# Argo Argo is an open-source workflow engine built for Kubernetes with integrated functionalities of Continuous Integration (CI) and Continuous Delivery (CD). The source code can be accessed openly at . ## Installing Argo ### Using `argo install` command ```bash # Download Argo. curl -sSL -o argo https://github.com/argoproj/argo/releases/download/v2.1.0/argo-linux-amd64 chmod +x argo sudo mv argo /usr/local/bin/argo # Deploy to Kubernetes kubectl create namespace argo argo install -n argo ``` ```bash ACCESS_KEY=AKIAIOSFODNN7EXAMPLE ACCESS_SECRET_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY helm install --namespace argo --name argo-artifacts --set accessKey=$ACCESS_KEY,secretKey=$ACCESS_SECRET_KEY,service.type=LoadBalancer stable/minio ``` Next, create a bucket named `argo-bucket` (You can use `kubectl port-forward service/argo-artifacts-minio :9000` to reach the Minio UI for operation): ```bash # download mc client sudo wget https://dl.minio.io/client/mc/release/linux-amd64/mc -O /usr/local/bin/mc sudo chmod +x /usr/local/bin/mc # create argo-bucket EXTERNAL_IP=$(kubectl -n argo get service argo-artifacts-minio -o jsonpath='{.status.loadBalancer.ingress[0].ip}') mc config host add argo-artifacts-minio-local http://$EXTERNAL_IP:9000 $ACCESS_KEY $ACCESS_SECRET_KEY --api=s3v4 mc mb argo-artifacts-minio-local/argo-bucket ``` Then, modify the Argo workflow controller to use Minio: ```bash $ kubectl -n argo create secret generic argo-artifacts-minio --from-literal=accesskey=$ACCESS_KEY --from-literal=secretkey=$ACCESS_SECRET_KEY $ kubectl edit configmap workflow-controller-configmap -n argo ... executorImage: argoproj/argoexec:v2.0.0 artifactRepository: s3: bucket: argo-bucket endpoint: argo-artifacts-minio.argo:9000 insecure: true # accessKeySecret and secretKeySecret are secret selectors. # It references the k8s secret named 'argo-artifacts-minio' # which was created during the minio helm install. The keys, # 'accesskey' and 'secretkey', inside that secret are where the # actual minio credentials are stored. accessKeySecret: name: argo-artifacts-minio key: accesskey secretKeySecret: name: argo-artifacts-minio key: secretkey ``` ### Installing Using Helm > Please note: The current Helm Charts use an outdated version of Minio and the deployment might fail. ```bash # Download Argo. curl -sSL -o /usr/local/bin/argo https://github.com/argoproj/argo/releases/download/v2.0.0/argo-linux-amd64 chmod +x /usr/local/bin/argo # Deploy to Kubernetes helm repo add argo https://argoproj.github.io/argo-helm/ kubectl create clusterrolebinding default-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default helm install argo/argo-ci --name argo-ci --namespace=kube-system ``` ## Accessing the Argo UI ```bash $ kubectl -n argo port-forward service/argo-ui :80 Forwarding from 127.0.0.1:52592 -> 8001 Forwarding from [::1]:52592 -> 8001 # Open browser and visit 127.0.0.1:52592 ``` ## Working with workflows Firstly, give the default ServiceAccount cluster administration permission ```bash # Authz yourself if you are not admin. kubectl create clusterrolebinding default-admin --clusterrole=cluster-admin --serviceaccount=argo:default ``` Example 1: The simplest workflow ```yaml apiVersion: argoproj.io/v1alpha1 kind: Workflow metadata: generateName: hello-world- spec: entrypoint: whalesay templates: - name: whalesay container: image: docker/whalesay:latest command: [cowsay] args: ["hello world"] ``` ```bash argo -n argo submit https://raw.githubusercontent.com/argoproj/argo/master/examples/hello-world.yaml ``` Example 2: Workflow with multiple containers ```yaml # This example demonstrates the ability to pass artifacts # from one step to the next. apiVersion: argoproj.io/v1alpha1 kind: Workflow metadata: generateName: artifact-passing- spec: entrypoint: artifact-example templates: - name: artifact-example steps: - - name: generate-artifact template: whalesay - - name: consume-artifact template: print-message arguments: artifacts: - name: message from: "{{steps.generate-artifact.outputs.artifacts.hello-art}}" - name: whalesay container: image: docker/whalesay:latest command: [sh, -c] args: ["cowsay hello world | tee /tmp/hello_world.txt"] outputs: artifacts: - name: hello-art path: /tmp/hello_world.txt - name: print-message inputs: artifacts: - name: message path: /tmp/message container: image: alpine:latest command: [sh, -c] args: ["cat /tmp/message"] ``` ```bash argo -n argo submit https://raw.githubusercontent.com/argoproj/argo/master/examples/artifact-passing.yaml ``` After the workflows are created, their status and logs can be queried, and they can be deleted when no longer needed: ```bash $ argo list NAME STATUS AGE DURATION artifact-passing-65p6g Running 6s 4s hello-world-cdnpq Running 8s 6s $ argo -n argo logs hello-world-4dhg8 _____________ < hello world > ------------- \ \ \ ## . ## ## ## == ## ## ## ## === /""""""""""""""""___/ === ~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ / ===- ~~~ \______ o __/ \ \ __/ \____\______/ $ argo -n argo delete hello-world-4dhg8 Workflow 'hello-world-4dhg8' deleted ``` For more information on the format of workflow YAML files, see the [official documentation](https://argoproj.github.io/argo-workflows/). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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