Heterogeneous Run Time version of TensorFlow. Added heterogeneous capabilities to the TensorFlow, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original TensorFlow architecture which users deploy their applications seamlessly.
TensorFlow-HRT is a project that is maintained by OPEN AI LAB, it uses heterogeneous computing infrastructure framework to speed up Tensorflow and provide utilities to debug, profile and tune application performance.
The release version is 0.0.1, is based on Rockchip RK3399 Platform, target OS is Ubuntu 16.04. Can download the source code from OAID/TensorFlow-HRT
There are some compatibility issues between ACL and Tensorflow ops.
Performance is not good. In the future, TensorFlow-HRT needs to skip ACL runtime layer or only uses ACL very low layer APIs.
The Tensorflow version is 31b79e42b9e1643b3bcdc9df992eb3ce216804c5.
Support Arm Compute Library version 17.12 and following TF ops
Linux CPU |
Linux GPU |
Mac OS CPU |
Windows CPU |
Android |
---|---|---|---|---|
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 lets you 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.
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.
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
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.Individual whl files
$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> sess.run(hello)
'Hello, TensorFlow!'
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> sess.run(a + b)
42
>>> sess.close()
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.