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Course Outline
Introduction to CANN and Ascend AI Processors
- Overview of CANN and its position within Huawei’s AI compute stack
- Detailed look at Ascend processor architectures (e.g., 310, 910)
- Summary of supported AI frameworks and the associated toolchain
Model Conversion and Compilation
- Utilizing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX
- Generating and verifying OM model files
- Addressing unsupported operators and resolving frequent conversion challenges
Deployment via MindSpore and Other Frameworks
- Deploying models using MindSpore Lite
- Integrating OM models with Python APIs or C++ SDKs
- Managing models through the Ascend Model Manager
Performance Optimization and Profiling
- Insights into AI Core, memory, and tiling optimization techniques
- Profiling model execution using CANN-specific tools
- Best practices for enhancing inference speed and resource efficiency
Error Handling and Debugging
- Common deployment errors and strategies for resolution
- Interpreting logs and utilizing error diagnosis utilities
- Conducting unit testing and functional validation of deployed models
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge computing 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, including TensorFlow or PyTorch
- A solid grasp of neural network architectures and model training processes
- Fundamental knowledge of Linux command-line interface (CLI) and scripting
Target Audience
- AI engineers focused on model deployment
- Machine learning practitioners aiming to leverage hardware acceleration
- Deep learning developers constructing inference solutions
14 Hours
