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Duration 14 hours
Course Outline
Intro to Huawei’s AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B
- Key high-level components: MindSpore, CANN, and AscendCL
- Industry positioning and core architectural principles
The Function of CANN in Huawei’s AI Stack
- What is CANN? Understanding the SDK's purpose and internal layers
- ATC, TBE, and AscendCL: Compiling and running models
- How CANN aids in inference optimization and deployment
MindSpore: Overview and Architecture
- Training and inference workflows within MindSpore
- Graph mode, PyNative, and hardware abstraction mechanisms
- Connecting with the Ascend NPU through the CANN backend
AI Lifecycle on Ascend: From Training to Deployment
- Creating models in MindSpore or migrating from other frameworks
- Exporting and compiling models via ATC
- Deploying on Ascend hardware using OM models and AscendCL
Benchmarking Against Other AI Stacks
- MindSpore versus PyTorch and TensorFlow: Focus and market positioning
- Deployment workflows on Ascend compared to GPU-based stacks
- Prospects and constraints for enterprise adoption
Enterprise Integration Scenarios
- Applications in smart manufacturing, government AI, and telecommunications
- Considerations for scalability, compliance, and ecosystem alignment
- Hybrid cloud/on-prem deployments utilizing the Huawei stack
Wrap-up and Future Directions
Requirements
- General familiarity with AI workflows or platform architecture
- Foundational knowledge of model training and deployment concepts
- No prior hands-on experience with CANN or MindSpore is necessary
Target Audience
- AI platform evaluators and infrastructure architects
- AI/ML DevOps engineers and pipeline integrators
- Technology managers and strategic decision-makers
