Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to the Huawei AI Ecosystem
- Ascend AI hardware: including the 310, 910, and 910B chips
- MindSpore, CANN, and associated support tools
- The AI development workflow from training through to deployment
Understanding the CANN Toolkit
- Defining CANN and explaining its significance
- An overview of core components, such as ATC, AscendCL, and operator libraries
- The role of CANN in AI inference pipelines
Getting Started with MindSpore and CANN
- Setting up the environment (MindSpore + CANN + Python)
- Training a basic model using MindSpore
- Exporting and converting the model via ATC
Running Inference on Ascend Devices
- Utilizing the OM model with AscendCL or Python APIs
- 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
- A 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
- Overview of the MindSpore Hub and model zoo
- Community forums, events, and support channels
Summary and Next Steps
Requirements
- A fundamental grasp of machine learning and deep learning principles
- Some programming proficiency in Python
- No previous experience with CANN or Ascend hardware is necessary
Target Audience
- Machine learning developers investigating deployment workflows
- Students or researchers new to Huawei’s AI ecosystem
- AI framework contributors and enthusiasts keen on model acceleration
7 Hours
