Python wrapper on kubectl that makes deploying easy.
Requires python 3.5 and configured kubectl. To install run:
pip3 install kubepy
You can use this package to install all yml definitions from given directory.
Just run kubepy-apply-all from a directory where all of you Kubernetes definition yml files are.
Supported Kubernetes resources:
- Deployment
- StatefulSet
- Job
- Pod (used to run a one-off command)
- Service
- Ingress
- Secret
- StorageClass
- PersistentVolume
- PersistentVolumeClaim
- PodDisruptionBudget
Options:
--directory <path>- uses path instead of local directory. Can be used multiple times to add new and partially override existing definitions.--build-tag <tag>- sets tag to all images without specified tag in your definition files--replace- if present, replaces deployments instead of updating them. Default: false.--host-volume <name>=<path>Adds host volume to each pod definition. Can be used multiple times.--env <VAR>=valueSets environment variable on every container.--max-job-retries <n>While waiting for job to finish if it fails n times than delete job and fail. Job sometimes can still be executed more than n times.
There is also kubepy-apply-one command which is called as kubepy-apply-one name1 [name2 ...]
It applies only files selected files. Names should be without ".yml".
It accepts all options from kubepy-apply-all. Additionally you can pass option:
--show-definition- shows definition instead of applying them.
Applying usualy means that underlying kubectl apply or kubectl replace is called. However applying job is treated
differently.
To ensure that job finished and succeeded kubectl waits for job to finish and fails if job failed.
Usually you don't need to apply pods manually, but if you want to run some kind of check and you need to know if it
succeeded without retries then you can use pod with restartPolicy: Never. Only pods with this policy are currently
supported. They are treated as jobs, so applying waits for them to finish and fails if they fail.