Naty Clementi
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.
Sessions
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
Geospatial analysis relies on raster data — n-dimensional arrays where each cell holds a spatial measurement. The scale of modern remote sensing data makes CPU-based workflows impractical, but raster operations are naturally parallelizable and well suited for GPU acceleration. This talk walks through a GPU-accelerated end-to-end workflow to classify satellite imagery into land cover types, covering data access via STAC, preprocessing (cloud masking, compositing, spectral index computation), training a Random Forest classifier on millions of pixels, and running inference on unseen tiles. The pipeline uses familiar APIs from Xarray, Dask, pandas, and scikit-learn, accelerated with RAPIDS. No prior geospatial or GPU experience is required.
Until very recently, producing and using reproducible scientific software environments required advanced knowledge and a strict adherence to best practices (e.g. DOI: 10.25080/majora-212e5952-028). Now, with the advent of modern tooling with lockfile-first workflows (i.e. Pixi and uv), and the emergence of lockfile standards across scientific open source, applications can be made reproducible at the digest level through tooling decisions. As this technology and practices become increasingly common there is an opportunity to define common best practices around lockfile based software development that can further reduce developer overhead and maintenance burden. This Birds of a Feather panel will focus on how experienced developers are leveraging lockfiles across software development, applications, and deployment while providing best practices and practical recommendations, while also highlighting continuing challenges and opportunities for improvement.