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

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