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Argo Workflows

Dagger runs dagger check as one step of an Argo Workflow, so the same checks you run locally run on every workflow execution — while keeping all of your existing Argo Workflows infrastructure.

tip

For checks with no workflow YAML at all, enable Cloud Checks with dagger ws autocheck on. The workflow below is the hybrid-mode alternative for when you want to drive Dagger from your existing Argo Workflows setup.

How it works​

Dagger is invoked like any other tool, as one step of an Argo Workflow. Argo Workflows executes the Dagger CLI in a container and connects to the Dagger Engine running in a sidecar container. The CLI runs dagger check against the modules installed in your workspace, and the workflow succeeds or fails based on the check results.

Prerequisites​

Example​

The following example runs dagger check for a simple Go application.

Create a file called workflow.yaml with the following content:

workflow.yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: dagger-in-argo-
spec:
  entrypoint: dagger-workflow
  volumes:
    - name: dagger-socket
      emptyDir: {}
    - name: dagger-storage
      emptyDir: {}
  templates:
    - name: dagger-workflow
      sidecars:
        - name: dagger-engine
          # replace with the latest available version of Dagger for your platform
          image: registry.dagger.io/engine:v0.21.4
          securityContext:
            privileged: true
            capabilities:
              add:
                - ALL
          readinessProbe:
            exec:
              command: ["dagger", "core", "version"]
          volumeMounts:
            - mountPath: /run/dagger
              name: dagger-socket
            - mountPath: /var/lib/dagger
              name: dagger-storage
      inputs:
        artifacts:
          - name: project-source
            path: /work
            git:
              # replace with your repository URL
              repo: https://github.com/kpenfound/greetings-api.git
              revision: "main"
          - name: dagger-cli
            path: /usr/local/bin
            mode: 0755
            http:
              # replace with the latest available version of Dagger for your platform
              url: https://github.com/dagger/dagger/releases/download/v0.21.4/dagger_v0.21.4_linux_amd64.tar.gz
      container:
        image: alpine:latest
        command: ["sh", "-c"]
        # runs the checks from the modules installed in your workspace
        args: ["dagger check"]
        workingDir: /work
        env:
        - name: "_EXPERIMENTAL_DAGGER_RUNNER_HOST"
          value: "unix:///run/dagger/engine.sock"
          # assumes the Dagger Cloud token is
          # in a Kubernetes secret named dagger-cloud
        - name: "DAGGER_CLOUD_TOKEN"
          valueFrom:
            secretKeyRef:
              name: dagger-cloud
              key: token
        volumeMounts:
          - mountPath: /run/dagger
            name: dagger-socket

A few important points to note:

  • The workflow uses hardwired artifacts to clone the Git repository and to install the Dagger CLI.
  • unix://run/dagger/engine.sock is mounted and specified with the _EXPERIMENTAL_DAGGER_RUNNER_HOST environment variable.
  • The Dagger CLI is downloaded and installed. Confirm the version and architecture are accurate for your cluster and project.
  • Setting the DAGGER_CLOUD_TOKEN environment variable is only necessary if integrating with Dagger Cloud.

When you're satisfied with the workflow configuration, run it with Argo:

argo submit -n argo --watch ./workflow.yaml

The --watch argument provides an ongoing status feed of the workflow request in Argo. To see the logs from your workflow, note the pod name and in another terminal run kubectl logs -f POD_NAME

Once the workflow has successfully completed, run it again with argo submit -n argo --watch ./workflow.yaml. Dagger's caching should result in a significantly faster second execution.

note

Argo Workflows is not a full-featured CI platform in itself, and won't directly respond to changes in repositories or have any automated triggers. To use Argo Workflows as a CI server, it should be paired with other tools like Argo Events.

Resources​

Some resources from the Dagger community that may help are listed below. If you have any questions about additional ways to use Argo Workflows with Dagger, join our Discord and ask your questions in our Kubernetes channel.

  • Video: Argo Workflows with Dagger by Kyle Penfound: In this demo, Kyle demonstrates how to use Dagger to define pipelines in code and then use argo Workflows to orchestrate the execution of those pipelines.

About Argo Workflows​

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.