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Duration 14 hours
Course Outline
Overview of Huawei's AI Ecosystem
- Introduction to Ascend AI hardware: 310, 910, and 910B
- Key high-level elements: MindSpore, CANN, and AscendCL
- Market positioning and foundational architecture concepts
CANN's Function within Huawei's AI Framework
- Defining CANN: SDK objectives and internal structural layers
- ATC, TBE, and AscendCL: The mechanics of model compilation and execution
- Leveraging CANN for enhanced inference optimization and deployment
MindSpore Framework: Architecture and 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 Production
- Building models in MindSpore or migrating them from external frameworks
- Model export and compilation processes utilizing ATC
- Deploying on Ascend hardware using OM models and AscendCL
Benchmarking Against Competing AI Stacks
- MindSpore versus PyTorch and TensorFlow: Strategic focus and market positioning
- Deployment pathways on Ascend compared to GPU-centric ecosystems
- Analyzing opportunities and constraints for enterprise adoption
Real-World Enterprise Integration
- Applications in smart manufacturing, government AI initiatives, and telecommunications
- Considerations regarding scalability, regulatory compliance, and ecosystem fit
- Implementing hybrid cloud/on-prem solutions using the Huawei stack
Conclusions and Future Directions
Requirements
- General knowledge of AI workflows or platform structures
- Fundamental grasp of model training and deployment processes
- No existing hands-on proficiency with CANN or MindSpore is necessary
Target Audience
- AI platform assessors and infrastructure architects
- AI/ML DevOps specialists and pipeline integration experts
- Technology leaders and strategic decision-makers
