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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the framework is versatile enough to be used in other areas as well. TensorFlow provides stable Python and C++ APIs, as well as a non-guaranteed backward compatible API for other languages. Keep up-to-date with release announcements and security updates by subscribing to [email protected]. See all the mailing lists. InstallSee the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source. To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows): $ pip install tensorflowOther devices (DirectX and MacOS-metal) are supported using Device plugins. A smaller CPU-only package is also available: $ pip install tensorflow-cpuTo update TensorFlow to the latest version, add --upgrade flag to the above commands. Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi. Try your first TensorFlow program $ python >>> import tensorflow as tf >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() b'Hello, TensorFlow!'For more examples, see the TensorFlow tutorials. Contribution guidelinesIf 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, please see TensorFlow Forum 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. Patching guidelinesFollow these steps to patch a specific version of TensorFlow, for example, to apply fixes to bugs or security vulnerabilities: Clone the TensorFlow repo and switch to the corresponding branch for your desired TensorFlow version, for example, branch r2.8 for version 2.8. Apply (that is, cherry-pick) the desired changes and resolve any code conflicts. Run TensorFlow tests and ensure they pass. Build the TensorFlow pip package from source. Continuous build statusYou can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table. Official Builds Build Type Status Artifacts Linux CPU
PyPI
Linux GPU
PyPI
Linux XLA
TBA
macOS
PyPI
Windows CPU
PyPI
Windows GPU
PyPI
Android
Download
Raspberry Pi 0 and 1
Py3
Raspberry Pi 2 and 3
Py3
Libtensorflow MacOS CPU
Status Temporarily Unavailable
Nightly Binary Official GCS
Libtensorflow Linux CPU
Status Temporarily Unavailable
Nightly Binary Official GCS
Libtensorflow Linux GPU
Status Temporarily Unavailable
Nightly Binary Official GCS
Libtensorflow Windows CPU
Status Temporarily Unavailable
Nightly Binary Official GCS
Libtensorflow Windows GPU
Status Temporarily Unavailable
Nightly Binary Official GCS
Resources
TensorFlow.org
TensorFlow Tutorials
TensorFlow Official Models
TensorFlow Examples
TensorFlow Codelabs
TensorFlow Blog
Learn ML with TensorFlow
TensorFlow Twitter
TensorFlow YouTube
TensorFlow model optimization roadmap
TensorFlow White Papers
TensorBoard Visualization Toolkit
TensorFlow Code Search
Learn more about the TensorFlow community and how to contribute. Courses Coursera Udacity Edx LicenseApache License 2.0 |
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