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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
Grasping the CANN Toolkit
- Defining CANN and its significance
- An overview of key components (ATC, AscendCL, operator libraries)
- The role of CANN within AI inference pipelines
Commencing with MindSpore and CANN
- Environment setup (MindSpore + CANN + Python)
- Training a fundamental model in MindSpore
- Exporting and converting the model using ATC
Executing Inference on Ascend Devices
- Utilising the OM model via AscendCL or Python APIs
- Basic input and output preprocessing
- Verifying model outputs
Collaborating with 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)
Discovering the CANN and MindSpore Developer Ecosystem
- Essential resources: documentation, GitHub repositories, and sample code
- Overview of MindSpore Hub and the model zoo
- Community forums, events, and support channels
Recap and Future Directions
Requirements
- A foundational understanding of machine learning and deep learning principles
- Some programming proficiency with Python
- No prior experience with CANN or Ascend hardware is necessary
Target Audience
- Machine learning developers investigating deployment workflows
- Students or researchers new to Huawei’s AI ecosystem
- AI framework contributors and enthusiasts interested in model acceleration
