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Course Outline
Introduction to the Huawei Ascend Platform
- Examination of Ascend architecture and its ecosystem
- Overview of MindSpore and CANN
- Industry relevance and practical use cases
Configuring the Development Environment
- Setup of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project management
- Validating the environment using sample models
Model Creation with MindSpore
- Defining and training models in MindSpore
- Constructing 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 AI Core scheduling
- Utilizing benchmarking and profiling utilities
Deployment Approaches
- Balancing tradeoffs between edge and cloud deployment
- Deploying using the MindX SDK
- Integrating with CloudMatrix workflows
Debugging and System Monitoring
- Tracing with Profiler and AiD
- Resolving runtime failures through debugging
- Tracking resource consumption and throughput
Case Study and Laboratory Integration
- Developing a complete pipeline using MindSpore
- Laboratory session: Building, optimizing, and deploying a model on Ascend
- Comparing performance against other platforms
Recap and Future Directions
Requirements
- A solid grasp of neural networks and AI operational flows
- Proficiency in Python programming
- Knowledge of 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
