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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and ecosystem
- MindSpore and CANN overview
- Use cases and industry relevance
Configuring the Development Environment
- Installing MindSpore and the CANN toolkit
- Testing the environment using sample models
- Utilizing CloudMatrix and ModelArts for project orchestration
Developing Models with MindSpore
- Model definition and training within MindSpore
- Exporting models to Ascend-compatible formats
- Data pipelines and dataset formatting
Optimizing Performance on Ascend
- Operator fusion and custom kernels
- AI Core scheduling and tiling strategies
- Profiling and benchmarking tools
Deployment Strategies
- Utilizing the MindX SDK for deployment
- Edge versus cloud deployment tradeoffs
- Integration with CloudMatrix workflows
Debugging and Monitoring
- Tracing using Profiler and AiD
- Debugging runtime failures
- Monitoring resource usage and throughput
Case Study and Lab Integration
- Lab: Build, optimize, and deploy a model on Ascend
- Full pipeline development using MindSpore
- Performance comparison with other platforms
Summary and Next Steps
Requirements
- Proficiency in AI workflows and neural networks
- Hands-on experience with Python programming
- Understanding of deployment pipelines and model training
Audience
- AI engineers
- ML developers utilizing MindSpore and Ascend
- Data scientists engaged with the Huawei AI stack
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