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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
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
- Defining and training models in MindSpore
- Establishing data pipelines and formatting datasets
- Exporting models to Ascend-compatible formats
Performance Optimization on Ascend
- Operator fusion and custom kernel implementation
- Tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling tools
Deployment Strategies
- Evaluating tradeoffs between edge and cloud deployment
- Using the MindX SDK for deployment
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Tracing with Profiler and AiD
- Addressing 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
- Comparing performance against other platforms
Summary and Next Steps
Requirements
- A solid understanding of neural networks and AI workflows
- Proficiency in Python programming
- Familiarity with model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists leveraging the Huawei AI stack
- ML developers utilizing 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
