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Duration 7 hours
Course Outline
Overview of Huawei’s AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and auxiliary tools
- AI development lifecycle: from training to deployment
Grasping the CANN Toolkit
- Definition of CANN and its significance
- Snapshot of core components (ATC, AscendCL, operator libraries)
- The function of CANN within AI inference pipelines
Initializing with MindSpore and CANN
- Establishing the environment (MindSpore + CANN + Python)
- Training a fundamental model using MindSpore
- Exporting and converting the model via ATC
Executing Inference on Ascend Devices
- Utilizing the OM model through AscendCL or Python APIs
- Basic input/output preprocessing techniques
- Verification of model outputs
Interoperating with Other Frameworks
- Support landscape for TensorFlow, PyTorch, and ONNX
- Compatible operators and associated constraints
- A simple demonstration of model conversion (e.g., from ONNX to OM)
Discovering the CANN and MindSpore Developer Ecosystem
- Essential resources: documentation, GitHub repositories, and sample code
- Introduction to MindSpore Hub and the model zoo
- Community forums, events, and support channels
Recap and Future Steps
Requirements
- A fundamental grasp of machine learning and deep learning principles
- Familiarity with programming in Python
- No previous hands-on 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 keen on model acceleration
