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Course Outline
Introduction to the Huawei Ascend Ecosystem
- Insights into Ascend architecture and its broader ecosystem
- High-level view of MindSpore and CANN
- Real-world use cases and sector-specific relevance
Establishing the Development Workspace
- Setup of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project coordination
- Validating the environment with reference models
Building Models with MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset structures
- Converting models to Ascend-compatible formats
Optimizing Ascend Performance
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling utilities
Deployment Methodologies
- Evaluating tradeoffs between edge and cloud deployment
- Utilizing the MindX SDK for rollout tasks
- Integrating with CloudMatrix processes
Troubleshooting and Oversight
- Employing Profiler and AiD for tracing issues
- Resolving runtime errors
- Tracking resource consumption and throughput
Case Studies and Lab Work
- End-to-end pipeline creation with MindSpore
- Practical lab: Construct, refine, and deploy a model on Ascend
- Comparative performance analysis against other platforms
Recap and Future Directions
Requirements
- Foundational knowledge of neural networks and AI processes
- Proficiency in Python programming
- Understanding of model training and deployment workflows
Target Audience
- AI Engineers
- Data scientists utilizing 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
