TF_Adapter

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TF_Adapter is committed to providing the outstanding computing power of Ascend AI Processors to developers who use the TensorFlow framework. Developers only need to install the TF_Adapter plug-in and add a small amount of configuration to the existing TensorFlow script to accelerate their training jobs on the Ascend AI Processors.

You can read TF_Adapter Interface for more details。

Installation Guide

Building from source

You can build the TF_Adapter software package from the source code and install it on the Ascend AI Processor environment.

The TF_Adapter plug-in has a strict matching relationship with TensorFlow. Before building from source code, you need to ensure that it has been installed correctly TensorFlow v1.15.0 Version.

You may also build GraphEngine from the source. To build GraphEngine, please make sure that you have access to an Ascend 910 environment as compiling environment, and make sure that the following software requirements are fulfilled.

• Linux OS
• GCC >= 7.3.0
• CMake >= 3.14.0
• SWIG

Download

git clone https://gitee.com/ascend/tensorflow.git
cd tensorflow

Execute the script to generate the installation package

chmod +x build.sh
./build.sh

After the script is successfully executed, a compressed file of tfadapter.tar will be generated in the output directory.

Install

Unzip the tfadapter.tar file to generate npu_bridge-1.15.0-py3-none-any.whl. Then you can install the TF_Adapter plug-in using pip.

pip install ./dist/python/dist/npu_bridge-1.15.0-py3-none-any.whl

It should be noted that you should ensure that the installation path is the same as the Python you specified when compiling The interpreter search path is consistent.

Contributing

Welcome to contribute.

Release Notes

For Release Notes, see our RELEASE.

License

Apache License 2.0