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Duration 7 hours
Course Outline
Overview of Huawei's AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and associated tools
- AI development lifecycle: from training to deployment
Comprehending the CANN Toolkit
- The definition and significance of CANN
- Key components explained (ATC, AscendCL, operator libraries)
- CANN's function within AI inference pipelines
Commencing with MindSpore and CANN
- Configuring the environment (MindSpore + CANN + Python)
- Training a fundamental model using MindSpore
- Exporting and converting models via ATC
Executing Inference on Ascend Devices
- Utilizing OM models through AscendCL or Python APIs
- Basic input/output preprocessing techniques
- Verifying model outputs
Integrating with Alternative Frameworks
- Support status for TensorFlow, PyTorch, and ONNX
- Compatible operators and associated limitations
- Demonstration of simple model conversion (e.g., ONNX to OM)
Navigating the CANN and MindSpore Developer Community
- Essential resources: documentation, GitHub repositories, sample code
- Introduction to MindSpore Hub and the model zoo
- Accessing community forums, events, and support channels
Recap and Future Directions
Requirements
- Fundamental knowledge of machine learning and deep learning principles
- Familiarity with Python programming
- No previous experience with CANN or Ascend hardware is necessary
Target Audience
- Machine learning engineers investigating deployment workflows
- Academic students or researchers new to Huawei's AI ecosystem
- AI framework contributors and enthusiasts focused on model acceleration
