commit | b1e271c89cedf3e715a0292efd5ceed900f0e92b | [log] [tgz] |
---|---|---|
author | Nicolas Vasilache <ntv@google.com> | Fri Sep 20 09:25:52 2019 -0700 |
committer | TensorFlower Gardener <gardener@tensorflow.org> | Fri Sep 20 09:47:35 2019 -0700 |
tree | 542d6d02aa243f6bb4e07a20b2be94d1ea8fea02 | |
parent | dfed968d9dca14a0ca201f383ad67def2a3571d3 [diff] |
Add utility to extract strides from layout map in MemRefType. The RFC for unifying Linalg and Affine compilation passes into an end-to-end flow discusses the notion of a strided MemRef (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio). This CL adds helper functions to extract strides from the layout map which in turn will allow converting between a strided form of the type and a layout map. For now strides are only computed on a single affine map with a single result (i.e. the closed subset of linearization maps that are compatible with striding semantics). This restriction will be reevaluated / lifted in the future based on concrete use cases. PiperOrigin-RevId: 270284686
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 for the purposes of conducting 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 backwards 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 for CPU-only:
$ pip install tensorflow
Use the GPU package for CUDA-enabled GPU cards:
$ pip install tensorflow-gpu
Nightly binaries are available for testing using the tf-nightly and tf-nightly-gpu packages on PyPi.
$ python
>>> import tensorflow as tf >>> tf.enable_eager_execution() >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() '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 | Py2 Py3 | |
Raspberry Pi 2 and 3 | Py2 Py3 |
Build Type | Status | Artifacts |
---|---|---|
Linux AMD ROCm GPU Nightly | Nightly | |
Linux AMD ROCm GPU Stable Release | Release | |
Linux s390x Nightly | Nightly | |
Linux s390x CPU Stable Release | Release | |
Linux ppc64le CPU Nightly | Nightly | |
Linux ppc64le CPU Stable Release | Release | |
Linux ppc64le GPU Nightly | Nightly | |
Linux ppc64le GPU Stable Release | Release | |
Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
Linux CPU with Intel® MKL-DNN Supports Python 2.7, 3.4, 3.5, 3.6 and 3.7 | 1.14.0 pypi | |
Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 pypi |
Learn more about the TensorFlow community and how to contribute.