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Course Outline
Introduction to Huawei’s AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and auxiliary tools
- The AI development lifecycle: from training to deployment
Understanding the CANN Toolkit
- Defining CANN and its significance
- An overview of core components (ATC, AscendCL, operator libraries)
- CANN’s role in AI inference pipelines
Getting Started with MindSpore and CANN
- Environment setup (MindSpore + CANN + Python)
- Training a basic model within MindSpore
- Exporting and converting the model using ATC
Running Inference on Ascend Devices
- Utilising the OM model via AscendCL or Python APIs
- Basic input and output preprocessing
- Validating model outputs
Working with Other Frameworks
- Overview of support for TensorFlow, PyTorch, and ONNX
- Supported operators and associated limitations
- A simple model conversion demonstration (e.g., from ONNX to OM)
Exploring the CANN and MindSpore Developer Ecosystem
- Key resources: documentation, GitHub repositories, and sample code
- MindSpore Hub and model zoo overview
- Community forums, events, and support channels
Summary and Next Steps
Requirements
- A fundamental grasp of machine learning and deep learning principles
- Proficiency in Python programming
- No prior experience with CANN or Ascend hardware is necessary
Target Audience
- Machine learning developers investigating deployment workflows
- Students or researchers new to Huawei’s AI ecosystem
- AI framework contributors and enthusiasts interested in model acceleration
7 Hours
