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Course Outline
Introduction to Huawei's AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B.
- Key components: MindSpore, CANN, and AscendCL.
- Industry positioning and core architectural principles.
CANN's Function in Huawei's AI Stack
- Defining CANN: SDK purpose and internal layering.
- ATC, TBE, and AscendCL: The process of compiling and executing models.
- How CANN enables inference optimization and deployment.
MindSpore Architecture and Overview
- Training and inference workflows within MindSpore.
- Graph mode, PyNative, and hardware abstraction layers.
- Integration with Ascend NPUs via the CANN backend.
AI Lifecycle on Ascend: From Training to Deployment
- Creating models in MindSpore or converting them from other frameworks.
- Exporting and compiling models using ATC.
- Deploying on Ascend hardware using OM models and AscendCL.
Comparing Huawei with Other AI Stacks
- MindSpore vs. PyTorch and TensorFlow: Differences in focus and positioning.
- Deployment workflows on Ascend compared to GPU-based stacks.
- Enterprise opportunities and associated limitations.
Enterprise Integration Scenarios
- Use cases in smart manufacturing, government AI, and telecommunications.
- Considerations for scalability, compliance, and ecosystem integration.
- Hybrid cloud/on-premises deployment utilizing the Huawei stack.
Summary and Recommended Next Steps
Requirements
- Familiarity with AI workflows or platform architectures.
- Fundamental knowledge of model training and deployment processes.
- No prior hands-on experience with CANN or MindSpore is necessary.
Target Audience
- AI platform evaluators and infrastructure architects.
- AI/ML DevOps specialists and pipeline integrators.
- Technology managers and strategic decision-makers.
14 Hours
