tramphero/tensorflow

`Documentation`

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
announce@tensorflow.org.

Installation

See Installing TensorFlow for instructions on how to install our release binaries or 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
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> sess.run(hello)
'Hello, TensorFlow!'
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> sess.run(a + b)
42
>>> sess.close()

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

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:

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

Community Supported Builds

Build Type	Status	Artifacts
IBM s390x		TBA
IBM ppc64le CPU		TBA
IBM ppc64le GPU		TBA
Linux CPU with Intel® MKL-DNN Nightly		Nightly
Linux CPU with Intel® MKL-DNN Python 2.7 Linux CPU with Intel® MKL-DNN Python 3.5 Linux CPU with Intel® MKL-DNN Python 3.6		1.9.0 py2.7 1.9.0 py3.5 1.9.0 py3.6