Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Huawei’s AI Ecosystem
- Overview of Ascend AI hardware: 310, 910, and 910B
- Key components: MindSpore, CANN, and AscendCL
- Market positioning and architectural principles
The Function of CANN in Huawei’s AI Stack
- Defining CANN: SDK objectives and internal structures
- ATC, TBE, and AscendCL: The mechanics of model compilation and execution
- Leveraging CANN for inference optimization and deployment
MindSpore: Overview and Architectural Design
- Training and inference workflows within MindSpore
- Graph mode, PyNative, and hardware abstraction layers
- Connecting with Ascend NPUs via the CANN backend
AI Lifecycle on Ascend: From Training to Deployment
- Developing models in MindSpore or migrating from other frameworks
- Exporting and compiling models using ATC
- Deploying on Ascend hardware via OM models and AscendCL
Comparative Analysis with Other AI Stacks
- MindSpore versus PyTorch and TensorFlow: Strategic focus and positioning
- Deployment workflows on Ascend compared to GPU-based stacks
- Evaluating opportunities and constraints for enterprise adoption
Enterprise Integration Scenarios
- Applications in smart manufacturing, government AI, and telecommunications
- Considering scalability, compliance, and ecosystem factors
- Hybrid cloud/on-premises deployment strategies using the Huawei stack
Conclusion and Future Directions
Requirements
- General familiarity with AI workflows or platform architecture
- Fundamental knowledge of model training and deployment processes
- No prior hands-on 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
