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Course Outline
Intro to the Huawei AI Ecosystem
- Ascend AI processors: An overview of models 310, 910, and 910B
- Key components: MindSpore, CANN, and AscendCL
- Market positioning and core architectural tenets
The Function of CANN within Huawei's AI Stack
- Defining CANN: Purpose of the SDK and its internal hierarchy
- ATC, TBE, and AscendCL: Processes for compiling and running models
- Supporting inference efficiency and deployment via CANN
Introduction to MindSpore and its Design
- Workflows for training and inference within MindSpore
- Graph mode, PyNative, and hardware abstraction layers
- Connecting to Ascend NPUs through the CANN backend
AI Lifecycle on Ascend: From Training to Deployment
- Building models in MindSpore or migrating from other frameworks
- Exporting and compiling models utilizing ATC
- Deploying on Ascend hardware via OM models and AscendCL
Benchmarking Against Other AI Ecosystems
- MindSpore compared to PyTorch and TensorFlow: Objectives and market niche
- Deployment processes on Ascend versus GPU-centric stacks
- Prospects and constraints for enterprise adoption
Enterprise Integration Cases
- Applications in smart manufacturing, government AI, and telecommunications
- Considerations regarding scalability, regulatory compliance, and ecosystem health
- Hybrid cloud/on-prem deployments leveraging the Huawei stack
Recap and Future Directions
Requirements
- General knowledge of AI workflows or platform design
- Foundational grasp of model training and deployment processes
- No specific prior experience with CANN or MindSpore is necessary
Target Audience
- Professionals evaluating AI platforms and infrastructure architects
- AI/ML DevOps specialists and pipeline integration experts
- Technology leaders and strategic decision-makers
14 Hours