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Course Outline
Introduction to the Huawei Ascend Platform
- Exploration of Ascend architecture and its ecosystem
- Overview of MindSpore and CANN frameworks
- Real-world use cases and industry significance
Establishing the Development Environment
- Installation and setup of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project coordination
- Validation of the environment using test models
Model Development via MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset structures
- Converting models into Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling techniques
- Utilizing benchmarking and profiling utilities
Deployment Approaches
- Analyzing tradeoffs between edge and cloud deployments
- Executing deployments using the MindX SDK
- Integrating with CloudMatrix operational workflows
Debugging and System Monitoring
- Employing Profiler and AiD tools for performance tracing
- Diagnosing and resolving runtime errors
- Tracking resource consumption and throughput metrics
Case Study and Lab Integration
- End-to-end pipeline development leveraging MindSpore
- Practical Lab: Constructing, optimizing, and deploying a model on Ascend
- Comparative performance analysis against other platforms
Summary and Future Directions
Requirements
- Foundational knowledge of neural networks and AI operational workflows
- Proficiency in Python programming
- Working understanding of model training and deployment pipelines
Target Audience
- AI Engineers
- Data Scientists operating within 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
