Jaya Venkatesh
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.
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.