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.
Duration 7 hours
Course Outline
Overview of Huawei's AI Ecosystem
- Ascend AI hardware: 310, 910, and 910B chips
- MindSpore, CANN, and associated utilities
- AI development pipeline: from training to deployment
Comprehending the CANN Toolkit
- Defining CANN and its significance
- Summary of core elements (ATC, AscendCL, operator libraries)
- The function of CANN within AI inference pipelines
Initial Steps with MindSpore and CANN
- Establishing the environment (MindSpore + CANN + Python)
- Training a foundational model in MindSpore
- Exporting and transforming the model via ATC
Executing Inference on Ascend Devices
- Utilizing the OM model through AscendCL or Python APIs
- Basic preprocessing of inputs and outputs
- Verifying model results
Interoperability with Other Frameworks
- Support landscape for TensorFlow, PyTorch, and ONNX
- Compatible operators and known limitations
- Simple model conversion demonstration (e.g., from ONNX to OM)
Discovering the CANN and MindSpore Developer Community
- Essential resources: documentation, GitHub repositories, sample code
- MindSpore Hub and model zoo introduction
- Community forums, events, and support avenues
Wrap-up and Future Directions
Requirements
- A foundational grasp of machine learning and deep learning principles
- Familiarity with Python programming
- No prior hands-on experience with CANN or Ascend hardware is necessary
Target Audience
- ML developers looking to explore deployment workflows
- Students or researchers new to Huawei's AI ecosystem
- AI framework contributors and enthusiasts interested in model acceleration
