Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and ecosystem.
- Overview of MindSpore and CANN.
- Use cases and industry relevance.
Setting Up the Development Environment
- Installing the CANN toolkit and MindSpore.
- Utilizing ModelArts and CloudMatrix for project orchestration.
- Testing the environment with sample models.
Model Development with MindSpore
- Model definition and training in MindSpore.
- Data pipelines and dataset formatting.
- Exporting models to Ascend-compatible format.
Performance Optimization on Ascend
- Operator fusion and custom kernels.
- Tiling strategy and AI Core scheduling.
- Benchmarking and profiling tools.
Deployment Strategies
- Edge vs cloud deployment tradeoffs.
- Using 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
- Full pipeline development using MindSpore.
- Lab: Build, optimize, and deploy a model on Ascend.
- Performance comparison with other platforms.
Summary and Next Steps
Requirements
- Understanding of neural networks and AI workflows.
- Experience with Python programming.
- Familiarity with model training and deployment pipelines.
Target Audience
- AI engineers.
- Data scientists working with the Huawei AI stack.
- ML developers using 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.
