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Course Outline
Introduction to Huawei’s AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips.
- MindSpore, CANN, and supporting tools.
- AI development workflow: from training to deployment.
Understanding the CANN Toolkit
- What CANN is and why it matters.
- Overview of core components (ATC, AscendCL, operator libraries).
- The 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
Requirements
- A fundamental understanding of machine learning and deep learning concepts.
- Some programming experience in Python.
- No prior exposure to CANN or Ascend hardware is required.
Audience
- Machine learning developers investigating deployment workflows.
- Students or researchers new to Huawei’s AI ecosystem.
- AI framework contributors and hobbyists interested in model acceleration.
7 Hours
