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Course Outline
Introduction to Huawei’s AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and supporting tools
- AI development workflow: training to deployment
Understanding the CANN Toolkit
- What is CANN and why it matters
- Overview of core components (ATC, AscendCL, operator libraries)
- Role of CANN in AI inference pipelines
Getting Started with MindSpore and CANN
- Setting up the environment (MindSpore + CANN + Python)
- Training a basic model in MindSpore
- Exporting and converting the model using ATC
Running Inference on Ascend Devices
- Using the OM model with AscendCL or Python APIs
- Basic input/output preprocessing
- Validating model outputs
Working with Other Frameworks
- Overview of support for TensorFlow, PyTorch, and ONNX
- Supported operators and limitations
- Simple model conversion demo (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 overview
- Community forums, events, and support channels
Summary and Next Steps
Requirements
- Basic understanding of machine learning and deep learning concepts
- Some programming experience with Python
- No prior experience with CANN or Ascend hardware required
Audience
- Machine learning developers exploring deployment workflows
- Students or researchers new to Huawei’s AI ecosystem
- AI framework contributors and hobbyists interested in model acceleration
7 Hours
