A demo to run tensorflow-lite on Penglai TEE.

Overview

Python PyPI

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 [email protected]. 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

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
>>> 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.

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:

Fuzzing Status CII Best Practices Contributor Covenant

Continuous build status

Official Builds

Build Type Status Artifacts
Linux CPU Status PyPI
Linux GPU Status PyPI
Linux XLA Status TBA
macOS Status PyPI
Windows CPU Status PyPI
Windows GPU Status PyPI
Android Status Download
Raspberry Pi 0 and 1 Status Py3
Raspberry Pi 2 and 3 Status 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

Community Supported Builds

Build Type Status Artifacts
Linux AMD ROCm GPU Nightly Build Status Nightly
Linux AMD ROCm GPU Stable Release Build Status Release 1.15 / 2.x
Linux s390x Nightly Build Status Nightly
Linux s390x CPU Stable Release Build Status Release
Linux ppc64le CPU Nightly Build Status Nightly
Linux ppc64le CPU Stable Release Build Status Release 1.15 / 2.x
Linux ppc64le GPU Nightly Build Status Nightly
Linux ppc64le GPU Stable Release Build Status Release 1.15 / 2.x
Linux aarch64 CPU Nightly (Linaro) Build Status Nightly
Linux aarch64 CPU Stable Release (Linaro) Build Status Release 1.x & 2.x
Linux aarch64 CPU Nightly (OpenLab)
Python 3.6
Build Status Nightly
Linux aarch64 CPU Stable Release (OpenLab) Build Status Release 1.15 / 2.x
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Nightly Build Status Nightly
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable Release Build Status Release 1.15 / 2.x
Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6
Build Status 1.13.1 PyPI

Community Supported Containers

Container Type Status Artifacts
TensorFlow aarch64 Neoverse-N1 CPU Stable (Linaro)
Debian
Static Release 2.3

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0

Issues
Owner
IPADS
The institute of parallel and distributed systems (IPADS) conducts research in all aspects of computer systems.
IPADS
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