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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.

Keep up to date with release announcements and security updates by subscribing to [email protected].

Tensorflow ROCm port This project is based on TensorFlow 1.8.0. It has been verified to work with the latest ROCm1.8.2 release. Please follow the instructions here to set up your ROCm stack.

A docker container: rocm/tensorflow:rocm1.7.1(https://hub.docker.com/r/rocm/tensorflow/) is readily available to be used.

The Wheels:

• Python 2: http://repo.radeon.com/rocm/misc/tensorflow/tensorflow-1.8.0-cp27-cp27mu-manylinux1_x86_64.whl
• Python 3: http://repo.radeon.com/rocm/misc/tensorflow/tensorflow-1.8.0-cp35-cp35m-manylinux1_x86_64.whl

For details on Tensorflow ROCm port, please take a look at the ROCm-specific README file.

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®		TBA

For more information

• TensorFlow Website
• TensorFlow White Papers
• TensorFlow YouTube Channel
• TensorFlow Model Zoo
• TensorFlow MOOC on Udacity
• TensorFlow Course at Stanford

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

License

Apache License 2.0