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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend architecture and ecosystem
- Insights into MindSpore and CANN
- Relevant use cases and industry applications
Establishing the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project orchestration
- Verifying the environment using sample models
Model Development with MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset formatting
- Exporting models to formats compatible with Ascend
Performance Optimization on Ascend
- Operator fusion and custom kernel implementation
- Tiling strategies and AI Core scheduling
- Benchmarking and profiling utilities
Deployment Strategies
- Evaluating tradeoffs between edge and cloud deployment
- Employing the MindX SDK for deployment purposes
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Using Profiler and AiD for tracing
- Resolving runtime failures
- Tracking resource usage and throughput
Case Study and Lab Integration
- Full pipeline development using MindSpore
- Lab Exercise: Build, optimize, and deploy a model on Ascend
- Performance comparison with other platforms
Summary and Next Steps
Requirements
- A foundational understanding of neural networks and AI workflows
- Practical experience with Python programming
- Familiarity with pipelines for model training and deployment
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