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План на курса
Introduction to CANN and Ascend AI Processors
- What is CANN? Role in Huawei’s AI compute stack
- Overview of Ascend processor architecture (310, 910, etc.)
- Supported AI frameworks and toolchain overview
Model Conversion and Compilation
- Using the ATC tool for model conversion (TensorFlow, PyTorch, ONNX)
- Creating and validating OM model files
- Handling unsupported operators and common conversion issues
Deploying with MindSpore and Other Frameworks
- Deploying models with 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 with CANN tools
- Best practices for improving inference speed and resource usage
Error Handling and Debugging
- Common deployment errors and their resolution
- Reading logs and using 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
Изисквания
- Experience with Python-based deep learning frameworks such as TensorFlow or PyTorch
- Understanding of neural network architectures and model training workflows
- Basic familiarity with Linux CLI and scripting
Audience
- AI engineers working with model deployment
- Machine learning practitioners targeting hardware acceleration
- Deep learning developers building inference solutions
14 Часа
