Understanding Huawei’s AI Compute Stack: From CANN to MindSpore

• Familiarity with AI workflows or platform architecture
• Basic understanding of model training and deployment
• No prior hands-on experience with CANN or MindSpore required

Audience

• AI platform evaluators and infrastructure architects
• AI/ML DevOps and pipeline integrators
• Technology managers and decision-makers

Huawei’s AI stack — from the low-level CANN SDK to the high-level MindSpore framework — offers a tightly integrated AI development and deployment environment optimized for Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to understand how the CANN and MindSpore components work together to support AI lifecycle management and infrastructure decisions.

By the end of this training, participants will be able to:

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN supports model optimization and hardware-level deployment.
• Evaluate the MindSpore framework and toolchain in relation to industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-prem environments.

Format of the Course

• Interactive lecture and discussion.
• Live system demos and case-based walkthroughs.
• Optional guided labs on model flow from MindSpore to CANN.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Huawei’s AI Ecosystem

• Ascend AI hardware: 310, 910, and 910B overview
• High-level components: MindSpore, CANN, AscendCL
• Industry positioning and architecture principles

The Role of CANN in Huawei’s AI Stack

• What is CANN? SDK purpose and internal layers
• ATC, TBE, and AscendCL: compiling and executing models
• How CANN supports inference optimization and deployment

MindSpore Overview and Architecture

• Training and inference workflows in MindSpore
• Graph mode, PyNative, and hardware abstraction
• Integration with Ascend NPU via CANN backend

AI Lifecycle on Ascend: Training to Deployment

• Model creation in MindSpore or conversion from other frameworks
• Exporting and compiling models using ATC
• Deployment on Ascend hardware using OM models and AscendCL

Comparison with Other AI Stacks

• MindSpore vs. PyTorch, TensorFlow: focus and positioning
• Deployment workflows on Ascend vs. GPU-based stacks
• Opportunities and limitations for enterprise use

Enterprise Integration Scenarios

• Use cases in smart manufacturing, government AI, and telecom
• Scalability, compliance, and ecosystem considerations
• Cloud/on-prem hybrid deployment using Huawei stack

Summary and Next Steps