AWS and Microsoft push sovereign AI infrastructure, OpenAI seeks Amazon funding, and Google launches Ironwood TPU for AI inference.
An introduction to WebGPU, focusing on using its low-level API for general-purpose GPU computing rather than graphics rendering.
A guide to setting up a GPU-powered JupyterHub environment on AWS for a data science hackathon, including driver installation and configuration.
A look back at a 2018 PyData talk on end-to-end GPU data science workflows using OmniSci and RAPIDS, highlighting concepts still relevant today.
Explores GPU-based data science workflows using MapD (now OmniSci) for high-performance analytics and machine learning without data transfer bottlenecks.
A tutorial on installing OmniSci (formerly MapD) using Docker and loading data for GPU-accelerated SQL analytics and visualization.
A guide to building a custom data science workstation for GPU computing and Docker, including specs, assembly, and a later upgrade.
