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Course Outline
Introduction to the Huawei Ascend Platform
- Insight into Ascend architecture and its ecosystem
- Overview of MindSpore and CANN functionalities
- Exploration of use cases and industry relevance
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Verifying the setup using sample models
Model Development via MindSpore
- Defining and training models within MindSpore
- Managing data pipelines and dataset formatting
- Converting models into Ascend-compatible formats
Optimizing Performance on Ascend
- Applying operator fusion and developing custom kernels
- Implementing tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling tools
Deployment Approaches
- Weighing the tradeoffs between edge and cloud deployment
- Deploying solutions using the MindX SDK
- Integrating with CloudMatrix workflows
Debugging and Monitoring Practices
- Employing Profiler and AiD for process tracing
- Resolving runtime failures effectively
- Tracking resource consumption and throughput metrics
Case Study and Lab Application
- Developing a full pipeline using MindSpore
- Hands-on lab: Constructing, optimizing, and deploying a model on Ascend
- Comparing performance against alternative platforms
Recap and Future Steps
Requirements
- A solid grasp of neural networks and AI workflow architectures
- Proficiency in Python programming
- Familiarity with the pipelines involved in model training and deployment
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
