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Kubetools is a tool and processes for developing and deploying microservices to Kubernetes.

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Kubetools is a tool and processes for developing and deploying microservices to Kubernetes. Say that:

  • You have a bunch of repositories, each containing one or more microservices
  • You want to deploy each of these microservices into one or more Kubernetes clusters
  • You want a single configuration file per project (repository)

And you would like:

  • Development setup should be near-instant - and not require specific K8s knowledge
  • Deployment to production can be automated - and integrated with existing CI tooling

Kubetools provides the tooling required to achieve this, by way of two CLI tools:

  • ktd: generates 100% local development environments using Docker/docker-compose
  • kubetools: deploys projects to Kubernetes, handling any changes/jobs as required

Both of these use a single configuration file, kubetools.yml, for example a basic django app:

name: my-app

      dockerfile: Dockerfile
        - ./:/opt/django_app

  - name: Upgrade database
    containerContext: django_app
    command: [./, migrate, --noinput]

  - name: Nosetests
    containerContext: django_app
    command: [./, test]

    serviceAccountName: webserver
        mountPath: /mnt/secrets-store
        secretProviderClass: webserver-secrets
        command: [uwsgi, --ini, /etc/uwsgi.conf]
        containerContext: django_app
          - 80
          command: [./, runserver, '']

        image: mariadb:v10.4.1

    batch-api-version: 'batch/v1beta1'  # Must add if k8s version < 1.21+
    schedule: "*/1 * * * *"
    concurrency_policy: "Replace"
        image: busybox
        command: [/bin/sh, -c, date; echo Hello from the Kubernetes cluster]

With this in your current directory, you can now:

# Bring up a local development environment using docker-compose
ktd up

# Deploy the project to a Kubernetes namespace
kubetools deploy my-namespace


pip install kubetools


Users can configure some aspects of kubetools. The configuration folder location depends on the operating system of the user. See the Click documentation to find the appropriate one for you. Note that we use the "POSIX" version (for example ~/.kubetools/ on Unix systems).

  • kubetools.conf contains key-value settings, see for the possible settings and their meaning.
  • scripts/ can contain scripts to be made available to ktd script command


Install the package in editable mode, with the dev extras:

pip install -e .[dev]

Local deployment testing

For deployment testing, you will need a kubernetes cluster and a docker registry. You can get both easily using minikube:

minikube start --addons registry --insecure-registry ${MINIKUBE_IP}:5000

Then you can deploy to that environment:

kubetools --context minikube deploy --default-registry ${MINIKUBE_IP}:5000 default .

MINIKUBE_IP value can vary depending on your local environment. The easiest way to get the correct value is to start minikube once then reset it:

minikube start
MINIKUBE_IP=$(minikube ip)
minikube delete

Releasing (admins/maintainers only)

  • Update CHANGELOG to add new version and document it
  • In GitHub, create a new release
    • Title the release v<version> (for example v1.2.3)
    • Select to create a new tag v<version> against master branch
    • Copy changes in the release from into the release description
    • GitHub Actions will package the release and publish it to Pypi

Mounting K8s Secrets

We assume that ServiceAccount and SecretProviderClass are already created (if needed), before deploying the project with kubetools.

Docker build args

kubetools now supports passing values for ARG parameters used in Dockerfiles, using --build-args. This has a couple of caveats though:

  • it is NOT supported in ktd. A workaround for this is to use the default value of the ARG instruction.
  • this doesn't affect the image tag pushed to the docker registry, which is based only on the git commit hash. This means that these arguments cannot be used to generate multiple images from the same Dockerfile. So their main usage should be to pass secrets that should not be recorded in the git repository but are needed at build time, to access external resources for example.
  • these values could be recorded in the docker image layer history. To prevent leaking secrets, you should consider using multi-stage builds where the secrets are only used in a "builder" image.

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