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An Open Source Machine Learning Framework for Everyone


Documentation |——————- |Documentation |

TensorFlow is an end-to-end open source platformfor machine learning. It has a comprehensive, flexible ecosystem oftools,libraries, andcommunity resources that letsresearchers push the state-of-the-art in ML and developers easily build anddeploy ML powered applications.

TensorFlow was originally developed by researchers and engineers working on theGoogle Brain team within Google’s Machine Intelligence Research organization forthe purposes of conducting machine learning and deep neural networks research.The system is general enough to be applicable in a wide variety of otherdomains, as well.

TensorFlow provides stable Pythonand C++ APIs, as well asnon-guaranteed backwards compatible API forother languages.

Keep up-to-date with release announcements and security updates by all the mailing lists.


See the TensorFlow install guide for thepip package, toenable GPU support, use aDocker container, andbuild from source.

To install the current release for CPU-only:

$ pip install tensorflow

Use the GPU package forCUDA-enabled GPU cards (Ubuntu andWindows):

$ pip install tensorflow-gpu

Nightly binaries are available for testing using thetf-nightly andtf-nightly-gpu packages on PyPi.

Try your first TensorFlow program

shell$ python


import tensorflow as tf tf.add(1, 2).numpy() 3 hello = tf.constant(‘Hello, TensorFlow!’) hello.numpy() ‘Hello, TensorFlow!’ “`

For more examples, see theTensorFlow tutorials.

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review thecontribution guidelines. This project adheres to TensorFlow’scode of conduct. By participating, you are expected touphold this code.

We use GitHub issues fortracking requests and bugs, please seeTensorFlow Discussfor general questions and discussion, and please direct specific questions toStack Overflow.

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

CII Best PracticesContributor Covenant

Continuous build status

Official Builds

Build Type | Status | Artifacts———————— | ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— | ———Linux CPU | Status | pypiLinux GPU | Status | pypiLinux XLA | Status | TBAMacOS | Status | pypiWindows CPU | Status | pypiWindows GPU | Status | pypiAndroid | Status | DownloadRaspberry Pi 0 and 1 | Status Status | Py2 Py3Raspberry Pi 2 and 3 | Status Status | Py2 Py3

Community Supported Builds

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


Learn more about theTensorFlow community and how tocontribute.


Apache License 2.0

To restore the repository download the bundle


and run:

 git clone tensorflow-tensorflow_-_2019-11-09_09-32-36.bundle 

Uploader: tensorflow
Upload date: 2019-11-09