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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 associated tools.
- AI development process: from training to deployment.
Grasping the CANN Toolkit
- Definition of CANN and its significance.
- Summary of core elements (ATC, AscendCL, operator libraries).
- The function of CANN within AI inference pipelines.
Initiation with MindSpore and CANN
- Configuring 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.
- Verifying model outputs.
Integration with Other Frameworks
- Summary of compatibility for TensorFlow, PyTorch, and ONNX.
- Compatible operators and potential constraints.
- A basic model conversion demonstration (e.g., from ONNX to OM).
Investigating the CANN and MindSpore Developer Community
- Essential resources: documentation, GitHub repositories, and sample code.
- MindSpore Hub and model zoo highlights.
- Community forums, events, and support channels.
Recap and Future Directions
Requirements
- Fundamental grasp of machine learning and deep learning principles.
- Some coding proficiency in Python.
- No prior hands-on experience with CANN or Ascend hardware is necessary.
Target Audience
- Machine learning engineers investigating deployment processes.
- Students or researchers newly entering Huawei’s AI ecosystem.
- AI framework contributors and enthusiasts with an interest in model acceleration.
