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Course Outline
Introduction to the Huawei Ascend Platform
- Overview of Ascend architecture and ecosystem
- Introduction to MindSpore and CANN
- Relevant use cases and industry applications
Configuring the Development Environment
- Installation of the CANN toolkit and MindSpore
- Leveraging ModelArts and CloudMatrix for project management
- Validating the environment with sample models
Developing Models with MindSpore
- Defining and training models in MindSpore
- Managing data pipelines and dataset structures
- Converting models for Ascend compatibility
Optimizing Performance on Ascend
- Operator fusion and custom kernels
- Tiling strategies and AI Core scheduling
- Tools for benchmarking and profiling
Deployment Strategies
- Trade-offs between edge and cloud deployment
- Utilizing the MindX SDK for deployment
- Integrating with CloudMatrix workflows
Debugging and Monitoring
- Employing Profiler and AiD for tracing
- Resolving runtime failures
- Tracking resource usage and throughput
Case Study and Lab Integration
- End-to-end development using MindSpore
- Lab: Construct, optimize, and deploy a model on Ascend
- Performance comparisons with other platforms
Summary and Future Steps
Requirements
- Knowledge of neural networks and AI processes
- Proficiency in Python programming
- Experience with model training and deployment workflows
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
