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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and the broader ecosystem
- Introductory look at MindSpore and CANN
- Real-world use cases and industry significance
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project coordination
- Validating the setup with test models
Building Models with MindSpore
- Defining and training models within MindSpore
- Structuring data pipelines and formatting datasets
- Converting models to Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Employing benchmarking and profiling utilities
Deployment Approaches
- Evaluating trade-offs between edge and cloud deployment
- Leveraging the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and System Monitoring
- Utilizing Profiler and AiD for trace analysis
- Troubleshooting runtime issues
- Tracking resource consumption and throughput
Case Studies and Lab Integration
- End-to-end pipeline development using MindSpore
- Lab session: Construct, refine, and deploy a model on Ascend
- Comparing performance against alternative platforms
Conclusion and Future Directions
Requirements
- A solid grasp of neural networks and AI processes
- Proficiency in Python programming
- Knowledge of model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists utilizing the Huawei AI stack
- ML developers working with 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
