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Course Outline
Introduction to the Huawei Ascend Platform
- An overview of Ascend architecture and its ecosystem.
- An introduction to MindSpore and CANN.
- Exploring use cases and their industry relevance.
Configuring the Development Environment
- Installing the CANN toolkit and MindSpore.
- Leveraging ModelArts and CloudMatrix for project orchestration.
- Validating the setup with sample models.
Building Models with MindSpore
- Defining and training models within MindSpore.
- Managing data pipelines and dataset formatting.
- Exporting models to formats compatible with Ascend.
Optimizing Performance on Ascend
- Implementing operator fusion and custom kernels.
- Applying tiling strategies and AI Core scheduling.
- Utilizing benchmarking and profiling tools.
Deployment Strategies
- Analyzing tradeoffs between edge and cloud deployment.
- Using the MindX SDK for deployment processes.
- Integrating with CloudMatrix workflows.
Debugging and Monitoring
- Employing Profiler and AiD for tracing.
- Troubleshooting runtime failures.
- Monitoring resource utilization 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 across different platforms.
Conclusion and Future Steps
Requirements
- A solid grasp of neural networks and AI workflows.
- Practical experience with Python programming.
- Familiarity with model training and deployment pipelines.
Target Audience
- AI engineers.
- Data scientists working within the Huawei AI ecosystem.
- ML developers utilizing 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
