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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.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

Install

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

Other devices (DirectX and MacOS-metal) are supported using Device plugins.

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.

Try your first TensorFlow program

$ python
>>>importtensorflowastf>>>tf.add(1, 2).numpy()
3>>>hello=tf.constant('Hello, TensorFlow!')
>>>hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

Contribution guidelines

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.

Continuous build status

You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.

Official Builds

Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS

Resources

Learn more about the TensorFlow community and how to contribute.

Courses

License

Apache License 2.0

About

An Open Source Machine Learning Framework for Everyone

tensorflow.org

Resources

Readme
Apache-2.0 license

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity

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0 stars

Watchers

1 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - mansnils/tensorflow: An Open Source Machine Learning Framework for Everyone · GitHub
Skip to content
BranchesTags
Repository files navigation
PythonPyPIDOICII Best PracticesOpenSSF ScorecardFuzzing StatusFuzzing StatusContributor Covenant
Documentation
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.
Install
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

Other devices (DirectX and MacOS-metal) are supported using
Device plugins.
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.
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>tf.add(1, 2).numpy()
3>>>hello=tf.constant('Hello, TensorFlow!')
>>>hello.numpy()
b'Hello, TensorFlow!'
For more examples, see the
TensorFlow tutorials.
Contribution guidelines
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.
Continuous build status
You can find more community-supported platforms and configurations in the
TensorFlow SIG Build community builds table.
Official Builds
Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Resources
Learn more about the
TensorFlow community and how to
contribute.
Courses
License
Apache License 2.0
About
An Open Source Machine Learning Framework for Everyone
tensorflow.org
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
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, 'i');
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var style = document.createElement('style');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - mansnils/tensorflow: An Open Source Machine Learning Framework for Everyone · GitHub
Skip to content
BranchesTags
Repository files navigation
PythonPyPIDOICII Best PracticesOpenSSF ScorecardFuzzing StatusFuzzing StatusContributor Covenant
Documentation
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.
Install
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

Other devices (DirectX and MacOS-metal) are supported using
Device plugins.
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.
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>tf.add(1, 2).numpy()
3>>>hello=tf.constant('Hello, TensorFlow!')
>>>hello.numpy()
b'Hello, TensorFlow!'
For more examples, see the
TensorFlow tutorials.
Contribution guidelines
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.
Continuous build status
You can find more community-supported platforms and configurations in the
TensorFlow SIG Build community builds table.
Official Builds
Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Resources
Learn more about the
TensorFlow community and how to
contribute.
Courses
License
Apache License 2.0
About
An Open Source Machine Learning Framework for Everyone
tensorflow.org
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - mansnils/tensorflow: An Open Source Machine Learning Framework for Everyone · GitHub
Skip to content
BranchesTags
Repository files navigation
PythonPyPIDOICII Best PracticesOpenSSF ScorecardFuzzing StatusFuzzing StatusContributor Covenant
Documentation
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.
Install
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

Other devices (DirectX and MacOS-metal) are supported using
Device plugins.
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.
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>tf.add(1, 2).numpy()
3>>>hello=tf.constant('Hello, TensorFlow!')
>>>hello.numpy()
b'Hello, TensorFlow!'
For more examples, see the
TensorFlow tutorials.
Contribution guidelines
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.
Continuous build status
You can find more community-supported platforms and configurations in the
TensorFlow SIG Build community builds table.
Official Builds
Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Resources
Learn more about the
TensorFlow community and how to
contribute.
Courses
License
Apache License 2.0
About
An Open Source Machine Learning Framework for Everyone
tensorflow.org
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
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var u = new URL(url, window.location.origin);
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});
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - mansnils/tensorflow: An Open Source Machine Learning Framework for Everyone · GitHub
Skip to content
BranchesTags
Repository files navigation
PythonPyPIDOICII Best PracticesOpenSSF ScorecardFuzzing StatusFuzzing StatusContributor Covenant
Documentation
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.
Install
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

Other devices (DirectX and MacOS-metal) are supported using
Device plugins.
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.
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>tf.add(1, 2).numpy()
3>>>hello=tf.constant('Hello, TensorFlow!')
>>>hello.numpy()
b'Hello, TensorFlow!'
For more examples, see the
TensorFlow tutorials.
Contribution guidelines
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.
Continuous build status
You can find more community-supported platforms and configurations in the
TensorFlow SIG Build community builds table.
Official Builds
Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Resources
Learn more about the
TensorFlow community and how to
contribute.
Courses
License
Apache License 2.0
About
An Open Source Machine Learning Framework for Everyone
tensorflow.org
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
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Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
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lastUrl = location.href;
setTimeout(tryTheater, 500);
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - mansnils/tensorflow: An Open Source Machine Learning Framework for Everyone · GitHub
Skip to content
BranchesTags
Repository files navigation
PythonPyPIDOICII Best PracticesOpenSSF ScorecardFuzzing StatusFuzzing StatusContributor Covenant
Documentation
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.
Install
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

Other devices (DirectX and MacOS-metal) are supported using
Device plugins.
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.
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>tf.add(1, 2).numpy()
3>>>hello=tf.constant('Hello, TensorFlow!')
>>>hello.numpy()
b'Hello, TensorFlow!'
For more examples, see the
TensorFlow tutorials.
Contribution guidelines
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.
Continuous build status
You can find more community-supported platforms and configurations in the
TensorFlow SIG Build community builds table.
Official Builds
Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
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GitHub - mansnils/tensorflow: An Open Source Machine Learning Framework for Everyone · GitHub
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Documentation
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.
Install
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

Other devices (DirectX and MacOS-metal) are supported using
Device plugins.
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.
Try your first TensorFlow program
$ python
>>>importtensorflowastf>>>tf.add(1, 2).numpy()
3>>>hello=tf.constant('Hello, TensorFlow!')
>>>hello.numpy()
b'Hello, TensorFlow!'
For more examples, see the
TensorFlow tutorials.
Contribution guidelines
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.
Continuous build status
You can find more community-supported platforms and configurations in the
TensorFlow SIG Build community builds table.
Official Builds
Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly BinaryOfficial GCS
Resources
Learn more about the
TensorFlow community and how to
contribute.
Courses
License
Apache License 2.0
About
An Open Source Machine Learning Framework for Everyone
tensorflow.org
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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