Learning Wizard
This section describes the intended audience, development process of a TensorFlow 2.6.5-based model, and precautions for using this document.
Intended Audience
This document is intended for AI algorithm engineers. It describes how to port a training script developed by TensorFlow 2.6.5 Python APIs to the Ascend AI Processor, and achieve optimal training accuracy.
- Be familiar with the basic CANN architecture and features.
- Be proficient in Python programming.
- Be familiar with TensorFlow APIs.
- Possess knowledge of machine learning and deep learning, especially network training basics.
Supported Products
Precautions
- Before porting a model to the Ascend AI Processor, prepare a model developed in TensorFlow 2.6.5 and the dataset, and run the model on the GPU or CPU to test its accuracy and performance. In addition, record the accuracy and performance results for later comparison with those on the Ascend AI Processor.
- The code snippets in this document are only examples. Manual tweaking is needed.
Model Development Process
The model development aims to port the original TensorFlow-based model to the Ascend AI Processor and start training. The process is as follows.
Porting Modes
You have the following two options for porting your training script developed based on the TensorFlow Python API to the Ascend AI Processor:
- Automatic porting
Algorithm engineers can use the porting tool to analyze the support for the TensorFlow Python API on the Ascend AI Processor, and automatically port native TensorFlow training scripts to those supported by the Ascend AI Processor. For APIs unportable by the tool, modify your training scripts according to the tool report.
- Manual porting
Algorithm engineers can modify TensorFlow training scripts to adapt them to the Ascend AI Processor. This method is more complex. The more friendly automated porting mode is recommended.