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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend architecture and its ecosystem
- Introduction to MindSpore and CANN
- Real-world use cases and industry relevance
Establishing the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Validating the environment using sample models
Model Development with MindSpore
- Defining and training models within MindSpore
- Configuring data pipelines and dataset structures
- Converting models to Ascend-compatible formats
Performance Optimization on Ascend
- Operator fusion and implementation of custom kernels
- Tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling tools
Deployment Strategies
- Weighing the pros and cons of edge versus cloud deployment
- Employing the MindX SDK for deployment purposes
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Using Profiler and AiD for trace analysis
- Resolving runtime failures
- Monitoring resource utilization and throughput
Case Study and Lab Integration
- Executing full pipeline development using MindSpore
- Lab: Construct, optimize, and deploy a model on Ascend
- Comparing performance metrics against other platforms
Summary and Next Steps
Requirements
- A solid grasp of neural networks and AI operational workflows
- Proficiency in Python programming
- Familiarity with pipelines for model training and deployment
Target Audience
- AI engineers
- Data scientists leveraging the Huawei AI stack
- Machine learning 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
