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Course Outline
Introduction to Huawei’s AI Ecosystem
- Overview of Ascend AI hardware: models 310, 910, and 910B.
- Key components: MindSpore, CANN, and AscendCL.
- Industry positioning and architectural principles.
The Role of CANN in Huawei’s AI Stack
- What is CANN? Understanding its SDK purpose and internal layers.
- Functions of ATC, TBE, and AscendCL in compiling and executing models.
- How CANN supports inference optimization and deployment.
MindSpore Overview and Architecture
- Training and inference workflows within MindSpore.
- Graph mode, PyNative, and hardware abstraction.
- Integration with Ascend NPU via the CANN backend.
AI Lifecycle on Ascend: From Training to Deployment
- Model creation in MindSpore or conversion from other frameworks.
- Exporting and compiling models using ATC.
- Deploying on Ascend hardware using OM models and AscendCL.
Comparison with Other AI Stacks
- MindSpore versus PyTorch and TensorFlow: focus and positioning.
- Deployment workflows on Ascend versus GPU-based stacks.
- Opportunities and limitations for enterprise use.
Enterprise Integration Scenarios
- Use cases in smart manufacturing, government AI, and telecommunications.
- Scalability, compliance, and ecosystem considerations.
- Cloud/on-premises hybrid deployment using the Huawei stack.
Summary and Next Steps
Requirements
- Familiarity with AI workflows or platform architecture.
- Basic understanding of model training and deployment.
- No prior hands-on experience with CANN or MindSpore is required.
Target Audience
- AI platform evaluators and infrastructure architects.
- AI/ML DevOps professionals and pipeline integrators.
- Technology managers and decision-makers.
14 Hours
