commit | 1de80ace2c74f3501fdcac48293eb3afdaa3e86e | [log] [tgz] |
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author | Yang Chen <yangchen@google.com> | Wed Nov 17 13:27:55 2021 -0800 |
committer | TensorFlower Gardener <gardener@tensorflow.org> | Wed Nov 17 13:42:40 2021 -0800 |
tree | 758726674c89633b88660443ee00f6776c51e63f | |
parent | 76bc1a8a691eb6d21dac3cd54a8c3ae984855f2c [diff] |
[tf.data service] Workers pass job UIDs to the dispatcher and client. The worker passes the UID to dispatcher in WorkerHeartbeatRequest. The dispatcher passes the UID to client in ClientHeartbeatResponse. (Passing it in GetOrCreateJobResponse may make it easier to export, but workers may not be ready at that time.) PiperOrigin-RevId: 410614144 Change-Id: Ie539949a49f980749ded7b52c1b7c9649aa506b1
Documentation |
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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 on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.
Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.
See 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 tensorflow
A smaller CPU-only package is also available:
$ pip install tensorflow-cpu
To 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.
$ 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.
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, 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:
You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.
Build Type | Status | Artifacts |
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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 |
Learn more about the TensorFlow community and how to contribute.