Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to the Huawei Ascend Platform
- Overview of the Ascend architecture and ecosystem
- Overview of MindSpore and CANN
- Use cases and industry relevance
Setting Up the Development Environment
- Installing the CANN toolkit and MindSpore
- Utilising ModelArts and CloudMatrix for project orchestration
- Testing the environment with sample models
Model Development with MindSpore
- Model definition and training in MindSpore
- Data pipelines and dataset formatting
- Exporting models to an Ascend-compatible format
Performance Optimization on Ascend
- Operator fusion and custom kernels
- Tiling strategy and AI Core scheduling
- Benchmarking and profiling tools
Deployment Strategies
- Tradeoffs between edge and cloud deployment
- Utilising the MindX SDK for deployment
- Integration with CloudMatrix workflows
Debugging and Monitoring
- Using Profiler and AiD for tracing
- Debugging runtime failures
- Monitoring resource usage and throughput
Case Study and Lab Integration
- Full pipeline development using MindSpore
- Lab: Build, optimize, and deploy a model on Ascend
- Performance comparison with other platforms
Summary and Next Steps
Requirements
- A solid understanding of neural networks and AI workflows
- Practical experience with Python programming
- Familiarity with model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists utilising the Huawei AI stack
- ML developers working with 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
