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Duration 21 hours
Course Outline
Overview of the Huawei Ascend Platform
- Detailed look at Ascend architecture and its ecosystem
- Introduction to MindSpore and CANN
- Real-world applications and industry significance
Configuring the Development Setup
- Installing the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project management
- Validating the setup with test models
Creating Models with MindSpore
- Defining and training models within MindSpore
- Establishing data pipelines and dataset structures
- Converting models to 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
- Evaluating tradeoffs between edge and cloud deployment
- Utilizing the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Troubleshooting and Monitoring
- Employing Profiler and AiD for trace analysis
- Resolving runtime errors and failures
- Tracking resource consumption and throughput
Case Studies and Lab Practice
- End-to-end pipeline development using MindSpore
- Laboratory exercise: Constructing, optimizing, and deploying a model on Ascend
- Comparative performance analysis against other platforms
Conclusion and Future Pathways
Requirements
- A solid grasp of neural networks and AI operational flows
- Proficiency in Python programming
- Familiarity with standard model training and deployment pipelines
Target Audience
- AI Engineers
- Data Scientists utilizing the Huawei AI stack
- ML Developers working with Ascend and MindSpore
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
