MKL Version

This version implements:

1. MKL backend for Eigen
2. 1DFFT of tensors using MKL FFTW wrapper

You must install MKL development libraries - e.g. http://registrationcenter-download.intel.com/akdlm/irc_nas/tec/15816/l_mkl_2019.5.281.tgz Make sure to install and export MKL environment variables on by adding the following line at bashrc:

**`Documentation`** |
------------------- |
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |

[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 to
conduct 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](https://www.tensorflow.org/api_docs/python)
and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as
non-guaranteed backward compatible API for
[other languages](https://www.tensorflow.org/api_docs).

Keep up-to-date with release announcements and security updates by subscribing
to
[[email protected]](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).
See all the [mailing lists](https://www.tensorflow.org/community/forums).

## 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

Use the GPU package for
[CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu) *(Ubuntu and
Windows)*:

$ pip install tensorflow-gpu

*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) 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 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:

Continuous build status

Official Builds

Build Type	Status	Artifacts
Linux CPU		PyPI
Linux GPU		PyPI
Linux XLA		TBA
macOS		PyPI
Windows CPU		PyPI
Windows GPU		PyPI
Android
Raspberry Pi 0 and 1		Py2 Py3
Raspberry Pi 2 and 3		Py2 Py3

Community Supported Builds

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

Resources

• TensorFlow.org
• TensorFlow tutorials
• TensorFlow official models
• TensorFlow examples
• TensorFlow in Practice from Coursera
• Intro to TensorFlow for Deep Learning from Udacity
• Introduction to TensorFlow Lite from Udacity
• TensorFlow blog
• TensorFlow Twitter
• TensorFlow YouTube
• TensorFlow roadmap
• TensorFlow white papers
• TensorBoard visualization toolkit

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0