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Duration 14 hours
Course Outline
Introductory Overview of Huawei’s AI Ecosystem
- An overview of Ascend AI hardware, including models 310, 910, and 910B
- Key high-level components: MindSpore, CANN, and AscendCL
- Market positioning and core architectural principles
CANN’s Functionality Within Huawei’s AI Framework
- Defining CANN: SDK objectives and internal structural layers
- ATC, TBE, and AscendCL: The mechanics of model compilation and execution
- CANN’s contribution to inference optimization and deployment strategies
MindSpore: Architecture and Key Features
- Managing training and inference workflows within MindSpore
- Exploring graph mode, PyNative, and hardware abstraction layers
- Connecting with Ascend NPU through the CANN backend
Navigating the AI Lifecycle on Ascend: From Training to Deployment
- Building models in MindSpore or converting them from alternative frameworks
- Utilizing ATC for model export and compilation
- Deploying OM models and AscendCL onto Ascend hardware
Benchmarking Against Competing AI Stacks
- MindSpore versus PyTorch and TensorFlow: Comparing focus and market positioning
- Analyzing deployment workflows on Ascend hardware against GPU-based alternatives
- Identifying potential opportunities and constraints for enterprise adoption
Real-World Enterprise Integration Scenarios
- Application examples in smart manufacturing, public sector AI, and telecommunications
- Considerations regarding scalability, regulatory compliance, and ecosystem alignment
- Implementing hybrid cloud/on-premises deployments using the Huawei stack
Key Takeaways and Future Directions
Requirements
- A solid grasp of AI workflows or platform architecture
- Fundamental knowledge of model training and deployment processes
- No previous practical experience with CANN or MindSpore is necessary
Target Audience
- AI platform analysts and infrastructure architects
- AI/ML DevOps specialists and pipeline integration experts
- Technology leaders and strategic decision-makers
