Jacob Tomlinson
Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source Kubernetes Python framework kr8s in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.
he/him
NVIDIA
Senior Software Engineer
Session
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.
In this tutorial we will cover:
- 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.
- Troubleshooting and monitoring: Examples of performance analysis, diagnostics, and debugging. Showcasing of diagnostic tools like nvdashboard, nvtop, nsys, pynvml, etc.