Opinionated platform that runs on Kubernetes to take you from Code to URL in one step.

E2E tests:

Epinio makes it easy for developers to develop their applications running in Kubernetes clusters. Easy means:

• No experience with Kubernetes is needed
• No steep learning curve
• Quick local setup with minimal configuration
• Deploying to production the same as deploying in development

Kubernetes is the standard for container orchestration. Developers may want to use Kubernetes for its benefits or because their Ops team has chosen it. In either case, working with Kubernetes can be complex depending on the environment. It has a steep learning curve and doing it right is a full-time job. Developers should spend their time working on their applications, not doing operations.

Epinio adds the needed abstractions and tools to allow developers to use Kubernetes as a PaaS.

Documentation is available at docs.epinio.io.

Requires a Kubernetes cluster, an Ingress Controller and a Cert Manager as explained in the installation documentation. Once this is in place, and leaving out DNS setup, an installation reduces to:

helm repo add epinio https://epinio.github.io/helm-charts
helm repo update

helm install --namespace epinio --create-namespace epinio epinio/epinio \
  --set global.domain=mydomain.example.com

Installation of the Epinio CLI is by downloading a binary from the release page, or usage of brew, i.e.

brew install epinio

There is further documentation here.

Our QuickStart Tutorial works through how to create a namespace and push an application.

• Slack: #epinio on Rancher Users
• Github: Discuss

Epinio uses Github Project for tracking issues.

Find more information in the Contribution Guide.

The developer documentation explains how to build and run Epinio from a git source checkout.

• Security
  • TLS secured API server
  • Basic Authentication to access the API
  • or OIDC-based token
• Epinio Clients
  • Web UI
  • Epinio CLI
• Apps
  • Push code directly without further tools or steps
  • Basic operation of your application once pushed
  • Cloud Native Buildpacks build and containerize your code for you
• Configurations
  • CRUD operations on your configuration. A configuration can be a database, SaaS etc. A configuration can be an external component or can be created using epinio configuration.
  • Bind configurations to apps.

• Rails: https://github.com/epinio/example-rails
• Java: https://github.com/spring-projects/spring-petclinic/
• Paketo Buildpack example apps: https://github.com/paketo-buildpacks/samples

Copyright (c) 2020-2023 SUSE, LLC

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Epinio 1.13 rehomes configurations for the staging workloads to a more standardized format that supports a larger variety of configs. These are no longer configured directly on the Epinio container via ENV variables or through Viper CLI flags but rather read from an in-cluster ConfigMap at staging time.

To summarize, version 1.12 referenced the following ENV variables:

STAGING_SERVICE_ACCOUNT_NAME
STAGING_RESOURCE_CPU
STAGING_RESOURCE_MEMORY
STAGING_RESOURCE_DISK

These ENV variables were configured directly by the Helm Chart in the deployment's env section. This is no longer the case as we push in the direction of improving configurability of these staging workloads. These configurations are now more expansive, including resource requests vs. limits, node tolerations, affinity criteria, etc. And as such, the ENV approach to this would become unruly as capabilities grew. Instead, we are conforming more closely to how Kubernetes would structure these configs directly in the Job YAML. These configurations are now read in from chart values into the stage script ConfigMaps. The contents of this ConfigMap, as far as the Epinio Server is concerned, would resemble the following (with examples):

serviceAccountName: "epinio-server"
ttlSecondsAfterFinished: 300
resources:
  requests:
    cpu: "200m"
    memory: "1Gi"
  # limits:
  #   cpu: ""
  #   memory: ""
storage:
  cache:
    emptyDir: false
    size: 1Gi
    storageClassName: ""
    volumeMode: Filesystem
    accessModes:
    - ReadWriteOnce
  sourceBlobs:
    emptyDir: true
    # size: 1Gi
    # storageClassName: ""
    # volumeMode: Filesystem
    # accessModes:
    # - ReadWriteOnce
nodeSelector: {}
  # kubernetes.io/os: linux
affinity: {}
  # nodeAffinity:
  #   requiredDuringSchedulingIgnoredDuringExecution:
  #     nodeSelectorTerms:
  #     - matchExpressions:
  #       - key: kubernetes.io/os
  #         operator: In
  #         values:
  #         - linux
tolerations: []
  # - key: "kubernetes.io/os"
  #   operator: "Equal"
  #   value: "linux"
  #   effect: "NoSchedule"