cover.jpg

github.com-tensorflow-tensorflow_-_2019-02-03_11-42-36

by: tensorflow

Publication date: 2015-11-07

Topics: GitHub, code, software, git

Download the bundle tensorflow-tensorflow_-_2019-02-03_11-42-36.bundle and run:

 git clone tensorflow-tensorflow_-_2019-02-03_11-42-36.bundle -b master

An Open Source Machine Learning Framework for Everyone

| Documentation ||-----------------|| |

TensorFlow is an open source software library for numerical computationusing data flow graphs. The graph nodes represent mathematical operations, whilethe graph edges represent the multidimensional data arrays (tensors) that flowbetween them. This flexible architecture enables you to deploy computation toone or more CPUs or GPUs in a desktop, server, or mobile device withoutrewriting code. TensorFlow also includesTensorBoard, a data visualizationtoolkit.

TensorFlow was originally developed by researchers and engineersworking on the Google Brain team within Google's Machine Intelligence Researchorganization for the purposes of conducting machine learning and deep neuralnetworks research. The system is general enough to be applicable in a widevariety of other domains, as well.

TensorFlow provides stable Python and C APIs as well as non-guaranteed backwardscompatible API's for C++, Go, Java, JavaScript and Swift.

Keep up to date with release announcements and security updates bysubscribing toannounce@tensorflow.org.

Installation

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 detailedinstructions, 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 packagesunder the tf-nightly andtf-nightly-gpu project on pypi.Simply run pip install tf-nightly or pip install tf-nightly-gpu in a cleanenvironment to install the nightly TensorFlow build. We support CPU and GPUpackages on Linux, Mac, and Windows.

Try your first TensorFlow program

shell$ python

```python

import tensorflow as tf tf.enableeagerexecution() tf.add(1, 2).numpy() 3 hello = tf.constant('Hello, TensorFlow!') hello.numpy() 'Hello, TensorFlow!' ```

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

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contributionguidelines. This project adheres to TensorFlow'scode of conduct. By participating, you are expected touphold this code.

We use GitHub issues fortracking requests and bugs, so please seeTensorFlow Discussfor general questions and discussion, and please direct specific questions toStack 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----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------IBM s390x | | TBALinux ppc64le CPU Nightly | | NightlyLinux ppc64le CPU Stable Release | | ReleaseLinux ppc64le GPU Nightly | | NightlyLinux ppc64le GPU Stable Release | | ReleaseLinux CPU with Intel® MKL-DNN Nightly | | NightlyLinux 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 | | 1.12.0 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 tensorflow.org for a few ways to participate.

License

Apache License 2.0

Source: https://github.com/tensorflow/tensorflow
Uploader: tensorflow
Upload date: 2015-11-07