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.
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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.
pip install tf-nightlyor
pip install tf-nightly-gpuin 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
>>> import tensorflow as tf >>> tf.enable_eager_execution() >>> tf.add(1, 2) 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:
Continuous build status
|Raspberry Pi 0 and 1||Py2 Py3|
|Raspberry Pi 2 and 3||Py2 Py3|
Community Supported Builds
|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
For more information
- TensorFlow Website
- TensorFlow Tutorials
- TensorFlow Model Zoo
- TensorFlow Twitter
- TensorFlow Blog
- TensorFlow Course at Stanford
- TensorFlow Roadmap
- TensorFlow White Papers
- TensorFlow YouTube Channel
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.