Clone this repo:
  1. 2283fef compat: Update forward compatibility horizon to 2019-01-23 by A. Unique TensorFlower · 3 hours ago master
  2. d072d4d Add support for bidirectional_sequence_rnn for tf.nn.static_bidirectional_rnn case by A. Unique TensorFlower · 3 hours ago
  3. 7cfe43a Fix typo. by A. Unique TensorFlower · 4 hours ago
  4. 76aa6cf [TF:XLA] Fix the AR/CRS combiner to avoid crashing when two cross-module AllReduces lead to the same cross-replica AllReduce. by Dimitris Vardoulakis · 5 hours ago
  5. bba3f6a Refuse a v2 argument to tf_export, since that is always user error. by Martin Wicke · 6 hours ago


TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

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 API and C APIs as well as without API backwards compatibility guarantee like C++, Go, Java, JavaScript and Swift.

Keep up to date with release announcements and security updates by subscribing to


To install the current release for CPU-only:

pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards:

pip install tensorflow-gpu

See Installing TensorFlow for detailed instructions, and how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Nightly pip packages

  • We are pleased to announce that TensorFlow now offers nightly pip packages under the tf-nightly and tf-nightly-gpu project on pypi. Simply run pip install tf-nightly or pip install tf-nightly-gpu in a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.

Try your first TensorFlow program

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

Learn more examples about how to do specific tasks in TensorFlow at the tutorials page of

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, so 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

Continuous build status

Official Builds

Build TypeStatusArtifacts
Linux CPUStatuspypi
Linux GPUStatuspypi
Linux XLAStatusTBA
Windows CPUStatuspypi
Windows GPUStatuspypi
Raspberry Pi 0 and 1Status StatusPy2 Py3
Raspberry Pi 2 and 3Status StatusPy2 Py3

Community Supported Builds

Build TypeStatusArtifacts
IBM s390xBuild StatusTBA
Linux ppc64le CPU NightlyBuild StatusNightly
Linux ppc64le CPU Stable ReleaseBuild StatusRelease
Linux ppc64le GPU NightlyBuild StatusNightly
Linux ppc64le GPU Stable ReleaseBuild StatusRelease
Linux CPU with Intel® MKL-DNN NightlyBuild StatusNightly
Linux CPU with Intel® MKL-DNN Python 2.7
Linux CPU with Intel® MKL-DNN Python 3.4
Linux CPU with Intel® MKL-DNN Python 3.5
Linux CPU with Intel® MKL-DNN Python 3.6
Build Status1.12.0 py2.7
1.12.0 py3.4
1.12.0 py3.5
1.12.0 py3.6

For more information

Learn more about the TensorFlow community at the community page of for a few ways to participate.


Apache License 2.0