This post comes from wanting a repeatable way to get a one-off CSV into Postgres as the first step of a machine learning pipeline, with everything running on Kubernetes locally. I set up a local Kubernetes cluster with Docker for Mac, install Postgres and the dashboard with Helm, and then use a Kubernetes job with pgfutter to ingest the CSV into the database. It is overkill for a single CSV, but the point is to have a standardized pipeline I can reuse and later extend to model training.

In 2015, Google released TensorFlow, a new deep learning framework and tensor library that was similar in many ways to Theano. I enjoy using it a lot more than Theano, mostly because of Theano’s long compile times when using Keras and TensorBoard. This post will not go into detail about using Theano, TensorFlow, or Keras. It is about how I built a Docker image for a slightly older NVIDIA card, which, for my purposes, is capable of using multiple GPUs in isolation and executing a model on one card without affecting the other.