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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend ecosystem and architecture
- MindSpore and CANN overview
- Industry relevance and use cases
Setting Up the Development Environment
- Installing MindSpore and the CANN toolkit
- Utilizing CloudMatrix and ModelArts for project orchestration
- Validating the environment with sample models
Model Development with MindSpore
- Model definition and training in MindSpore
- Dataset formatting and data pipelines
- Exporting models to Ascend-compatible format
Performance Optimization on Ascend
- Operator fusion and custom kernels
- AI Core scheduling and tiling strategy
- Profiling and benchmarking 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 throughput and resource usage
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 AI workflows and neural networks
- Experience with Python programming
- Familiarity with model deployment and training pipelines
Audience
- Data scientists leveraging the Huawei AI stack
- AI engineers
- ML developers using Ascend and MindSpore
21 Hours
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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