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Duration 21 hours
Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem
- Introduction to MindSpore and CANN
- Real-world use cases and industry applications
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilizing ModelArts and CloudMatrix for project management
- Validating the setup with sample models
Model Development Using MindSpore
- Defining and training models in MindSpore
- Managing data pipelines and dataset formatting
- Exporting models into Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Strategies for tiling and AI Core scheduling
- Utilizing benchmarking and profiling tools
Deployment Approaches
- Evaluating tradeoffs between edge and cloud deployment
- Deploying via the MindX SDK
- Integrating with CloudMatrix workflows
Debugging and Monitoring Processes
- Leveraging Profiler and AiD for tracing purposes
- Diagnosing runtime failures
- Monitoring resource utilization and throughput
Case Study and Practical Lab Integration
- Developing a full pipeline using MindSpore
- Lab exercise: Building, optimizing, and deploying a model on Ascend
- Comparing performance against alternative platforms
Conclusion and Future Directions
Requirements
- A solid grasp of neural networks and AI workflows
- Proficiency in Python programming
- Familiarity with model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists utilizing the Huawei AI stack
- ML developers working with Ascend and MindSpore
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
