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<img src="https://www.tensorflow.org/images/tf_logo_social.png">
</div>
-----------------
| **`Documentation`** |
|-----------------|
| [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |
**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](https://github.com/tensorflow/tensorboard), a data visualization
toolkit.
[TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem of
[tools](https://www.tensorflow.org/resources/tools),
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/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 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's for C++, Go, Java, JavaScript, and Swift.
TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)
and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as
non-guaranteed backwards compatible API for
[other languages](https://www.tensorflow.org/api_docs).
Keep up to date with release announcements and security updates by
subscribing to
Keep up-to-date with release announcements and security updates by subscribing
to
[announce@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).
See all the [mailing lists](https://www.tensorflow.org/community/forums).
## Installation
## Install
See the [TensorFlow install guide](https://www.tensorflow.org/install) for the
[pip package](https://www.tensorflow.org/install/pip), to
[enable GPU support](https://www.tensorflow.org/install/gpu), use a
[Docker container](https://www.tensorflow.org/install/docker), and
[build from source](https://www.tensorflow.org/install/source).
To install the current release for CPU-only:
```
pip install tensorflow
$ pip install tensorflow
```
Use the GPU package for CUDA-enabled GPU cards:
Use the GPU package for
[CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu):
```
pip install tensorflow-gpu
$ pip install tensorflow-gpu
```
*See [Installing TensorFlow](https://www.tensorflow.org/install) 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
*Nightly binaries are available for testing using the
[tf-nightly](https://pypi.python.org/pypi/tf-nightly) and
[tf-nightly-gpu](https://pypi.python.org/pypi/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.
[tf-nightly-gpu](https://pypi.python.org/pypi/tf-nightly-gpu) packages on PyPi.*
#### *Try your first TensorFlow program*
@ -74,8 +71,8 @@ $ python
'Hello, TensorFlow!'
```
Learn more examples about how to do specific tasks in TensorFlow at the
[tutorials page of tensorflow.org](https://www.tensorflow.org/tutorials/).
For more examples, see the
[TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
## Contribution guidelines
@ -126,20 +123,23 @@ Build Type
**Linux CPU with Intel® MKL-DNN** <br> **Supports Python 2.7, 3.4, 3.5, and 3.6** | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/lastStableBuild) | [1.13.1 pypi](https://pypi.org/project/intel-tensorflow/)
**Red Hat® Enterprise Linux® 7.6 CPU & GPU** <br> Python 2.7, 3.6 | [![Build Status](https://jenkins-tensorflow.apps.ci.centos.org/buildStatus/icon?job=tensorflow-rhel7-3.6&build=2)](https://jenkins-tensorflow.apps.ci.centos.org/job/tensorflow-rhel7-3.6/2/) | [1.13.1 pypi](https://tensorflow.pypi.thoth-station.ninja/index/)
## For more information
## Resources
* [TensorFlow Website](https://www.tensorflow.org)
* [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
* [TensorFlow Model Zoo](https://github.com/tensorflow/models)
* [TensorFlow.org](https://www.tensorflow.org)
* [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)
* [TensorFlow official models](https://github.com/tensorflow/models/tree/master/official)
* [TensorFlow examples](https://github.com/tensorflow/examples)
* [TensorFlow in Practice from Coursera](https://www.coursera.org/specializations/tensorflow-in-practice)
* [TensorFlow blog](https://blog.tensorflow.org)
* [TensorFlow Twitter](https://twitter.com/tensorflow)
* [TensorFlow Blog](https://blog.tensorflow.org)
* [TensorFlow Course at Stanford](https://web.stanford.edu/class/cs20si)
* [TensorFlow Roadmap](https://www.tensorflow.org/community/roadmap)
* [TensorFlow White Papers](https://www.tensorflow.org/about/bib)
* [TensorFlow YouTube Channel](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ)
* [TensorFlow Visualization Toolkit](https://github.com/tensorflow/tensorboard)
* [TensorFlow YouTube](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ)
* [TensorFlow roadmap](https://www.tensorflow.org/community/roadmap)
* [TensorFlow white papers](https://www.tensorflow.org/about/bib)
* [TensorBoard visualization toolkit](https://github.com/tensorflow/tensorboard)
Learn more about the TensorFlow community at the [community page of tensorflow.org](https://www.tensorflow.org/community) for a few ways to participate.
Learn more about the
[TensorFlow community](https://www.tensorflow.org/community) and how to
[contribute](https://www.tensorflow.org/community/contribute).
## License