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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and the broader ecosystem
- Overview of MindSpore and CANN
- Use cases and their relevance across industries
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Validating the setup with sample models
Model Development Using MindSpore
- Defining and training models within MindSpore
- Data pipeline management and dataset formatting
- Exporting models into Ascend-compatible formats
Optimizing Performance on Ascend
- Operator fusion and the creation of custom kernels
- Tiling strategies and AI Core scheduling
- Utilizing benchmarking and profiling tools
Deployment Strategies
- Comparing the tradeoffs between edge and cloud deployment
- Implementing deployment using the MindX SDK
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Using Profiler and AiD for tracing purposes
- Diagnosing runtime failures
- Monitoring resource consumption and throughput
Case Study and Lab Integration
- Developing a full pipeline using MindSpore
- Lab exercise: Build, optimize, and deploy a model on Ascend
- Comparing performance against other platforms
Summary and Future Directions
Requirements
- A solid grasp of neural networks and AI workflows
- Proficiency in Python programming
- Familiarity 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
