using Docker at Fred Hutch

Updated: July 11, 2023

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.

Accessing Docker

You can either install Docker on your own machine or you can run Apptainer.

On Your Local Computer

It’s recommended (but not required) that you install Docker on your workstation and develop your image on your own machine until it’s ready to be deployed.

Using pre-made Docker images with application stacks

You may not need to create your own Docker image, but that depends on what you want to do. If you are using software that is readily available, there is probably already a Docker image containing that software. Look around on Docker Hub to see if there’s already a Docker image available.

SciComp is also developing Docker images that contain much of the software you are used to finding in /app on the rhino machines the and gizmo clusters (here’s the R image).

If you’ve found an existing Docker image that meets your needs, you don’t need to read the rest of this section.

Create your own Docker image and put your software inside

Create GitHub Account

You really need a GitHub account to properly build docker containers. Please see our Shiny app example on how to create your own docker image

Deploy your Docker image to production

Your own machine or the SciComp test farm are likely poor choices for running production level applications in containers. For production deployments you will need a GitHub account. (see above)

Using docker hub

Dockerhub is a good choice for fully public open source projects

Push your Dockerfile to a GitHub repository

More to come.

Create an Automated Build in Docker Hub

More to come.

Create a Job Definition

Using the Fred Hutch Container environment

SciComp maintains a Docker Swarm for hosting applications. Applications can be set up to be accessible only inside the Fred Hutch network, or to be available on the public internet.

If you want to deploy an application, please see this page. Please note that we can deploy any type of application that can be containerized, despite using terminology specific to R’s Shiny package.

Updated: July 11, 2023

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.