCm dependencies
sudo apt install rocm-libs rccl

# Pip3 install the whl package from PyPI
pip3 install --user tensorflow-rocm --upgrade

For details on Tensorflow ROCm port, please take a look at the ROCm-specific README file.

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

Other devices (DirectX and MacOS-metal) are supported using Device plugins.

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 Forum 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.

Patching guidelines

Follow these steps to patch a specific version of TensorFlow, for example, to apply fixes to bugs or security vulnerabilities:

  • Clone the TensorFlow repo and switch to the corresponding branch for your desired TensorFlow version, for example, branch r2.8 for version 2.8.
  • Apply (that is, cherry-pick) the desired changes and resolve any code conflicts.
  • Run TensorFlow tests and ensure they pass.
  • Build the TensorFlow pip package from source.

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 Status PyPI
Linux GPU Status PyPI
Linux XLA Status TBA
macOS Status PyPI
Windows CPU Status PyPI
Windows GPU Status PyPI
Android Status Download
Raspberry Pi 0 and 1 Status Py3
Raspberry Pi 2 and 3 Status 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

Learn more about the TensorFlow community and how to contribute.

Courses

License

Apache License 2.0