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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend ecosystem and architecture
- Overview of CANN and MindSpore
- Industry relevance and use cases
Setting Up the Development Environment
- Installing MindSpore and the CANN toolkit
- Using CloudMatrix and ModelArts for project orchestration
- Validating the environment with sample models
Model Development with MindSpore
- Training and model definition within MindSpore
- Dataset formatting and data pipelines
- Exporting models to formats compatible with Ascend
Performance Optimization on Ascend
- AI Core scheduling and tiling strategies
- Custom kernels and operator fusion
- Profiling and benchmarking tools
Deployment Strategies
- Tradeoffs between cloud and edge deployment
- Utilizing the MindX SDK for deployment
- Integration with CloudMatrix workflows
Debugging and Monitoring
- Tracing with AiD and Profiler
- Monitoring resource usage and throughput
- Debugging runtime failures
Case Study and Lab Integration
- Lab: Building, optimizing, and deploying a model on Ascend
- Full pipeline development using MindSpore
- Performance comparison with other platforms
Summary and Next Steps
Requirements
- Knowledge of AI workflows and neural networks
- Proficiency in Python programming
- Familiarity with model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists utilizing the Huawei AI stack
- ML developers working with MindSpore and Ascend
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
