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Duration 14 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware, including the 310, 910, and 910B models
- Examination of high-level components such as MindSpore, CANN, and AscendCL
- Analysis of industry positioning and core architecture principles
The Function of CANN within Huawei’s AI Stack
- Defining CANN: SDK objectives and internal structural layers
- Exploring ATC, TBE, and AscendCL for model compilation and execution
- Understanding how CANN facilitates inference optimisation and deployment
MindSpore: Overview and Structural Design
- Navigating training and inference workflows within MindSpore
- Insights into Graph mode, PyNative, and hardware abstraction layers
- Integration mechanisms with Ascend NPUs via the CANN backend
Managing the AI Lifecycle on Ascend: From Training to Deployment
- Creating models in MindSpore or converting them from alternative frameworks
- Processes for exporting and compiling models using ATC
- Deploying models on Ascend hardware using OM formats and AscendCL
Comparative Analysis with Alternative AI Stacks
- Contrasting MindSpore with PyTorch and TensorFlow regarding focus and market positioning
- Comparing deployment workflows on Ascend hardware versus GPU-based architectures
- Evaluating opportunities and constraints for enterprise adoption
Enterprise Integration Scenarios
- Application in smart manufacturing, government AI initiatives, and telecommunications
- Considerations for scalability, regulatory compliance, and ecosystem alignment
- Implementing cloud/on-premises hybrid deployments using the Huawei stack
Summary and Forward-Looking Steps
Requirements
- Familiarity with AI workflows or platform architecture
- Foundational knowledge of model training and deployment processes
- No prior practical experience with CANN or MindSpore is necessary
Target Audience
- AI platform evaluators and infrastructure architects
- AI/ML DevOps specialists and pipeline integrators
- Technology managers and key decision-makers
