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Course Outline
Introduction to CANN and Ascend AI Processors for Government
- Overview of CANN: Its Role in Huawei’s AI Compute Stack for Government
- Detailed Architecture of Ascend Processors (310, 910, etc.) for Government Applications
- Supported AI Frameworks and Toolchain Overview for Government Use
Model Conversion and Compilation for Government
- Utilizing the ATC Tool for Model Conversion (TensorFlow, PyTorch, ONNX) in Government Projects
- Creating and Validating OM Model Files for Government Deployments
- Addressing Unsupported Operators and Common Conversion Issues for Government Users
Deploying with MindSpore and Other Frameworks for Government
- Deploying Models with MindSpore Lite in Government Environments
- Integrating OM Models with Python APIs or C++ SDKs for Government Applications
- Utilizing the Ascend Model Manager for Government Projects
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 Enhancing Inference Speed and Resource Usage in Government Deployments
Error Handling and Debugging for Government
- Common Deployment Errors and Their Resolution for Government Applications
- Reading Logs and Using the Error Diagnosis Tool for Government Users
- Conducting Unit Testing and Functional Validation of Deployed Models in Government Environments
Edge and Cloud Deployment Scenarios for Government
- Deploying to Ascend 310 for Edge Applications in Government Settings
- Integrating with Cloud-Based APIs and Microservices for Government Operations
- Real-World Case Studies in Computer Vision and NLP for Government Use Cases
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 efficient data processing
Audience
- AI engineers working on model deployment in government agencies
- Machine learning practitioners focusing on hardware acceleration for public sector projects
- Deep learning developers building inference solutions for government use
14 Hours