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Duration 7 hours
Course Outline
Introduction to Huawei’s AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips.
- MindSpore, CANN, and associated supporting tools.
- AI development workflow: from training to deployment.
Understanding the CANN Toolkit
- Definition of CANN and its strategic importance.
- Overview 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 in MindSpore.
- Exporting and converting the model using ATC.
Running Inference on Ascend Devices
- Utilising the OM model via AscendCL or Python APIs.
- Basic input/output preprocessing techniques.
- Validating model outputs.
Working with Other Frameworks
- Overview of support for TensorFlow, PyTorch, and ONNX.
- Supported operators and known limitations.
- Simple model conversion demonstration (e.g., from ONNX to OM).
Exploring the CANN and MindSpore Developer Ecosystem
- Key resources: documentation, GitHub repositories, and sample code.
- MindSpore Hub and model zoo overview.
- Community forums, events, and support channels.
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning and deep learning principles.
- Familiarity with Python programming.
- No previous exposure to CANN or Ascend hardware is necessary.
Target Audience
- Machine learning developers exploring deployment workflows.
- Students or researchers new to Huawei’s AI ecosystem.
- AI framework contributors and enthusiasts interested in model acceleration.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 1300 € + VAT*
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