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Duration 21 hours
Course Outline
Introduction to the Huawei Ascend Platform
- Exploration of Ascend architecture and its ecosystem
- Overview of MindSpore and CANN components
- Practical use cases and their industry significance
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project management
- Validating the setup using sample models
Model Creation with MindSpore
- Defining and training models within MindSpore
- Structuring data pipelines and formatting datasets
- Converting models into Ascend-compatible formats
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels
- Applying tiling strategies and managing AI Core scheduling
- Utilizing benchmarking and profiling utilities
Deployment Tactics
- Evaluating trade-offs between edge and cloud deployment
- Employing the MindX SDK for implementation
- Integrating with CloudMatrix workflows
Troubleshooting and Oversight
- Tracing issues using Profiler and AiD
- Resolving runtime failures
- Tracking resource consumption and throughput metrics
Case Studies and Laboratory Application
- End-to-end pipeline development leveraging MindSpore
- Lab exercise: Construct, refine, and launch a model on Ascend
- Comparative performance analysis against alternative platforms
Recap and Future Directions
Requirements
- Proficiency in neural network concepts and AI operational flows
- Proficiency in Python scripting
- Experience with model training and deployment pipelines
Target Audience
- AI Engineers
- Data Scientists utilizing the Huawei AI stack
- ML Developers working with Ascend and MindSpore
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
