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 Ascend architecture and ecosystem
- Introduction to MindSpore and CANN
- Real-world use cases and industry applications
Configuring the Development Environment
- Installing the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Validating the environment with sample models
Model Development Using MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset formatting
- Exporting models to Ascend-compatible formats
Optimising Performance on Ascend
- Operator fusion and custom kernel development
- Tiling strategies and AI Core scheduling
- Utilising benchmarking and profiling tools
Deployment Strategies
- Comparing edge versus cloud deployment trade-offs
- Implementing deployment using the MindX SDK
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Tracing issues using Profiler and AiD
- Diagnosing runtime failures
- Monitoring resource utilisation and throughput
Case Study and Lab Integration
- End-to-end pipeline development with MindSpore
- Lab: Build, optimise, and deploy a model on Ascend
- Performance comparison against other platforms
Summary and Future Directions
Requirements
- A solid grasp of neural networks and AI workflows
- Proficiency in Python programming
- Knowledge of 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
