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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem
- High-level overview of MindSpore and CANN
- Practical use cases and industrial applications
Preparing the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project orchestration
- Verifying environment setup with sample models
Building Models with MindSpore
- Defining and training models within MindSpore
- Designing data pipelines and formatting datasets
- Exporting models to Ascend-compatible formats
Performance Tuning on Ascend
- Operator fusion and development of custom kernels
- Tiling strategies and AI Core scheduling
- Employing benchmarking and profiling utilities
Deployment Approaches
- Evaluating tradeoffs between edge and cloud deployment
- Leveraging the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Troubleshooting and Monitoring
- Utilizing Profiler and AiD for tracing
- Resolving runtime failures
- Monitoring resource consumption and throughput
Case Studies and Laboratory Integration
- End-to-end pipeline development using MindSpore
- Lab: Construct, optimize, and deploy a model on Ascend
- Comparative performance analysis with other platforms
Recap and Future Directions
Requirements
- A solid grasp of neural networks and AI operational 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
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
