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Course Outline
Introduction to the Huawei Ascend Platform
- Comprehensive overview of Ascend architecture and ecosystem
- Insights into MindSpore and CANN
- Real-world use cases and industry relevance
Setting Up the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Validating the environment with sample models
Model Development with MindSpore
- Defining and training models in MindSpore
- Managing data pipelines and dataset formatting
- Exporting models to Ascend-compatible formats
Performance Optimization on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Utilising benchmarking and profiling tools
Deployment Strategies
- Evaluating tradeoffs between edge and cloud deployment
- Deploying via the MindX SDK
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Employing Profiler and AiD for tracing
- Resolving runtime failures
- Monitoring resource usage and throughput
Case Study and Lab Integration
- End-to-end pipeline development using MindSpore
- Lab Exercise: Build, optimise, and deploy a model on Ascend
- Conducting performance comparisons with other platforms
Summary and Next Steps
Requirements
- Proficiency in neural networks and AI workflows
- Hands-on experience with Python programming
- Familiarity with model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists working within the Huawei AI stack
- ML developers utilising 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
