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Course Outline
Introduction to the Huawei Ascend Platform
- Exploration of Ascend architecture and its ecosystem
- Overview of MindSpore and CANN components
- Analysis of use cases and industry impact
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project management
- Validating the setup with test models
Developing Models with MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset structures
- Exporting models into Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling utilities
Deployment Approaches
- Weighing the pros and cons of edge versus cloud deployment
- Using the MindX SDK to facilitate deployment
- Integrating with CloudMatrix operational flows
Troubleshooting and Monitoring
- Employing Profiler and AiD for trace analysis
- Resolving runtime issues and failures
- Tracking resource consumption and throughput metrics
Case Studies and Practical Labs
- Constructing a full pipeline with MindSpore
- Lab exercise: Creating, optimizing, and deploying a model on Ascend
- Comparative performance analysis against other platforms
Recap and Future Directions
Requirements
- Foundational knowledge of neural networks and AI workflows
- Proficiency in Python programming
- Familiarity with model training and deployment processes
Target Audience
- AI engineers
- Data scientists leveraging the Huawei AI stack
- Machine learning developers utilizing Ascend and MindSpore
21 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
