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Duration 7 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and associated tools
- AI development workflow: from training to deployment
Grasping the CANN Toolkit
- Definition of CANN and its significance
- Summary of core components (ATC, AscendCL, operator libraries)
- Function of CANN within AI inference pipelines
Initiating Work with MindSpore and CANN
- Environment setup (MindSpore + CANN + Python)
- Training a fundamental model in MindSpore
- Exporting and transforming the model via ATC
Executing Inference on Ascend Devices
- Utilizing the OM model with AscendCL or Python APIs
- Basic input/output preprocessing
- Verifying model outputs
Integration with Alternative Frameworks
- Summary of support for TensorFlow, PyTorch, and ONNX
- Supported operators and constraints
- Basic model conversion demonstration (e.g., from ONNX to OM)
Exploring the CANN and MindSpore Developer Community
- Essential resources: documentation, GitHub repositories, sample code
- Overview of MindSpore Hub and model zoo
- Community forums, events, and support channels
Recap and Future Directions
Requirements
- Foundational 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 developers investigating deployment workflows
- Students or researchers entering Huawei’s AI ecosystem
- AI framework contributors and enthusiasts interested in model acceleration
