Clone this repo:
  1. ff7e639 [Intel MKL] Fixing MKL graph layout pass test (#20065) by Niranjan Hasabnis · 3 hours ago master
  2. 8e86dcd Automate download and unzip of the model file (#14853) by Dan J · 21 hours ago
  3. ab9b134 Fix typo (#20082) by ManHyuk · 28 hours ago
  4. 6d16036 Do not depend on boringssl for big-endian architectures. (#20038) by Brennan Saeta · 2 days ago
  5. 79d7e11 Merge pull request #20048 from ruanjiandong/c_api_large_model by Asim Shankar · 3 days ago


TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

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.

Keep up to date with release announcements and security updates by subscribing to


See Installing TensorFlow for instructions on how to install our release binaries or how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Nightly pip packages

  • We are pleased to announce that TensorFlow now offers nightly pip packages under the tf-nightly and tf-nightly-gpu project on pypi. Simply run pip install tf-nightly or pip install tf-nightly-gpu in a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.

Try your first TensorFlow program

$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
'Hello, TensorFlow!'
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> + b)
>>> sess.close()

Learn more examples about how to do specific tasks in TensorFlow at the tutorials page of

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. So 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:

CII Best Practices

Continuous build status

Official Builds

Build TypeStatusArtifacts
Linux CPUStatuspypi
Linux GPUStatuspypi
Windows CPUStatuspypi
Windows GPUStatuspypi
AndroidStatusDownload demo APK, native libs build history

Community Supported Builds

Build TypeStatusArtifacts
IBM s390xBuild StatusTBA
IBM ppc64le CPUBuild StatusTBA

For more information

Learn more about the TensorFlow community at the community page of for a few ways to participate.


Apache License 2.0