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Duration 14 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B variants
- Key high-level components: MindSpore, CANN, and AscendCL
- Strategic industry positioning and core architectural principles
The Function of CANN within Huawei's AI Stack
- Defining CANN: The purpose of the SDK and its internal layers
- ATC, TBE, and AscendCL: Processes for compiling and running models
- How CANN facilitates inference optimization and deployment tasks
MindSpore: Architecture and Overview
- Training and inference workflows facilitated by MindSpore
- Graph mode, PyNative, and the approach to hardware abstraction
- Connecting with Ascend NPU through the CANN backend
AI Lifecycle on Ascend: From Training to Deployment
- Creating models in MindSpore or converting them from external frameworks
- Exporting and compiling models utilizing ATC
- Deploying on Ascend hardware using OM models and AscendCL
Comparative Analysis with Other AI Stacks
- MindSpore versus PyTorch and TensorFlow: Distinct focuses and market positioning
- Deployment workflows on Ascend compared to GPU-centric stacks
- Identifying opportunities and constraints for enterprise adoption
Enterprise Integration Use Cases
- Applications in smart manufacturing, government AI initiatives, and telecommunications
- Considerations regarding scalability, compliance, and ecosystem support
- Implementing cloud/on-premises hybrid deployments with the Huawei stack
Concluding Summary and Recommended Next Steps
Requirements
- General knowledge of AI workflows or platform architecture
- Foundational understanding of model training and deployment processes
- No prior practical experience with CANN or MindSpore is necessary
Intended Audience
- AI platform evaluators and infrastructure architects
- AI/ML DevOps engineers and pipeline integration specialists
- Technology leaders and strategic decision-makers
