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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem
- Introduction to MindSpore and CANN
- Practical use cases and industry relevance
Configuring the Development Environment
- Installing the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Validating the environment with sample models
Developing Models with MindSpore
- Defining models and executing training in MindSpore
- Constructing data pipelines and formatting datasets
- 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 Strategies
- Weighing the tradeoffs between edge and cloud deployment
- Utilizing the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Employing Profiler and AiD for tracing issues
- Diagnosing runtime failures
- Monitoring resource consumption and throughput
Case Study and Lab Integration
- Developing a full pipeline using MindSpore
- Lab Exercise: Building, optimizing, and deploying 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
- Experience with model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists working with 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
