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Course Outline
Introduction to the Huawei Ascend Platform
- Examining the Ascend architecture and its ecosystem.
- Reviewing MindSpore and CANN fundamentals.
- Exploring use cases and their relevance to the industry.
Preparing the Development Environment
- Installing the CANN toolkit and MindSpore.
- Leveraging ModelArts and CloudMatrix for project coordination.
- Validating the setup using sample models.
Developing Models with MindSpore
- Defining and training models within MindSpore.
- Managing data pipelines and dataset formatting.
- Exporting models into Ascend-compatible formats.
Optimizing Performance on Ascend
- Applying operator fusion and custom kernels.
- Implementing tiling strategies and AI Core scheduling.
- Utilizing benchmarking and profiling utilities.
Deployment Approaches
- Evaluating tradeoffs between edge and cloud deployment.
- Utilizing the MindX SDK for deployment tasks.
- Integrating with CloudMatrix workflows.
Debugging and Monitoring
- Using Profiler and AiD for tracing processes.
- Troubleshooting runtime failures.
- Monitoring resource consumption and throughput.
Case Study and Laboratory Integration
- Building a complete pipeline using MindSpore.
- Lab exercise: Construct, optimize, and deploy a model on Ascend.
- Comparing performance against other platforms.
Recap and Future Steps
Requirements
- Proficiency in neural networks and AI workflows.
- Solid experience with 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
