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Duration 14 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B series.
- Key high-level components: MindSpore, CANN, and AscendCL.
- Market positioning and core architectural principles.
The Function of CANN within Huawei’s AI Stack
- Defining CANN: SDK objectives and internal structural layers.
- ATC, TBE, and AscendCL: processes for compiling and executing models.
- How CANN enhances inference optimization and deployment strategies.
MindSpore: Overview and Architectural Design
- Managing training and inference workflows within MindSpore.
- Understanding Graph mode, PyNative, and hardware abstraction layers.
- Connecting with Ascend NPUs through the CANN backend.
Managing the AI Lifecycle on Ascend: From Training to Deployment
- Building models in MindSpore or converting them from other frameworks.
- Exporting and compiling models utilizing ATC.
- Deploying on Ascend hardware via OM models and AscendCL.
Benchmarking Against Other AI Stacks
- MindSpore compared to PyTorch and TensorFlow: strategic focus and positioning.
- Deployment workflows on Ascend versus traditional GPU-based stacks.
- Exploring opportunities and constraints for enterprise adoption.
Enterprise Integration Scenarios
- Application cases in smart manufacturing, government AI, and telecommunications.
- Considering scalability, compliance, and broader ecosystem factors.
- Implementing cloud/on-premise hybrid deployments using the Huawei stack.
Recap and Recommended Next Steps
Requirements
- General knowledge of AI workflows or platform architecture.
- A foundational understanding of model training and deployment concepts.
- 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 senior decision-makers.
