commit | 54ece8356a25aa09c747b82d59510c70ff5eca52 | [log] [tgz] |
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author | A. Unique TensorFlower <gardener@tensorflow.org> | Tue Oct 26 11:10:37 2021 -0700 |
committer | TensorFlower Gardener <gardener@tensorflow.org> | Tue Oct 26 11:19:07 2021 -0700 |
tree | f26f05a7250a415708b5eaef2cd3f13bc10d734a | |
parent | 02f5601acdca3b468e568de2833ec893cbba2ef8 [diff] |
Deduplicate FunctionLibraryDefinition objects in TF to save memory. This change de-duplicates the two FunctionLibraryDefinition objects that are created in the context of `tensorflow::DirectSession` when the tensorflow models are being loaded into the program ahead of inference executions. The bulk of the memory consumption in the library definition is taken by shared-pointers, which can be used to remove duplicates and save RAM. PiperOrigin-RevId: 405689714 Change-Id: I6d3f2afca0218dfec571d8551e2b9f8947d33993
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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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!'
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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 |
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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.