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Course Outline
Introduction to the Huawei Ascend Platform
- Detailed look at Ascend architecture and its broader ecosystem
- Overview of MindSpore and the CANN framework
- Real-world use cases and their industry impact
Configuring the Development Environment
- Step-by-step installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project orchestration
- Validating the setup through testing with sample models
Model Development Using MindSpore
- Defining and training models within the MindSpore framework
- Managing 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 managing AI Core scheduling
- Utilizing benchmarking and profiling tools for analysis
Deployment Strategies
- Evaluating the trade-offs between edge and cloud deployment
- Executing deployments via the MindX SDK
- Integrating workflows with CloudMatrix
Debugging and Monitoring
- Employing Profiler and AiD for process tracing
- Resolving runtime failures and errors
- Tracking resource consumption and system throughput
Case Studies and Lab Integration
- End-to-end pipeline development leveraging MindSpore
- Practical lab: Building, optimizing, and deploying a model on Ascend
- Comparing performance against alternative platforms
Summary and Future Directions
Requirements
- A solid grasp of neural networks and AI operational workflows
- Proficiency in Python programming
- Knowledge of model training and deployment pipelines
Target Audience
- AI engineers
- Data scientists utilizing the Huawei AI stack
- Machine learning 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
