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Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI processors: 310, 910, and 910B
- Key high-level elements: MindSpore, CANN, and AscendCL
- Market positioning and core architectural principles
CANN’s Function within Huawei’s AI Hierarchy
- Defining CANN: SDK objectives and internal structural layers
- ATC, TBE, and AscendCL: Mechanisms for compiling and running models
- How CANN enables inference optimization and system deployment
MindSpore: Architecture and Overview
- Training and inference pipelines in MindSpore
- Graph mode, PyNative, and hardware abstraction techniques
- Connecting to the Ascend NPU through the CANN backend
The AI Lifecycle on Ascend: From Training to Deployment
- Creating models in MindSpore or importing them from external frameworks
- Compiling and exporting models utilizing ATC
- Deploying on Ascend hardware via OM models and AscendCL
Benchmarking Against Other AI Stacks
- MindSpore compared with PyTorch and TensorFlow: Strategic focus and market role
- Deployment procedures on Ascend versus GPU-centric stacks
- Potential benefits and constraints for enterprise adoption
Enterprise Integration Use Cases
- Applications in intelligent manufacturing, public sector AI, and telecommunications
- Considerations regarding scalability, regulatory compliance, and ecosystem alignment
- Hybrid cloud/on-premise deployment strategies using the Huawei stack
Recap and Future Pathways
Requirements
- General knowledge of AI workflows or platform structures
- Fundamental comprehension of model training and deployment processes
- No existing practical experience with CANN or MindSpore is necessary
Target Audience
- AI platform assessors and infrastructure designers
- AI/ML DevOps specialists and pipeline integration experts
- Technology leaders and strategic decision-makers
14 Hours
