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Duration 7 hours
Course Outline
An Overview of Huawei’s AI Ecosystem
- Ascend AI hardware, including the 310, 910, and 910B chips
- MindSpore, CANN, and their associated supporting tools
- The end-to-end AI development workflow, from training to deployment
Comprehending the CANN Toolkit
- Defining CANN and understanding its significance
- An overview of key components, such as ATC, AscendCL, and operator libraries
- The role CANN plays in AI inference pipelines
Initial Steps with MindSpore and CANN
- Environment setup involving MindSpore, CANN, and Python
- Training a fundamental model within MindSpore
- Exporting and converting the model using ATC
Executing Inference on Ascend Devices
- Utilizing OM models via AscendCL or Python APIs
- Implementing basic input and output preprocessing
- Verifying and validating model outputs
Integrating with Other Frameworks
- Support landscape for TensorFlow, PyTorch, and ONNX
- Discussion of supported operators and known limitations
- A practical demonstration of model conversion (e.g., from ONNX to OM)
Navigating the CANN and MindSpore Developer Ecosystem
- Accessing key resources, including documentation, GitHub repositories, and sample code
- An overview of MindSpore Hub and the model zoo
- Engaging with community forums, events, and support channels
Recap and Recommended Next Steps
Requirements
- A fundamental understanding of machine learning and deep learning concepts
- Basic programming proficiency in Python
- No prior 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 interested in model acceleration
