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Duration 14 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware, including models 310, 910, and 910B
- Key components: MindSpore, CANN, and AscendCL
- Industry standing and core architectural principles
The Function of CANN within Huawei's AI Stack
- Defining CANN: The purpose of the SDK and its internal structure
- The roles of ATC, TBE, and AscendCL in compiling and running models
- How CANN enhances inference optimization and deployment processes
MindSpore: Overview and Structural Design
- Training and inference pathways within MindSpore
- Graph mode, PyNative, and the abstraction of hardware
- Connecting to the Ascend NPU through the CANN backend
AI Lifecycle on Ascend: From Training to Live Deployment
- Building models in MindSpore or converting them from external frameworks
- 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: Distinct focuses and market positioning
- Deployment workflows on Ascend versus GPU-centric architectures
- Potential advantages and constraints for enterprise adoption
Scenarios for Enterprise Integration
- Applications in smart manufacturing, government AI, and telecommunications
- Considerations regarding scalability, regulatory compliance, and ecosystem support
- Hybrid cloud/on-premise deployment strategies using the Huawei stack
Recap and Future Directions
Requirements
- General knowledge of AI workflows or platform architecture is beneficial.
- A fundamental grasp of model training and deployment concepts.
- No prior hands-on experience with CANN or MindSpore is necessary.
Target Participants
- AI platform assessors and infrastructure architects.
- AI/ML DevOps specialists and pipeline integration experts.
- Technology leaders and strategic decision-makers.
