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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem.
- Relevant use cases and industry applications.
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore.
- Utilizing ModelArts and CloudMatrix for project orchestration.
- Validating the environment using sample models.
Model Development using MindSpore
- Establishing data pipelines and formatting datasets.
- Exporting models into Ascend-compatible formats.
Performance Optimization on Ascend
- Implementing operator fusion and custom kernels.
- Applying benchmarking and profiling tools.
Deployment Strategies
- Evaluating tradeoffs between edge and cloud deployment.
- Leveraging the MindX SDK for deployment purposes.
- Integrating with CloudMatrix workflows.
Debugging and Monitoring
- Employing Profiler and AiD for tracing.
- Troubleshooting runtime failures.
- Monitoring resource utilization and throughput.
Case Study and Lab Integration
- Developing a full pipeline with MindSpore.
- Lab: Construct, optimize, and deploy a model on Ascend.
- Comparing performance against other platforms.
Summary and Next Steps
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 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
