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Duration 7 hours
Course Outline
Overview of Huawei’s AI Ecosystem
- Ascend AI hardware: details on 310, 910, and 910B chips
- MindSpore, CANN, and auxiliary tools
- The AI development journey: from training to deployment
Understanding the CANN Toolkit
- Defining CANN and its significance
- Key components at a glance (ATC, AscendCL, operator libraries)
- The function of CANN within AI inference pipelines
Initiating Work with MindSpore and CANN
- Configuring the environment (MindSpore + CANN + Python)
- Training a fundamental model using MindSpore
- Exporting and transforming the model via ATC
Executing Inference on Ascend Devices
- Utilizing OM models through AscendCL or Python APIs
- Basic input/output preprocessing techniques
- Verifying model outputs
Interfacing with Other Frameworks
- Support landscape for TensorFlow, PyTorch, and ONNX
- Compatible operators and known constraints
- Demonstration of a simple model conversion (e.g., ONNX to OM)
Navigating the CANN and MindSpore Developer Community
- Essential resources: documentation, GitHub repositories, and code samples
- Introduction to MindSpore Hub and the model zoo
- Community forums, events, and support avenues
Recap and Future Directions
Requirements
- A fundamental grasp of machine learning and deep learning principles.
- Familiarity with Python programming.
- No previous experience with CANN or Ascend hardware is necessary.
Target Audience
- Machine learning engineers looking to explore deployment processes.
- Students or researchers new to Huawei’s AI landscape.
- AI framework contributors and enthusiasts interested in accelerating model performance.
