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Course Outline
Introduction to CANN and Ascend AI Processors for Government
- What is CANN? Its Role in Huawei’s AI Compute Stack for Government Applications
- Overview of Ascend Processor Architecture (310, 910, etc.) for Government Use
- Supported AI Frameworks and Toolchain Overview for Government Projects
Model Conversion and Compilation for Government
- Using the ATC Tool for Model Conversion (TensorFlow, PyTorch, ONNX) in Government Environments
- Creating and Validating OM Model Files for Government Systems
- Handling Unsupported Operators and Common Conversion Issues for Government Applications
Deploying with MindSpore and Other Frameworks for Government
- Deploying Models with MindSpore Lite in Government Projects
- Integrating OM Models with Python APIs or C++ SDKs for Government Use
- Working with Ascend Model Manager for Government Applications
Performance Optimization and Profiling for Government
- Understanding AI Core, Memory, and Tiling Optimizations for Government Systems
- Profiling Model Execution with CANN Tools for Government Use
- Best Practices for Improving Inference Speed and Resource Usage in Government Applications
Error Handling and Debugging for Government
- Common Deployment Errors and Their Resolution for Government Systems
- Reading Logs and Using the Error Diagnosis Tool for Government Applications
- Unit Testing and Functional Validation of Deployed Models in Government Projects
Edge and Cloud Deployment Scenarios for Government
- Deploying to Ascend 310 for Edge Applications in Government Settings
- Integration with Cloud-Based APIs and Microservices for Government Use
- Real-World Case Studies in Computer Vision and NLP for Government Projects
Summary and Next Steps for Government
Requirements
- Experience with Python-based deep learning frameworks such as TensorFlow or PyTorch for government applications
- Understanding of neural network architectures and model training workflows in a public sector context
- Basic familiarity with Linux command-line interface (CLI) and scripting for government systems
Audience
- AI engineers working on model deployment for government projects
- Machine learning practitioners focusing on hardware acceleration for government initiatives
- Deep learning developers building inference solutions for government use
14 Hours
