Python Profiling: NVIDIA Nsight Tools Feature Spotlight
Profile Python for AI and deep learning applications with NVIDIA's suite of Nsight Developer Tools. This video explores Python profiling features that will help you optimize GPU-accelerated Python code and ensure your applications are fully saturating available resources.
00:00 - Introduction
0:31 - Nsight Tools JupyterLab Extension
1:22 - Nsight Systems Python Profiling
2:03 - Nsight Compute Python Profiling
2:55 - Custom Python scripts
3:36 - Conclusion
Highlights include:
The Nsight Tools JupyterLab Extension is now available. This extension integrates performance profiling features from Nsight Systems and Nsight Compute into JupyterLab, allowing detailed performance analysis of Python cells without leaving your development environment.
Learn how to use the Nsight Systems GUI to visualize your code's execution timeline and identify performance bottlenecks. With Python Backtrace sampling and NVTX-annotated Python functions you can quickly profile your code to find where threads are slow and how to boost performance.
Nsight Compute lets you profile kernel execution in detail and correlate source code with performance metrics. Explore the CPU Call Stack report and source-level profile information to validate code execution and find optimization opportunities.
Python developers can customize Nsight Developer Tools to suit their specific use cases. This includes creating custom rules for identifying slowdowns in Nsight Compute and writing multi-node analysis scripts in Nsight Systems.
Stay tuned for future releases of Nsight tools that will add more features supporting the Python community.
Download Nsight Tools JupyterLab Extension: https://pypi.org/project/jupyterlab-n...
Download Nsight Systems: https://developer.nvidia.com/nsight-s...
Download Nsight Compute: https://developer.nvidia.com/nsight-c...
See all of the Nsight tools: https://developer.nvidia.com/tools-ov...
Browse Nsight tools tutorial resources: https://developer.nvidia.com/tools-tu...
Check out the CUDA Developer Tools tutorial series to get started: • CUDA Developer Tools
Python, NVIDIA, Python profiling, GPU-accelerated Python, Nsight, JupyterLab, performance analysis, optimization, Python Backtrace sampling, NVTX, CPU Call Stack, source-level profiling, customization, multi-node analysis, deep learning, AI.
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