| commit | fdcfc2359157aacf6a67f26c1c39ce99d5fdde51 | [log] [tgz] |
|---|---|---|
| author | Rahul Joshi <jurahul@google.com> | Thu Dec 17 09:25:03 2020 -0800 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Thu Dec 17 09:28:50 2020 -0800 |
| tree | a0e82bd3182a320d898532814948912882d6efec | |
| parent | 165b3e83a7b19cecdb08c1a4d81887c524d857e0 [diff] |
[XLA:GPU] Add layout attributes to LHLO_GPU Convolution operations. - MLIR MemRefs do not preserve layout information correctly when unit dimensions are involved. Operations like convolution that use cuDNN however need the correct layout to be preserved so that we do not end up creating an incompatible combination of input/filter/output layout that is not supported by cuDNN. - Add these layouts to convolution attributes in the form of I32ArrayAttr for representing the layout in "minor_to_major" form similar to XLA. PiperOrigin-RevId: 348034757 Change-Id: I4bbccfc713d136335ac3b436a8b657bd34b98fae
Documentation |
|---|
 |
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:
| 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 |  | Nightly GCS Official GCS |
| Libtensorflow Linux CPU |  | Nightly GCS Official GCS |
| Libtensorflow Linux GPU |  | Nightly GCS Official GCS |
| Libtensorflow Windows CPU |  | Nightly GCS Official GCS |
| Libtensorflow Windows GPU |  | Nightly GCS Official 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 (Linaro) | Nightly | |
| Linux aarch64 CPU Stable Release (Linaro) | Release 1.x & 2.x | |
| Linux aarch64 CPU Nightly (OpenLab) Python 3.6 | Nightly | |
| Linux aarch64 CPU Stable Release (OpenLab) | 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 |
| Container Type | Status | Artifacts |
|---|---|---|
| TensorFlow aarch64 Neoverse-N1 CPU Stable (Linaro) Debian | Static | Release 2.3 |
Learn more about the TensorFlow community and how to contribute.