Cursusaanbod

Introduction to CANN and Ascend AI Processors

  • What is CANN? Role in Huawei’s AI compute stack
  • Overview of Ascend processor architecture (310, 910, etc.)
  • Supported AI frameworks and toolchain overview

Model Conversion and Compilation

  • Using the ATC tool for model conversion (TensorFlow, PyTorch, ONNX)
  • Creating and validating OM model files
  • Handling unsupported operators and common conversion issues

Deploying with MindSpore and Other Frameworks

  • Deploying models with MindSpore Lite
  • Integrating OM models with Python APIs or C++ SDKs
  • Working with Ascend Model Manager

Performance Optimization and Profiling

  • Understanding AI Core, memory, and tiling optimizations
  • Profiling model execution with CANN tools
  • Best practices for improving inference speed and resource usage

Error Handling and Debugging

  • Common deployment errors and their resolution
  • Reading logs and using the error diagnosis tool
  • Unit testing and functional validation of deployed models

Edge and Cloud Deployment Scenarios

  • Deploying to Ascend 310 for edge applications
  • Integration with cloud-based APIs and microservices
  • Real-world case studies in computer vision and NLP

Summary and Next Steps

Vereisten

  • Experience with Python-based deep learning frameworks such as TensorFlow or PyTorch
  • Understanding of neural network architectures and model training workflows
  • Basic familiarity with Linux CLI and scripting

Audience

  • AI engineers working with model deployment
  • Machine learning practitioners targeting hardware acceleration
  • Deep learning developers building inference solutions
 14 Uren

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