tensorflow-upstream

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Published: Dec 21, 2019 License: Apache-2.0

README

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 on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting 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 backwards compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

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 for CPU-only:

$ pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow-gpu

Nightly binaries are available for testing using the tf-nightly and tf-nightly-gpu 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()
'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:

CII Best Practices Contributor Covenant

Continuous build status

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 Status Py2 Py3
Raspberry Pi 2 and 3 Status Status Py2 Py3

Community Supported Builds

Build Type Status Artifacts
Linux AMD ROCm GPU Nightly Build Status Nightly
Linux AMD ROCm GPU Stable Release Build Status Release 1.15 / 2.x
Linux s390x Nightly Build Status Nightly
Linux s390x CPU Stable Release Build Status Release
Linux ppc64le CPU Nightly Build Status Nightly
Linux ppc64le CPU Stable Release Build Status Release
Linux ppc64le GPU Nightly Build Status Nightly
Linux ppc64le GPU Stable Release Build Status Release
Linux CPU with Intel® MKL-DNN Nightly Build Status Nightly
Linux CPU with Intel® MKL-DNN
Supports Python 2.7, 3.4, 3.5, 3.6 and 3.7
Build Status 1.14.0 pypi
Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6
Build Status 1.13.1 pypi

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0

Directories

Path Synopsis
tensorflow
go
Package tensorflow is a Go binding to TensorFlow.
Package tensorflow is a Go binding to TensorFlow.
go/genop
Command genop generates a Go source file with functions for TensorFlow ops.
Command genop generates a Go source file with functions for TensorFlow ops.
go/genop/internal
Package internal generates Go source code with functions for TensorFlow operations.
Package internal generates Go source code with functions for TensorFlow operations.
go/op
Package op defines functions for adding TensorFlow operations to a Graph.
Package op defines functions for adding TensorFlow operations to a Graph.

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