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Course Outline
Introduction to Huawei’s AI Ecosystem
- Ascend AI hardware: An overview of the 310, 910, and 910B series
- High-level components: MindSpore, CANN, and AscendCL
- Industry positioning and underlying architecture principles
The Role of CANN in Huawei’s AI Stack
- What is CANN? The purpose of the SDK and its internal layers
- ATC, TBE, and AscendCL: Compiling and executing models
- How CANN enables inference optimisation 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
- Creating models in MindSpore or converting them from other frameworks
- Exporting and compiling models using ATC
- Deploying on Ascend hardware using OM models and AscendCL
Comparison with Other AI Stacks
- MindSpore vs. PyTorch and TensorFlow: Strategic focus and positioning
- Deployment workflows on Ascend compared to GPU-based stacks
- Opportunities and limitations for enterprise applications
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 required
Audience
- AI platform evaluators and infrastructure architects
- AI/ML DevOps and pipeline integrators
- Technology managers and decision-makers
14 Hours
