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Course Outline
Intro to the Huawei Ascend Platform
- Overview of Ascend architecture and its ecosystem
- Introduction to MindSpore and CANN
- Real-world use cases and industry impact
Configuring the Development Environment
- Installing the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project management
- Validating the setup with sample models
Building Models with MindSpore
- Defining and training models in MindSpore
- Managing data pipelines and dataset formats
- Exporting models to Ascend-compatible formats
Optimizing Ascend Performance
- Implementing operator fusion and custom kernels
- Applying tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling tools
Deployment Approaches
- Comparing edge versus cloud deployment trade-offs
- Using the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and System Monitoring
- Employing Profiler and AiD for tracing activities
- Resolving runtime failures
- Tracking resource consumption and throughput
Case Studies and Lab Application
- End-to-end pipeline development using MindSpore
- Practical lab: Building, optimizing, and deploying a model on Ascend
- Benchmarking performance against other platforms
Recap and Future Steps
Requirements
- A solid grasp of neural networks and AI workflows
- Proficiency in Python programming
- 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
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
