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Duration 14 hours
Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN and its position within Huawei’s AI compute stack
- Overview of Ascend processor architectures (e.g., 310, 910)
Model Conversion and Compilation
- Employing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX
- Generating and validating OM model files
- Managing unsupported operators and addressing frequent conversion challenges
Deployment with MindSpore and Other Frameworks
- Deploying models using MindSpore Lite
- Integrating OM models via Python APIs or C++ SDKs
- Operating with the Ascend Model Manager
Performance Optimization and Profiling
- Grasping AI Core, memory, and tiling optimization strategies
- Profiling model execution using CANN tools
- Applying best practices to enhance inference speed and resource efficiency
Error Handling and Debugging
- Resolving common deployment errors
- Interpreting logs and utilizing error diagnosis tools
- Conducting unit tests and functional validation of deployed models
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge case applications
- Integrating with cloud-based APIs and microservices
- Real-world case studies in computer vision and NLP
Summary and Future Directions
Requirements
- Proficiency with Python-based deep learning frameworks like TensorFlow or PyTorch
- Fundamental knowledge of Linux command-line interfaces and scripting
Intended Audience
- AI engineers focused on model deployment
- Machine learning practitioners aiming for hardware acceleration
- Deep learning developers constructing inference solutions
