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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem
- High-level look at 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 management
- Validating the environment with example models
Building Models with MindSpore
- Defining and training models in MindSpore
- Managing data pipelines and dataset structures
- Converting 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 Approaches
- Evaluating the tradeoffs between edge and cloud deployment
- Utilizing the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Employing Profiler and AiD for tracing issues
- Resolving runtime failures
- Tracking resource consumption and throughput
Case Study and Practical Lab
- Developing a complete pipeline with MindSpore
- Hands-on lab: Construct, optimize, and deploy a model on Ascend
- Comparing performance 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 (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
