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Course Outline
Introduction to the Huawei Ascend Platform
- Insights into Ascend architecture and its broader ecosystem
- Overview of MindSpore and the CANN toolkit
- Practical use cases and sector-specific relevance
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for orchestrating projects
- Verifying the environment using example models
Building Models with MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset formats
- Converting models to Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Strategies for tiling and AI Core scheduling
- Utilizing benchmarking and profiling utilities
Deployment Methodologies
- Weighing the tradeoffs between edge and cloud deployment
- Utilizing the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and System Monitoring
- Employing Profiler and AiD for trace analysis
- Resolving runtime issues and failures
- Tracking resource consumption and throughput metrics
Case Studies and Laboratory Integration
- Developing a complete pipeline using MindSpore
- Lab session: Constructing, optimizing, and deploying a model on Ascend
- Comparing performance against alternative platforms
Recap and Future Directions
Requirements
- Solid grasp of neural networks and AI workflows
- Proficiency in Python programming
- Knowledge of model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists leveraging 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
