Jacob Tomlinson

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.

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.

As coding agents grow in popularity, open source project documentation is increasingly consumed by LLMs. When people build things with your open source library their agent will read your documentation and write code based on what it discovers there.

Geospatial analysis often relies on raster data, n‑dimensional arrays where each cell holds a spatial measurement. Many raster operations, such as computing indices, statistical analysis, and classification, are naturally parallelizable and ideal for GPU acceleration.

Many notable PyData projects including Jupyter Hub, Matplotlib and JAX follow a versioning scheme called EffVer, where instead of making promises around backward compatibility they communicate the likelihood and magnitude of the work required to adopt a new version. In this talk we will dive into EffVer, what it is and what it means for developers and users.

In today’s world of ever-growing data and AI, learning about GPUs has become an essential part of software carpentry, professional development and the education curriculum. However, teaching with GPUs can be challenging, from resource accessibility to managing dependencies and varying knowledge levels.

Accelerating Python using the GPU is much easier than you might think. We will explore the powerful CUDA-enabled Python ecosystem in this tutorial through hands-on examples using some of the most popular accelerated scientific computing libraries.

Accelerating Python using the GPU is much easier than you might think. We will explore the powerful CUDA-enabled Python ecosystem in this tutorial through hands-on examples using some of the most popular accelerated scientific computing libraries.

Since joining NVIDIA I’ve gotten to grips with the fundamentals of writing accelerated code in Python. I was amazed to discover that I didn’t need to learn C++ and I didn’t need new development tools.

Dask is a flexible library for parallel computing in Python. Dask provides high-level interfaces to extend the PyData ecosystem to larger-than-memory or distributed environments, as well as lower-level interfaces to customise workflows. No previous experience is required, though knowledge of...

This year I built a library that already exists. The existing solutions didn’t quite meet my needs, I wanted something that ticked all of my boxes. When thinking about building something new people referred me to xkcd #927. But I did it anyway.