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Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B series
- Key high-level elements: MindSpore, CANN, and AscendCL
- Architectural principles and industry standing
The Function of CANN within Huawei's AI Stack
- Defining CANN: SDK objectives and internal structural layers
- ATC, TBE, and AscendCL: processes for compiling and executing models
- How CANN enables inference enhancement and streamlined deployment
MindSpore: Overview and Structural Design
- Training and inference workflows facilitated by MindSpore
- Graph mode, PyNative, and hardware abstraction techniques
- Integration with Ascend NPU through the CANN backend
AI Lifecycle on Ascend: From Training to Deployment
- Model development in MindSpore or migration from alternative frameworks
- Model export and compilation utilizing ATC
- Deployment on Ascend hardware leveraging OM models and AscendCL
Benchmarking Against Other AI Stacks
- MindSpore vs. PyTorch and TensorFlow: distinct focuses and market positions
- Deployment pipelines on Ascend versus GPU-centric stacks
- Enterprise opportunities and inherent limitations
Enterprise Integration Scenarios
- Applications in smart manufacturing, government AI, and telecommunications
- Considerations regarding scalability, compliance, and ecosystem dynamics
- Hybrid cloud/on-premise deployment strategies using the Huawei stack
Conclusions and Future Directions
Requirements
- General awareness of AI workflows or platform structures
- Fundamental grasp of model training and deployment processes
- No previous hands-on experience 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
14 Hours