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