| commit | ce2f9824eee904d60fdc0444ac8a82b217ea9149 | [log] [tgz] |
|---|---|---|
| author | Scott Zhu <scottzhu@google.com> | Thu Jun 04 09:44:23 2020 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Thu Jun 04 09:53:42 2020 -0700 |
| tree | 5083f3efaab2880c8c86e7f250dcbb5735e15c04 | |
| parent | fe33f393b86904913898d25c5fd7f263058ed5a6 [diff] |
Trying to reduce the memory leak when validation_split is used in model.fit(). Not sure about the life cycle of the eager tensor has, but the memory increase is greatly reduced after this change when testing with np input and validation split. It is possible that there are still issues in the code base that cause the eager tensor to be not released, but we probably should aggressively convert np or pd data into eager tensor. Memory log before change: #--- Run 1 of 20 memory used (MB): 420.94592 #--- Run 2 of 20 memory used (MB): 455.458816 #--- Run 3 of 20 memory used (MB): 480.89088 #--- Run 4 of 20 memory used (MB): 504.799232 #--- Run 5 of 20 memory used (MB): 465.563648 #--- Run 6 of 20 memory used (MB): 485.797888 #--- Run 7 of 20 memory used (MB): 506.544128 #--- Run 8 of 20 memory used (MB): 526.76608 #--- Run 9 of 20 memory used (MB): 547.782656 #--- Run 10 of 20 memory used (MB): 487.981056 #--- Run 11 of 20 memory used (MB): 508.862464 #--- Run 12 of 20 memory used (MB): 528.904192 #--- Run 13 of 20 memory used (MB): 549.933056 #--- Run 14 of 20 memory used (MB): 570.032128 #--- Run 15 of 20 memory used (MB): 510.455808 #--- Run 16 of 20 memory used (MB): 530.501632 #--- Run 17 of 20 memory used (MB): 551.559168 #--- Run 18 of 20 memory used (MB): 571.408384 #--- Run 19 of 20 memory used (MB): 529.518592 #--- Run 20 of 20 memory used (MB): 549.376 Memory log after change: #--- Run 1 of 20 memory used (MB): 441.933824 #--- Run 2 of 20 memory used (MB): 463.753216 #--- Run 3 of 20 memory used (MB): 465.801216 #--- Run 4 of 20 memory used (MB): 466.366464 #--- Run 5 of 20 memory used (MB): 467.0464 #--- Run 6 of 20 memory used (MB): 467.709952 #--- Run 7 of 20 memory used (MB): 468.668416 #--- Run 8 of 20 memory used (MB): 468.62336 #--- Run 9 of 20 memory used (MB): 474.35776 #--- Run 10 of 20 memory used (MB): 474.353664 #--- Run 11 of 20 memory used (MB): 474.472448 #--- Run 12 of 20 memory used (MB): 474.648576 #--- Run 13 of 20 memory used (MB): 474.697728 #--- Run 14 of 20 memory used (MB): 474.750976 #--- Run 15 of 20 memory used (MB): 474.804224 #--- Run 16 of 20 memory used (MB): 474.800128 #--- Run 17 of 20 memory used (MB): 474.857472 #--- Run 18 of 20 memory used (MB): 474.918912 #--- Run 19 of 20 memory used (MB): 475.086848 #--- Run 20 of 20 memory used (MB): 475.348992 PiperOrigin-RevId: 314746357 Change-Id: I84cd784059ae4aec827a6e908df2ca738b2dac48
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!'
For more examples, see the TensorFlow tutorials.
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| 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 |  |  |
| Raspberry Pi 0 and 1 |  | Py3 |
| Raspberry Pi 2 and 3 |  | Py3 |
| Build Type | Status | Artifacts |
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| 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.