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Course Outline
Intro to the Huawei Ascend Platform
- Insight into Ascend architecture and its surrounding ecosystem
- Overview of MindSpore and CANN
- Real-world use cases and industry applications
Configuring the Development Environment
- Installing the CANN toolkit and MindSpore framework
- Leveraging ModelArts and CloudMatrix for project coordination
- Validating the setup with sample models
Developing Models with MindSpore
- Defining and training models within MindSpore
- Constructing data pipelines and formatting datasets
- 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 Methodologies
- Evaluating the tradeoffs between edge and cloud deployment
- Utilizing the MindX SDK for deployment tasks
- Integrating with CloudMatrix workflows
Debugging and System Monitoring
- Employing Profiler and AiD for process tracing
- Troubleshooting runtime issues
- Tracking resource utilization and throughput
Case Study and Laboratory Integration
- Developing a complete pipeline using MindSpore
- Lab exercise: Construct, optimize, and deploy a model on Ascend
- Comparing performance against alternative platforms
Recap and Future Steps
Requirements
- Solid understanding of neural networks and AI operational 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
