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Course Outline

Introduction to Huawei’s AI Ecosystem

  • Ascend AI hardware: details on the 310, 910, and 910B chips.
  • Overview of MindSpore, CANN, and associated supporting tools.
  • The AI development workflow, from training to deployment.

Understanding the CANN Toolkit

  • Definition of CANN and its significance.
  • Summary of core components (ATC, AscendCL, operator libraries).
  • The role of CANN within AI inference pipelines.

Getting Started with MindSpore and CANN

  • Environment setup (MindSpore + CANN + Python).
  • Training a basic model using MindSpore.
  • Exporting and converting the model via ATC.

Running Inference on Ascend Devices

  • Utilizing OM models with AscendCL or Python APIs.
  • Performing basic input and output preprocessing.
  • Validating model outputs.

Working with Other Frameworks

  • Overview of support for TensorFlow, PyTorch, and ONNX.
  • Supported operators and known limitations.
  • Demonstration of simple model conversion (e.g., from ONNX to OM).

Exploring the CANN and MindSpore Developer Ecosystem

  • Key resources: documentation, GitHub repositories, and sample code.
  • Overview of the MindSpore Hub and model zoo.
  • Community forums, events, and support channels.

Summary and Next Steps

Requirements

  • A basic understanding of machine learning and deep learning concepts.
  • Some experience programming in Python.
  • No prior exposure to CANN or Ascend hardware is necessary.

Target Audience

  • Machine learning developers looking to explore deployment workflows.
  • Students or researchers who are new to Huawei’s AI ecosystem.
  • Contributors to AI frameworks and enthusiasts interested in model acceleration.
 7 Hours

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