|author||Xin Li <email@example.com>||Wed Oct 30 11:48:50 2019 -0700|
|committer||Xin Li <firstname.lastname@example.org>||Wed Oct 30 11:48:50 2019 -0700|
DO NOT MERGE - qt-qpr1-dev-plus-aosp-without-vendor@5915889 into stage-aosp-master Bug: 142003500 Change-Id: If787877ddc34c1ee4bae5c407516467d7d07ed98
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@example.com.
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 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 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.
>>> import tensorflow as tf >>> tf.enable_eager_execution() >>> 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 the tutorials page of tensorflow.org.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
|Raspberry Pi 0 and 1||Py2 Py3|
|Raspberry Pi 2 and 3||Py2 Py3|
|Linux s390x Nightly||Nightly|
|Linux s390x CPU Stable Release||Release|
|Linux ppc64le CPU Nightly||Nightly|
|Linux ppc64le CPU Stable Release||Release|
|Linux ppc64le GPU Nightly||Nightly|
|Linux ppc64le GPU Stable Release||Release|
|Linux CPU with Intel® MKL-DNN Nightly||Nightly|
|Linux CPU with Intel® MKL-DNN |
Supports Python 2.7, 3.4, 3.5, and 3.6
|Red Hat® Enterprise Linux® 7.6 CPU & GPU |
Python 2.7, 3.6
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.