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
- An overview of Ascend architecture and its ecosystem
- General overview of MindSpore and CANN
- Key use cases and industry applications
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Verifying the environment using sample models
Model Development Using MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset structures
- Exporting models into Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling utilities
Deployment Strategies
- Trade-offs between edge and cloud deployment
- Utilizing the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Tracing issues using Profiler and AiD
- Troubleshooting runtime failures
- Monitoring resource consumption and throughput
Case Study and Practical Labs
- End-to-end pipeline development with MindSpore
- Lab: Building, optimizing, and deploying a model on Ascend
- Comparative performance analysis against other platforms
Recap 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 utilizing 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
