| commit | 1c74b32aa27dc0d40a9ce1f883ea632d399a7b9a | [log] [tgz] |
|---|---|---|
| author | Haoyu Zhang <haoyuzhang@google.com> | Tue May 12 21:23:08 2020 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Tue May 12 21:27:15 2020 -0700 |
| tree | 15fcb72c41aeb86af46bb675e8c8f7fbea4eea4d | |
| parent | f690a054c599d51d7c8e9ae83c7d0ebd70f80cca [diff] |
Validate remote resource devices before safe access of resources. Cluster updates (due to recreated distribution strategies, remote worker failures, etc.) can lead to crashing failures with segfaults when accessing resources created before the update. Some common patterns are: * Accessing datasets created on old remote workers; * Accessing variables created on failed workers; * Garbage collecting datasets/iterators created on old remote workers; This CL validate the remote devices to make sure the access is safe before executing the ops by looking up the device in a set of device pointers and checking its incarnation ID. Remote workers on restarted devices will have different incarnation IDs, and accessing resources on those devices will fail gracefully. PiperOrigin-RevId: 311261000 Change-Id: Ifc07862229b06301e0275fe80975565d9df28152
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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 |   | 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 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 CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Stable Release | Release 1.15 / 2.x | |
| 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.