Introduction to CANN for AI Framework Developers

• Basic understanding of machine learning and deep learning concepts
• Some programming experience with Python
• No prior experience with CANN or Ascend hardware required

Audience

• Machine learning developers exploring deployment workflows
• Students or researchers new to Huawei’s AI ecosystem
• AI framework contributors and hobbyists interested in model acceleration

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit used to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like MindSpore, TensorFlow, and PyTorch.

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

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

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 chips
• MindSpore, CANN, and supporting tools
• AI development workflow: training to deployment

Understanding the CANN Toolkit

• What is CANN and why it matters
• Overview of core components (ATC, AscendCL, operator libraries)
• Role of CANN in AI inference pipelines

Getting Started with MindSpore and CANN

• Setting up the environment (MindSpore + CANN + Python)
• Training a basic model in MindSpore
• Exporting and converting the model using ATC

Running Inference on Ascend Devices

• Using the OM model with AscendCL or Python APIs
• Basic input/output preprocessing
• Validating model outputs

Working with Other Frameworks

• Overview of support for TensorFlow, PyTorch, and ONNX
• Supported operators and limitations
• Simple model conversion demo (e.g., from ONNX to OM)

Exploring the CANN and MindSpore Developer Ecosystem

• Key resources: documentation, GitHub repositories, sample code
• MindSpore Hub and model zoo overview
• Community forums, events, and support channels

Summary and Next Steps