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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend ecosystem and architecture.
- Introduction to CANN and MindSpore.
- Industry relevance and use cases.
Establishing the Development Environment
- Installation of MindSpore and the CANN toolkit.
- Using CloudMatrix and ModelArts for project orchestration.
- Verifying the environment with sample models.
Model Development with MindSpore
- Defining and training models within MindSpore.
- Dataset formatting and data pipelines.
- Exporting models to formats compatible with Ascend.
Performance Optimization on Ascend
- Custom kernels and operator fusion.
- AI Core scheduling and tiling strategies.
- Profiling and benchmarking tools.
Deployment Strategies
- Tradeoffs between edge and cloud deployment.
- Using the MindX SDK for deployment.
- Integration with CloudMatrix workflows.
Debugging and Monitoring
- Tracing with AiD and Profiler.
- Resolving runtime failures.
- Monitoring throughput and resource usage.
Lab Integration and Case Study
- Complete pipeline development using MindSpore.
- Lab: Develop, optimize, and deploy a model on Ascend.
- Comparing performance with other platforms.
Summary and Next Steps
Requirements
- Knowledge of neural networks and AI workflows.
- Proficiency in Python programming.
- Familiarity with pipelines for model training and deployment.
Target Audience
- AI engineers.
- Data scientists utilizing the Huawei AI stack.
- ML developers employing 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