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Course Outline
Introduction to Huawei's AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B.
- Key high-level components: MindSpore, CANN, and AscendCL.
- Industry positioning and core architecture principles.
The Role of CANN in Huawei's AI Stack
- Defining CANN: SDK purpose and internal layers.
- ATC, TBE, and AscendCL: model compilation and execution.
- How CANN facilitates inference optimisation and deployment.
MindSpore Overview and Architecture
- Training and inference workflows within MindSpore.
- Graph mode, PyNative, and hardware abstraction.
- Integration with Ascend NPUs 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 application.
Enterprise Integration Scenarios
- Use cases in smart manufacturing, government AI, and telecommunications.
- Scalability, compliance, and ecosystem considerations.
- Hybrid cloud and on-premise deployment using the Huawei stack.
Summary and Next Steps
Requirements
- General familiarity with AI workflows or platform architecture.
- A foundational understanding of model training and deployment processes.
- No prior practical experience with CANN or MindSpore is necessary.
Target Audience
- AI platform evaluators and infrastructure architects.
- AI/ML DevOps specialists and pipeline integrators.
- Technology managers and key decision-makers.
14 Hours
