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
An Introduction to Huawei's AI Environment
- Overview of Ascend AI hardware: 310, 910, and 910B series
- Key components: MindSpore, CANN, and AscendCL
- Market positioning and core architectural principles
The Function of CANN in Huawei's AI Ecosystem
- Defining CANN: SDK objectives and internal structure
- ATC, TBE, and AscendCL: processes for compiling and running models
- How CANN enables inference optimization and deployment
MindSpore: Overview and Design
- Workflows for training and inference within MindSpore
- Graph mode, PyNative, and hardware abstraction layers
- Connectivity to Ascend NPUs through the CANN backend
The AI Lifecycle on Ascend: From Training to Production
- Building models in MindSpore or converting them from other frameworks
- Exporting and compiling models via ATC
- Deploying on Ascend hardware using OM models and AscendCL
Benchmarking Against Other AI Stacks
- MindSpore compared to PyTorch and TensorFlow: strategic focus and roles
- Deployment processes on Ascend versus GPU-centric architectures
- Prospects and constraints for enterprise adoption
Real-World Enterprise Integration Examples
- Applications in smart manufacturing, public sector AI, and telecommunications
- Considerations for scalability, regulatory compliance, and ecosystem health
- Hybrid cloud/on-premise deployments leveraging the Huawei stack
Conclusion and Future Directions
Requirements
- General knowledge of AI workflows or platform structures
- Foundational understanding of model training and deployment processes
- No previous practical experience with CANN or MindSpore is necessary
Target Audience
- Specialists evaluating AI platforms and designing infrastructure
- AI/ML DevOps experts and pipeline integration specialists
- Technology leaders and strategic decision-makers
