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Course Outline
Introduction to the Huawei Ascend Platform
- Exploration of Ascend architecture and its ecosystem
- Overview of MindSpore and CANN
- Industry applications and use cases
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project orchestration
- Validating the environment using sample models
Model Development Using MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset formats
- Exporting models to Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling tools
Deployment Approaches
- Evaluating edge versus cloud deployment trade-offs
- Employing the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and Monitoring Practices
- Applying Profiler and AiD for tracing
- Troubleshooting runtime failures
- Monitoring resource consumption and throughput
Case Study and Lab Integration
- Developing a full pipeline using MindSpore
- Practical Lab: Construct, optimize, and deploy a model on Ascend
- Comparing performance against other platforms
Summary and Future Directions
Requirements
- A solid grasp of neural networks and AI workflows
- Proficiency in Python programming
- Familiarity with model training and deployment pipelines
Target Audience
- AI Engineers
- Data scientists working within the Huawei AI ecosystem
- 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
