| commit | a764f3ab76d22d5e313610b5f1262f73b395ad62 | [log] [tgz] |
|---|---|---|
| author | Allen Lavoie <allenl@google.com> | Thu Aug 27 16:30:38 2020 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Thu Aug 27 16:39:24 2020 -0700 |
| tree | 66c2bc126e8f63bd61f6c20ec81dda78d204d4af | |
| parent | e6f238fbaf3665b372d1349be87160f328a81e78 [diff] |
Give custom devices the option to do type-based dispatch for ops with no explicit placement When there is a custom device input and one or more physical device inputs to an op, presents the op to the custom device but indicates that the user did not explicitly request the placement (via the device property of the passed op). Custom devices which want to stick to strict scope-based placement can either copy off the inputs and run the op on the default device or throw an error. The parallel device will stick with scope-only dispatch for now. PiperOrigin-RevId: 328840123 Change-Id: Ic7490c0700a7ca5c74fd362211fa2fc9e008051c
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.
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$ 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!'
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| Build Type | Status | Artifacts |
|---|---|---|
| Linux CPU |  | PyPI |
| Linux GPU |  | PyPI |
| Linux XLA |  | TBA |
| macOS |  | PyPI |
| Windows CPU |  | PyPI |
| Windows GPU |  | PyPI |
| Android |  |  |
| Raspberry Pi 0 and 1 |  | Py3 |
| Raspberry Pi 2 and 3 |  | Py3 |
| Libtensorflow MacOS CPU |  | GCS |
| Libtensorflow Linux CPU |  | GCS |
| Libtensorflow Linux GPU |  | GCS |
| Libtensorflow Windows CPU |  | GCS |
| Libtensorflow Windows GPU |  | GCS |
| Build Type | Status | Artifacts |
|---|---|---|
| Linux AMD ROCm GPU Nightly | Nightly | |
| Linux AMD ROCm GPU Stable Release | Release 1.15 / 2.x | |
| Linux s390x Nightly | Nightly | |
| Linux s390x CPU Stable Release | Release | |
| Linux ppc64le CPU Nightly | Nightly | |
| Linux ppc64le CPU Stable Release | Release 1.15 / 2.x | |
| Linux ppc64le GPU Nightly | Nightly | |
| Linux ppc64le GPU Stable Release | Release 1.15 / 2.x | |
| Linux aarch64 CPU Nightly Python 3.6 | Nightly | |
| Linux aarch64 CPU Stable Release | Release 1.15 / 2.x | |
| Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Nightly | Nightly | |
| Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable Release | Release 1.15 / 2.x | |
| Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 PyPI |
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