Obsolete Document

The maintainers of EDD are working with the application as a continuous delivery project, and are not publishing explicit versioned packages or Docker images. The last official release of EDD is 2.6.6 from November 2020. Any new users should work off the trunk branch of the repo, and use their own release process for deployment. This document is kept as an historical example on one way to do that release process.

Release Process

This document is an outline of the steps that should be taken to make a new release of EDD. The instructions are mostly historical at this point. All build and release actions within JBEI / EmeryStation East are automated in CI.

Operations in git repository

For Major (N+1.0.0) or Minor (X.N+1.0) versions, create a date-stamped release branch from trunk for the release-candidate. For example, if starting a 4.0.0 release series on July 1st, 2019; a branch named release/20190701 is started from trunk. Commits working toward a final release artifact get added to this branch.

For Bugfix (X.Y.N+1) versions, add commits to the Major/Minor release branch. The exact commit used to create a final release artifact is to be tagged with the X.Y.Z version number for the released artifact. Multiple commits may exist between bugfix version tags.

Operations to build release artifacts

Notes on flags to docker build commands:

  • --pull makes sure to pull the latest version of the base image from Docker Hub
  • --no-cache makes sure to build from scratch, without build cache
  • -t [NAME] tags the resulting image; otherwise image is only accessible from its hash ID

Build the Docker image for jbei/edd-node in ${BASE}/docker/node/ directory. This image is responsible for the TypeScript build environment and creates the static script and stylesheet assets for EDD. The below command builds and tags the image with latest and the X.Y.Z version:

docker build \
    --pull \
    --no-cache \
    -t jbei/edd-node \
    -t jbei/edd-node:X.Y.Z \
    .

Build the Docker image for jbei/scikit-learn in ${BASE}/docker/edd/scikit-learn directory. This image is the base Python with Numpy, SciPy, Scikit-learn environment, that would otherwise take too long to build every time. This image should remain stable for longer periods. Release schedule is TBD, see README in the directory. The below command builds and tags the image:

docker build \
    --pull \
    --no-cache \
    -t jbei/scikit-learn \
    .

Build the Docker image for jbei/edd-core directly in the ${BASE} directory. Make sure to locally build jbei/edd-node and jbei/scikit-learn first. The below command builds and tags the image with latest and the X.Y.Z version:

DOCKER_BUILDKIT=1 docker build \
    -t jbei/edd-core \
    -t jbei/edd-core:X.Y.Z \
    -f docker/edd/core/Dockerfile \
    --build-arg "TARGET=prod" \
    --build-arg "EDD_VERSION=X.Y.Z" \
    .

For other Docker images under ${BASE}/docker/ directory, services should be tagged with the upstream service version. For example, custom build of Postgres 9.6 would get tagged jbei/postgres:9.6. Also tag custom builds of services with the ISO date of the build. For example, a build on July 1st, 2019 of Postgres 9.6 would get tagged jbei/postgres:9.6-20190701.

Deploying Releases

Push images to Docker Hub with docker push [IMAGENAME]. On the deploy host, Pull images from Docker Hub with docker pull [IMAGENAME]. Checkout or pull updated code/configs from git. For smallest downtime, use docker compose up -d to detect containers that need to be re-created, and automatically stop, remove, and re-create them.