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Course Outline
Introduction to Huawei’s AI Ecosystem for Government
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and supporting tools
- AI development workflow from training to deployment for government applications
Understanding the CANN Toolkit for Government
- Overview of CANN and its significance for government use cases
- Core components: ATC, AscendCL, and operator libraries
- The role of CANN in AI inference pipelines for government operations
Getting Started with MindSpore and CANN for Government
- Setting up the environment: MindSpore, CANN, and Python for government systems
- Training a basic model in MindSpore for government applications
- Exporting and converting the model using ATC for deployment in government settings
Running Inference on Ascend Devices for Government
- Utilizing OM models with AscendCL or Python APIs for government tasks
- Basic input/output preprocessing for government data
- Validating model outputs in a government context
Working with Other Frameworks for Government
- Support for TensorFlow, PyTorch, and ONNX in government projects
- Supported operators and limitations for government use
- Simple model conversion demo: from ONNX to OM for government applications
Exploring the CANN and MindSpore Developer Ecosystem for Government
- Key resources: documentation, GitHub repositories, and sample code for government developers
- Overview of MindSpore Hub and model zoo for government use
- Community forums, events, and support channels for government users
Summary and Next Steps for Government
Requirements
- A foundational understanding of machine learning and deep learning principles
- Prior programming experience with Python
- No previous knowledge of CANN or Ascend hardware is necessary
Audience for Government
- Machine learning developers investigating deployment workflows for government applications
- Students or researchers new to Huawei’s AI ecosystem and its relevance to public sector projects
- AI framework contributors and enthusiasts interested in model acceleration techniques for government use
7 Hours