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Course Outline

Introduction to CANN and Ascend AI Processors

  • Definition of CANN and its role within Huawei’s AI compute stack.
  • Overview of Ascend processor architecture (including models like 310 and 910).
  • Survey of supported AI frameworks and the corresponding toolchain.

Model Conversion and Compilation

  • Utilizing the ATC tool for model conversion from TensorFlow, PyTorch, and ONNX.
  • Creating and validating OM model files.
  • Addressing unsupported operators and resolving common conversion issues.

Deploying with MindSpore and Other Frameworks

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

Performance Optimization and Profiling

  • Exploring optimizations related to AI Core, memory management, and tiling.
  • Profiling model execution through CANN tools.
  • Best practices for enhancing inference speed and resource efficiency.

Error Handling and Debugging

  • Identifying common deployment errors and their resolutions.
  • Interpreting logs and utilizing the error diagnosis tool.
  • Conducting 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.
  • Examining real-world case studies in computer vision and NLP.

Summary and Next Steps

Requirements

  • Experience with Python-based deep learning frameworks such as TensorFlow or PyTorch.
  • A solid understanding of neural network architectures and model training workflows.
  • Basic familiarity with the Linux command-line interface (CLI) and scripting.

Target Audience

  • AI engineers focused on model deployment.
  • Machine learning practitioners seeking hardware acceleration.
  • Deep learning developers constructing inference solutions.
 14 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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