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Course Outline
Introduction to CANN and Ascend AI Processors
- Defining CANN and its position within Huawei’s AI compute ecosystem
- An overview of Ascend processor architectures (e.g., 310, 910)
- A summary of supported AI frameworks and the associated toolchain
Model Conversion and Compilation
- Leveraging the ATC tool for model conversion from TensorFlow, PyTorch, and ONNX
- Generating and validating OM model files
- Managing unsupported operators and addressing typical conversion challenges
Deployment via MindSpore and Other Frameworks
- Deploying models using MindSpore Lite
- Integrating OM models through Python APIs or C++ SDKs
- Utilizing the Ascend Model Manager
Performance Optimization and Profiling
- Exploring AI Core, memory, and tiling optimization strategies
- Profiling model execution using CANN diagnostic 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 tests and functional validation for 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 natural language processing
Conclusion and Recommended Next Steps
Requirements
- Proficiency with Python-based deep learning frameworks like TensorFlow or PyTorch
- Solid understanding of neural network structures and model training workflows
- Basic knowledge of Linux command-line interfaces and scripting
Target Audience
- AI engineers focused on model deployment tasks
- Machine learning specialists aiming for hardware acceleration solutions
- Deep learning developers constructing inference systems
14 Hours
