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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.
- The AI development cycle: from training to deployment.
Grasping the CANN Toolkit
- The definition and significance of CANN.
- An overview of core components (ATC, AscendCL, operator libraries).
- The function of CANN within AI inference pipelines.
Initial Steps with MindSpore and CANN
- Environment setup (MindSpore + CANN + Python).
- Training a basic 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.
Integrating Other Frameworks
- Support overview for TensorFlow, PyTorch, and ONNX.
- Compatible operators and associated constraints.
- A simple model conversion demonstration (e.g., from ONNX to OM).
Delving into the CANN and MindSpore Developer Ecosystem
- Essential resources: documentation, GitHub repositories, and sample code.
- An overview of MindSpore Hub and the model zoo.
- Community forums, events, and support channels.
Recap and Future Directions
Requirements
- A fundamental grasp of machine learning and deep learning principles.
- Proficiency in Python programming.
- No prior hands-on experience with CANN or Ascend hardware is necessary.
Target Audience
- Machine learning developers seeking to explore deployment workflows.
- Students or researchers newly entering Huawei’s AI ecosystem.
- AI framework contributors and enthusiasts keen on model acceleration.
