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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 architecture (e.g., 310, 910)
- Survey of supported AI frameworks and toolchains
Model Conversion and Compilation
- Utilizing the ATC tool for model conversion (TensorFlow, PyTorch, ONNX)
- Generating and validating OM model files
- Addressing unsupported operators and typical conversion challenges
Deployment with MindSpore and Other Frameworks
- Deploying models via MindSpore Lite
- Integrating OM models with Python APIs or C++ SDKs
- Working with Ascend Model Manager
Performance Optimization and Profiling
- Understanding AI Core, memory, and tiling optimizations
- Profiling model execution using CANN tools
- Best practices for boosting inference speed and resource efficiency
Error Handling and Debugging
- Resolving common deployment errors
- Interpreting logs and utilizing error diagnosis tools
- Conducting 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
- Proficiency with Python-based deep learning frameworks such as TensorFlow or PyTorch
- Knowledge of neural network architectures and model training processes
- Fundamental familiarity with Linux CLI and scripting
Target Audience
- AI engineers focused on model deployment
- Machine learning specialists aiming for hardware acceleration
- Deep learning developers creating inference solutions
14 Hours
