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
An Introduction to the Huawei Ascend Platform
- Overview of the Ascend ecosystem and architecture
- MindSpore and CANN overview
- Industry relevance and use cases
Setting Up the Development Environment
- Installing MindSpore and the CANN toolkit
- Utilizing ModelArts and CloudMatrix for project orchestration
- Validating the environment using sample models
Model Development with MindSpore
- Data pipelines and dataset formatting
- Model definition and training within MindSpore
- Exporting models to an Ascend-compatible format
Performance Optimization on Ascend
- Tiling strategy and AI Core scheduling
- Operator fusion and custom kernels
- Benchmarking and profiling tools
Deployment Strategies
- Integrating with CloudMatrix workflows
- Using the MindX SDK for deployment
- Edge versus cloud deployment trade-offs
Debugging and Monitoring
- Monitoring resource usage and throughput
- Debugging runtime failures
- Using Profiler and AiD for tracing
Case Study and Lab Integration
- Lab: Build, optimise, and deploy a model on Ascend
- Full pipeline development using MindSpore
- Performance comparison with other platforms
Summary and Next Steps
Requirements
- Knowledge of AI workflows and neural networks
- Proficiency in Python programming
- Understanding of model deployment and training pipelines
Target Audience
- Data scientists working with the Huawei AI stack
- ML developers utilising Ascend and MindSpore
- AI engineers
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
