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Duration 14 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware, including the 310, 910, and 910B series.
- Key high-level components: MindSpore, CANN, and AscendCL.
- Market positioning and core architectural principles.
CANN's Functionality Within Huawei’s AI Stack
- Defining CANN: Exploring the SDK's purpose and its internal structure.
- Examining ATC, TBE, and AscendCL for model compilation and execution.
- How CANN enables inference optimization and efficient deployment.
MindSpore: Overview and Architectural Design
- Training and inference workflows within the MindSpore environment.
- Graph mode, PyNative, and the abstraction of hardware layers.
- Interfacing with Ascend NPUs through the CANN backend.
The AI Lifecycle on Ascend: From Training to Deployment
- Creating models in MindSpore or converting them from external frameworks.
- Model export and compilation processes using ATC.
- Deploying OM models and utilizing AscendCL on Ascend hardware.
Benchmarking Against Competing AI Stacks
- MindSpore versus PyTorch and TensorFlow: Analyzing focus and market position.
- Comparing deployment workflows on Ascend against GPU-based ecosystems.
- Identifying opportunities and constraints for enterprise adoption.
Enterprise Integration Use Cases
- Applications in smart manufacturing, government AI initiatives, and telecommunications.
- Considerations for scalability, regulatory compliance, and ecosystem support.
- Implementing cloud/on-premises hybrid deployments using the Huawei stack.
Key Takeaways and Future Directions
Requirements
- Working knowledge of AI workflows or platform architecture.
- Fundamental understanding 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 engineers and pipeline integration specialists.
- Technology leaders and strategic decision-makers.
