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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem
- Introduction to MindSpore and CANN
- Relevant use cases and industry applications
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Utilising ModelArts and CloudMatrix for project orchestration
- Validating the setup with sample models
Developing Models with MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset formats
- Exporting models to Ascend-compatible formats
Optimising Performance on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Utilising benchmarking and profiling tools
Deployment Strategies
- Evaluating trade-offs between edge and cloud deployment
- Leveraging the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Tracing issues using Profiler and AiD
- Resolving runtime failures through debugging
- Monitoring resource consumption and throughput
Case Studies and Lab Integration
- Developing full pipelines using MindSpore
- Practical lab: Building, optimising, and deploying a model on Ascend
- Comparing performance across different platforms
Conclusion and Future Directions
Requirements
- Foundational knowledge of neural networks and AI workflows
- Proficiency in 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
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 3900 € + 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
