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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem
- Introduction to MindSpore and CANN
- Industry applications and relevance
Establishing the Development Environment
- Installing the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project coordination
- Validating the setup with example models
Model Development using MindSpore
- Defining and training models in MindSpore
- Data pipelines and dataset structuring
- Exporting models to Ascend-compatible formats
Optimizing Performance on Ascend
- Operator fusion and custom kernel development
- Tiling strategies and AI Core scheduling
- Utilization of benchmarking and profiling tools
Deployment Approaches
- Tradeoffs between edge and cloud deployment
- Deploying using the MindX SDK
- Integration with CloudMatrix workflows
Debugging and Monitoring
- Tracing with Profiler and AiD tools
- Diagnosing runtime errors
- Monitoring resource consumption and throughput
Case Study and Laboratory Integration
- End-to-end pipeline development with MindSpore
- Laboratory exercise: Building, optimizing, and deploying a model on Ascend
- Performance comparison against other platforms
Recap and Future Directions
Requirements
- Proficiency in neural networks and AI processes
- Practical experience with 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
