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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend architecture and its ecosystem.
- Insights into CANN and MindSpore.
- Industry relevance and practical use cases.
Setting Up the Development Environment
- Installation of MindSpore and the CANN toolkit.
- Utilizing CloudMatrix and ModelArts for project orchestration.
- Validating the environment using sample models.
Model Development with MindSpore
- Defining and training models within MindSpore.
- Handling dataset formatting and data pipelines.
- Exporting models to formats compatible with Ascend.
Performance Optimization on Ascend
- Implementing operator fusion and custom kernels.
- AI Core scheduling and tiling strategies.
- Utilizing profiling and benchmarking tools.
Deployment Strategies
- Evaluating tradeoffs between edge and cloud deployment.
- Employing the MindX SDK for deployment purposes.
- Integrating with CloudMatrix workflows.
Debugging and Monitoring
- Using AiD and Profiler for tracing activities.
- Resolving runtime failures.
- Monitoring throughput and resource usage.
Lab Integration and Case Study
- Developing the full pipeline using MindSpore.
- Lab: Construct, optimize, and deploy a model on Ascend.
- Comparing performance against other platforms.
Summary and Next Steps
Requirements
- A foundational understanding of AI workflows and neural networks.
- Proficiency in Python programming.
- Familiarity with pipelines for model training and deployment.
Target Audience
- AI engineers.
- Data scientists working with the Huawei AI stack.
- Machine learning developers utilizing MindSpore and Ascend.
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
