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Duration 14 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B
- 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: The purpose of the SDK and its internal structure
- ATC, TBE, and AscendCL: The processes of compiling and running models
- How CANN facilitates inference optimization and deployment
MindSpore: Architecture and Overview
- Training and inference workflows within MindSpore
- Graph mode, PyNative, and hardware abstraction layers
- Connecting with the Ascend NPU through the CANN backend
The AI Lifecycle on Ascend: From Training to Deployment
- Creating models in MindSpore or importing them from other frameworks
- Exporting and compiling models via ATC
- Deploying to Ascend hardware using OM models and AscendCL
Benchmarking Against Other AI Stacks
- MindSpore versus PyTorch and TensorFlow: Comparative focus and positioning
- Deployment workflows on Ascend hardware compared to GPU-centric stacks
- Potential benefits and constraints for enterprise adoption
Scenarios for Enterprise Integration
- Applications in smart manufacturing, government AI initiatives, and telecommunications
- Considerations regarding scalability, compliance, and ecosystem integration
- Hybrid cloud and on-premise deployments leveraging 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
Target Audience
- Professionals evaluating AI platforms and architects of infrastructure
- AI/ML DevOps specialists and pipeline integration experts
- Technology leaders and strategic decision-makers
