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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 (including 310, 910, and others)
- Survey of supported AI frameworks and toolchains
Model Conversion and Compilation
- Employing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX
- Generating and verifying OM model files
- Addressing unsupported operators and frequent conversion challenges
Deployment via MindSpore and Other Frameworks
- Deploying models utilizing MindSpore Lite
- Incorporating OM models with Python APIs or C++ SDKs
- Operating with the Ascend Model Manager
Performance Optimization and Profiling
- Exploring AI Core, memory, and tiling optimizations
- Profiling model execution using 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 the error diagnosis tool
- Conducting unit tests and functional validation for deployed models
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge applications
- Integrating 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, including TensorFlow or PyTorch
- Knowledge of neural network structures and model training processes
- Foundational knowledge of Linux CLI and scripting
Target Audience
- AI engineers focused on model deployment
- Machine learning specialists seeking hardware acceleration capabilities
- Deep learning developers creating inference solutions
14 Hours
