Making Humans and Code GPU-Capable at Mailchimp

What happens when you have a bunch of data scientists, a bunch of new and old projects, a big grab-bag of runtime environments, and you need to get all those humans and all that code access to GPUs? Come see how the ML Eng team at Mailchimp wrestled first with connecting abstract containerized processes to very-not-abstract hardware, then scaled that process across tons of humans and projects. We’ll talk through the technical how-to with Docker, Nvidia, and Kubernetes, but all good ML Engineers know that wrangling the tech is only half the battle and the human factors can be the trickiest part. 3 Key Takeaways: An overview of the call stack from container, orchestration framework, OS, and all the way down to real GPU hardware How ML Eng at Mailchimp provides GPU-compatible dev environments for many different projects and data scientists An experienced take on how to balance data scientist’s human needs against heavy system optimization (spoiler alert: favor the humans)