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Course Outline
Overview of the Huawei Ascend Platform
- Ascend architecture and ecosystem landscape
- Introduction to MindSpore and CANN
- Real-world use cases and industry applicability
Configuring the Development Workspace
- Installing the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Validating the setup with sample models
Model Creation with MindSpore
- Defining and training models within MindSpore
- Data pipelines and dataset structuring
- Exporting models to Ascend-compatible formats
Optimizing Performance on Ascend
- Operator fusion and custom kernel development
- Tiling techniques and AI Core scheduling
- Benchmarking and profiling utilities
Deployment Approaches
- Evaluating trade-offs 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 purposes
- Resolving runtime failures
- Tracking resource consumption and throughput
Case Studies and Lab Integration
- Developing a complete pipeline using MindSpore
- Lab Exercise: Build, optimize, and deploy a model on Ascend
- Comparing performance against other platforms
Recap and Future Directions
Requirements
- A 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 working 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
