Jacob Tomlinson
Jacob Tomlinson is a senior software engineer at NVIDIA. His work involves maintaining open source projects including RAPIDS and Dask. He also tinkers with kr8s in his spare time. He lives in Exeter, UK.
Sessions
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. We will discuss how to apply EffVer to your own projects and how to depend on projects that use it.
NVIDIA GPUs offer unmatched speed and efficiency for data processing and model training, significantly reducing the time and cost associated with these tasks. Using GPUs is even more tempting when you use zero-code-change plugins and libraries. You can use PyData libraries including pandas, polars and networkx without needing to rewrite your code to get the benefits of GPU acceleration. We can also mix in GPU native libraries like Numba, CuPy and pytorch to accelerate our workflows from end-to-end.
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.