2026-07-14 –, Accelerated Computing
As GPU acceleration becomes essential for scaling Python workloads, many developers face new challenges: understanding installation, managing dependencies, and deploying GPU-enabled environments. Even experienced Python users can struggle to integrate GPUs effectively or troubleshoot performance issues.
This tutorial addresses those barriers by walking participants step-by-step through the process of getting started with GPUs. Using NVIDIA’s RAPIDS ecosystem and familiar python tools, we’ll demonstrate how to set up, monitor, optimize and debug GPU-powered workflows—turning what often feels like complex infrastructure work into an approachable, reproducible process.
Installation Instructions: https://developer.nvidia.com/nsight-systems/get-started
Leveraging GPU acceleration is now a common necessity for scaling Python projects. NVIDIA GPUs offer unmatched speed and efficiency for data processing and model training, significantly reducing the time and cost associated with these tasks. GPU acceleration is already baked into many projects, or available via plugins. You can use PyData libraries including pandas, polars and networkx without needing to rewrite your code to get the benefits of GPU acceleration.
However, integrating GPUs into our workflow can be a new challenge where we need to learn about installation, dependency management, and deployment in the Python ecosystem. When writing code, we also need to monitor performance, leverage hardware effectively, and debug when things go wrong.
This is where RAPIDS and its tooling ecosystem comes to the rescue. RAPIDS, is a collection of open source software libraries to execute end-to-end data pipelines on NVIDIA GPUs using familiar PyData APIs. RAPIDS libraries give users access to GPU acceleration, reducing execution time and cost, but without needing to learn a whole new set of tools.
In this tutorial we will cover:
- A high level overview of popular Python libraries that have GPU acceleration
- Answers to questions like: “Where do I get a GPU?”, “How do I run a container on a VM with a GPU?”, “How do I install GPU packages into an existing environment?”, “What if I use uv pip?”, “What about conda? ”as well as follow along examples to get a GPU up and running.
- The GPU software stack from driver to Python and everything in between
- Troubleshooting and monitoring: Examples of performance analysis, diagnostics, and debugging. Showcasing of diagnostic tools like nvdashboard, nvtop, nsys, pynvml, etc.
Audience
This is a hands-on tutorial, participants should ideally have some experience using Python, pandas and sci-kit learn. We'll use cloud-based VMs, so familiarity with the cloud and resource creation is helpful but not required. No prior GPU knowledge is needed.
To maximize the tutorial's relevance, we will provide participants with the opportunity to submit their specific environment configurations ahead of time. Submissions received with adequate notice (between tutorial acceptance and conference date) will be integrated into the tutorial examples, allowing participants to see their real-world use cases addressed.
Key takeaways for participants will be:
- An understanding of the GPU Python software stack from driver through core libraries to high-level Python libraries
- How they can use their preferring tooling and package managers to install all the components they need
- How to monitor their GPUs and understand how well they are using their hardware
- How to attach debuggers to their GPU code or record traces and profiles for debugging later
This is a hands-on tutorial, participants should ideally have some experience using Python, pandas and sci-kit learn. We'll use cloud-based VMs, so familiarity with the cloud and resource creation is helpful but not required. No prior GPU knowledge is needed.
Installation Instructions:
Naty Clementi is a senior software engineer at NVIDIA. She is a former academic with a Masters in Physics and PhD in Mechanical and Aerospace Engineering to her name. Her work involves contributing to RAPIDS, and in the past she has also contributed and maintained other open source projects such as Ibis and Dask. She is an active member of PyLadies and an active volunteer and organizer of Women and Gender Expansive Coders DC meetups.
Jaya Venkatesh is a Software Engineer at NVIDIA, working on the RAPIDS ecosystem to streamline the deployment of GPU-accelerated data science workflows across cloud and distributed systems. Previously, he was a Machine Learning Engineer at Pixxel Space, where he developed large-scale, real-time data processing and inference pipelines for Earth observation using GPU-accelerated Python libraries.