| commit | e238bb0843ce04d9b9cbca9b657bf03636975ee9 | [log] [tgz] |
|---|---|---|
| author | Jacques Pienaar <jpienaar@google.com> | Mon Sep 20 21:02:09 2021 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Mon Sep 20 21:07:13 2021 -0700 |
| tree | ddddf6ecf4cff9331c2baa2ce77b8f4019b5209c | |
| parent | 73fdedeacf42bed73963b05a5c0b1d7adadee716 [diff] |
Use _output_shapes to override on import with inference context _output_shapes is a little bit special. It is handled by some shape functions, it is ignored by most. It is an unregistered attribute that need not be most up to date (well it need not actually be accurate at all) but treating it as accurate during import mirroring GraphConstructor and override shapes if _output_shapes is set. This cannot be unconditionally set at the moment as there are some workflows that this would break, so gate behind a flag initially. Set the value to false initially in the struct and plumb through to enable testing - this should be a NOP/opt-in. PiperOrigin-RevId: 397908081 Change-Id: Ifd2526d9b32825a05d40e3d8c1b1497ee40e0694
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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.
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You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.
| 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 |
Learn more about the TensorFlow community and how to contribute.