Deploying AI Models with CANN and Ascend AI Processors Training Course

Introduction to CANN and Ascend AI Processors

• Understanding CANN: Its definition and function within Huawei’s AI compute ecosystem.
• An overview of Ascend processor architectures, including models like the 310 and 910.
• A summary of supported AI frameworks and the associated toolchain.

Model Conversion and Compilation

• Utilizing the ATC tool to convert models from TensorFlow, PyTorch, and ONNX.
• The process of creating and verifying OM model files.
• Strategies for managing unsupported operators and addressing typical conversion challenges.

Deployment with MindSpore and Other Frameworks

• Deploying models using MindSpore Lite.
• Integrating OM models via Python APIs or C++ SDKs.
• Utilizing the Ascend Model Manager for efficient workflow management.

Performance Optimization and Profiling

• Insights into AI Core, memory management, and tiling optimizations.
• Profiling model execution using dedicated CANN tools.
• Best practices for enhancing inference speed and resource efficiency.

Error Handling and Debugging

• Identifying and resolving common deployment errors.
• Interpreting logs and utilizing error diagnosis utilities.
• Conducting unit testing and functional validation for deployed models.

Edge and Cloud Deployment Scenarios

• Deploying solutions to Ascend 310 for edge computing applications.
• Integrating models with cloud-based APIs and microservices.
• Examining real-world case studies in computer vision and NLP.

Summary and Future Directions