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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend ecosystem and architecture
- Insights into CANN and MindSpore
- Industry relevance and use cases
Establishing the Development Environment
- Installation of MindSpore and the CANN toolkit
- Utilizing CloudMatrix and ModelArts for project orchestration
- Validating the environment using sample models
Developing Models with MindSpore
- Training and model definition in MindSpore
- Data pipeline management and dataset formatting
- Exporting models for Ascend compatibility
Performance Optimization on Ascend
- AI Core scheduling and tiling strategies
- Custom kernels and operator fusion
- Profiling and benchmarking tools
Deployment Strategies
- Trade-offs between edge and cloud deployment
- Employing the MindX SDK for deployment
- Integration with CloudMatrix workflows
Debugging and Monitoring
- Utilizing AiD and Profiler for tracing
- Resolving runtime failures
- Tracking throughput and resource usage
Lab Integration and Case Study
- Comprehensive pipeline development using MindSpore
- Lab: Design, optimize, and deploy a model on Ascend
- Performance comparison with alternative platforms
Summary and Next Steps
Requirements
- Knowledge of AI workflows and neural networks
- Proficiency in Python programming
- Experience with deployment pipelines and model training
Target Audience
- AI engineers
- Data scientists working with the Huawei AI stack
- ML developers utilizing MindSpore and Ascend
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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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