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Duration 14 hours
Course Outline
Introduction to Huawei’s AI Ecosystem
- Overview of Ascend AI hardware, including 310, 910, and 910B chips
- High-level components: MindSpore, CANN, and AscendCL
- Architectural principles and market positioning
The Role of CANN in Huawei’s AI Stack
- Defining CANN: SDK objectives and internal layering
- ATC, TBE, and AscendCL: Mechanisms for model compilation and execution
- Leveraging CANN for inference optimization and deployment tasks
MindSpore Overview and Architecture
- Workflows for training and inference within MindSpore
- Graph mode, PyNative, and hardware abstraction techniques
- Connecting with Ascend NPU through the CANN backend
AI Lifecycle on Ascend: From Training to Deployment
- Building models in MindSpore or converting them from other frameworks
- Exporting and compiling models utilizing ATC
- Deploying on Ascend hardware via OM models and AscendCL
Comparison with Other AI Stacks
- MindSpore vs. PyTorch and TensorFlow: Distinct focuses and market roles
- Deployment workflows on Ascend compared to GPU-based alternatives
- Enterprise opportunities and potential limitations
Enterprise Integration Scenarios
- Applications in smart manufacturing, government AI, and telecommunications
- Considerations regarding scalability, compliance, and ecosystem health
- Hybrid cloud/on-prem deployment strategies using the Huawei stack
Summary and Next Steps
Requirements
- General knowledge of AI workflows or platform architecture
- Foundational grasp of model training and deployment processes
- Prior hands-on experience with CANN or MindSpore is not a requirement
Target Audience
- AI platform evaluators and infrastructure architects
- AI/ML DevOps engineers and pipeline integration specialists
- Technology leaders and strategic decision-makers
