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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.
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
