Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and ecosystem
- MindSpore and CANN overview
- Relevant use cases and industry applications
Setting Up the Development Environment
- Installing the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project orchestration
- Validating the environment with sample models
Model Development with MindSpore
- Model definition and training processes in MindSpore
- Data pipelines and dataset formatting
- Exporting models to Ascend-compatible formats
Performance Optimization on Ascend
- Operator fusion and custom kernels
- Tiling strategies and AI Core scheduling
- Benchmarking and profiling tools
Deployment Strategies
- Tradeoffs between edge and cloud deployment
- Leveraging the MindX SDK for deployment
- Integration with CloudMatrix workflows
Debugging and Monitoring
- Using Profiler and AiD for tracing
- Debugging runtime failures
- Monitoring resource usage and throughput
Case Study and Lab Integration
- End-to-end pipeline development using MindSpore
- Lab: 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
- 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 Ascend and MindSpore
Testimonials (2)
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
Michal Maj - XL Catlin Services SE (AXA XL)
Course - GitHub Copilot for Developers
Trainer able to adjust the course level during training to fit our understanding level on the topic, so that we could gain more useful knowledge that could further help us harness the tools in our daily works.