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Duration 14 hours
Course Outline
Introductory Perspectives on Huawei’s AI Ecosystem
- Overview of Ascend AI hardware: Models 310, 910, and 910B.
- Key high-level elements: MindSpore, CANN, and AscendCL.
- Industry standing and core architectural principles.
CANN’s Function Within Huawei’s AI Architecture
- Defining CANN: The purpose of the SDK and its internal structure.
- ATC, TBE, and AscendCL: The mechanisms for compiling and running models.
- How CANN enables inference optimization and efficient deployment.
MindSpore: An Overview of Design and Structure
- Managing training and inference workflows within MindSpore.
- Graph mode, PyNative, and the abstraction of hardware capabilities.
- Connecting with Ascend NPUs through the CANN backend.
AI Lifecycle on Ascend: From Training to Production
- Building models in MindSpore or importing them from other frameworks.
- The process of exporting and compiling models via ATC.
- Deploying on Ascend hardware utilizing OM models and AscendCL.
Benchmarking Against Other AI Stacks
- MindSpore compared to PyTorch and TensorFlow: Strategic focus and market positioning.
- Deployment pathways on Ascend versus traditional GPU-based stacks.
- Identifying opportunities and constraints for enterprise adoption.
Scenarios for Enterprise Integration
- Application in smart manufacturing, government AI initiatives, and telecommunications.
- Factors regarding scalability, regulatory compliance, and ecosystem health.
- Hybrid cloud and on-premise deployment strategies using the Huawei stack.
Conclusion and Future Directions
Requirements
- General familiarity with AI workflows or platform architecture concepts.
- A foundational grasp 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 integration experts.
- Technology leaders and strategic decision-makers.
