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Duration 7 hours
Course Outline
Introduction to Huawei’s AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and associated tools
- AI development workflow: from training to deployment
Understanding the CANN Toolkit
- Definition of CANN and its significance
- Overview of essential components (ATC, AscendCL, operator libraries)
- The function of CANN in AI inference pipelines
Initiating with MindSpore and CANN
- Environment setup (MindSpore + CANN + Python)
- Training a basic model using MindSpore
- Exporting and converting the model via ATC
Executing Inference on Ascend Devices
- Utilizing the OM model with AscendCL or Python APIs
- Verifying model outputs
Integrating Other Frameworks
- Support overview for TensorFlow, PyTorch, and ONNX
- Compatible operators and associated constraints
- Demo of a simple model conversion (e.g., from ONNX to OM)
Discovering the CANN and MindSpore Developer Ecosystem
- Essential resources: documentation, GitHub repositories, sample code
- Overview of MindSpore Hub and the model zoo
- Community forums, events, and support channels
Summary and Future Directions
Requirements
- Fundamental understanding of machine learning and deep learning principles
- No previous exposure to CANN or Ascend hardware is necessary
Target Audience
- Machine learning developers investigating deployment workflows
- Students or researchers newly entering Huawei’s AI ecosystem
- AI framework contributors and enthusiasts interested in accelerating model performance
