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Duration 7 hours
Course Outline
Introduction to Huawei’s AI Environment
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and ancillary tools
- AI development process: from training to deployment
Grasping the CANN Toolkit
- Defining CANN and its significance
- Overview of primary components (ATC, AscendCL, operator libraries)
- CANN’s function in AI inference pipelines
Initiating Work with MindSpore and CANN
- Configuring the environment (MindSpore + CANN + Python)
- Training a fundamental model in MindSpore
- Exporting and transforming the model via ATC
Executing Inference on Ascend Devices
- Leveraging the OM model through AscendCL or Python APIs
- Basic input/output data preparation
- Verifying model results
Collaborating with Alternative Frameworks
- Support status for TensorFlow, PyTorch, and ONNX
- Compatible operators and existing constraints
- Demonstration of simple model conversion (e.g., from ONNX to OM)
Exploring the CANN and MindSpore Developer Community
- Essential resources: documentation, GitHub repositories, example code
- Overview of MindSpore Hub and model collection
- Community forums, events, and support avenues
Recap and Future Directions
Requirements
- Foundational 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 developers investigating deployment processes
- Students or researchers new to Huawei’s AI environment
- AI framework contributors and enthusiasts interested in accelerating model performance
