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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 auxiliary tools
- AI development workflow: from training to deployment
Understanding the CANN Toolkit
- Definition of CANN and its significance
- Summary of core components (ATC, AscendCL, operator libraries)
- CANN’s role in AI inference pipelines
Getting Started with MindSpore and CANN
- Environment setup (MindSpore + CANN + Python)
- Training a fundamental model in MindSpore
- Exporting and converting the model via ATC
Executing Inference on Ascend Devices
- Utilising the OM model with AscendCL or Python APIs
- Basic input/output preprocessing
- Verifying model outputs
Integrating Other Frameworks
- Support overview for TensorFlow, PyTorch, and ONNX
- Supported operators and associated limitations
- Demonstration of simple model conversion (e.g., from ONNX to OM)
Exploring the CANN and MindSpore Developer Ecosystem
- Key resources: documentation, GitHub repositories, sample code
- MindSpore Hub and model zoo introduction
- Community forums, events, and support channels
Recap and Future Steps
Requirements
- A basic grasp of machine learning and deep learning principles
- Some programming familiarity with Python
- No previous experience with CANN or Ascend hardware is necessary
Target Audience
- Machine learning developers investigating deployment workflows
- Students or researchers unfamiliar with Huawei’s AI ecosystem
- AI framework contributors and enthusiasts interested in model acceleration
