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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 supporting tools
- The AI development workflow: from training to deployment
Grasping the CANN Toolkit
- What CANN is and its significance
- Overview of core components (ATC, AscendCL, operator libraries)
- CANN’s role in AI inference pipelines
Initiation with MindSpore and CANN
- Environment setup (MindSpore + CANN + Python)
- Training a basic model in MindSpore
- Exporting and converting the model using ATC
Executing Inference on Ascend Devices
- Utilizing the OM model with AscendCL or Python APIs
- Fundamental input/output preprocessing
- Verifying model outputs
Integrating with Other Frameworks
- Overview of support 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
- Essential resources: documentation, GitHub repositories, sample code
- MindSpore Hub and model zoo overview
- Community forums, events, and support channels
Summary and Future Directions
Requirements
- A fundamental grasp of machine learning and deep learning principles
- Familiarity with Python programming
- No previous hands-on 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
