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Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN and its role in Huawei’s AI compute stack
- Overview of Ascend processor architectures (including 310, 910, etc.)
- Overview of supported AI frameworks and the toolchain
Model Conversion and Compilation
- Employing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX
- Creating and validating OM model files
- Addressing unsupported operators and resolving common conversion issues
Deploying with MindSpore and Other Frameworks
- Deploying models using MindSpore Lite
- Integrating OM models with Python APIs or C++ SDKs
- Working with the Ascend Model Manager
Performance Optimization and Profiling
- Understanding optimizations related to AI Core, memory, and tiling
- Profiling model execution using CANN tools
- Best practices for enhancing inference speed and resource utilization
Error Handling and Debugging
- Common deployment errors and their resolutions
- Interpreting logs and utilizing the error diagnosis tool
- Unit testing and functional validation of deployed models
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge applications
- Integration with cloud-based APIs and microservices
- Real-world case studies in computer vision and NLP
Summary and Next Steps
Requirements
- Hands-on experience with Python-based deep learning frameworks such as TensorFlow or PyTorch
- Solid understanding of neural network architectures and model training workflows
- Basic proficiency with Linux command-line interface and scripting
Target Audience
- AI engineers focused on model deployment
- Machine learning practitioners seeking hardware acceleration
- Deep learning developers constructing inference solutions
14 Hours
