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Update codefresh-runner.md
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‎_docs/administration/codefresh-runner.md‎

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@@ -190,7 +190,7 @@ Make sure you have [installed the Codefresh Runner](#codefresh-runner-installati
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**How to**
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1. Run`kubectl edit deployment runner -n codefresh-runtime` and add the proxy variables:
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```yaml
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```
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spec:
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containers:
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- env:
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#### Configuration
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{:start="1"}
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1. Create Storage Class for EBS volumes:
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>Choose **one** of the Availability Zones (AZs)to be used for your pipeline builds. Multi AZ configuration is not supported.
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@@ -349,9 +350,9 @@ codefresh get runtime-environments my-eks-cluster/codefresh -o yaml > runtime.ya
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```
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{:start="4"}
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1. Modify the YAML:
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* In`dockerDaemonScheduler.cluster`, add`nodeSelector: topology.kubernetes.io/zone:<your_az_here>`.
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> Make sure you define the same AZ you selected for Runtime Configuration.
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* Modify`pvcs.dind` to use the Storage Class you created above (`dind-ebs`).
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* In`dockerDaemonScheduler.cluster`, add`nodeSelector: topology.kubernetes.io/zone:<your_az_here>`.
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> Make sure you define the same AZ you selected for Runtime Configuration.
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* Modify`pvcs.dind` to use the Storage Class you created above (`dind-ebs`).
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Here is an example of the`runtime.yaml` including the required updates:
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```yaml
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```
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**With the values`values-example.yaml` file:**
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```yaml
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...
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### Storage parameter example for GCE disks
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autoScaler: true
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availabilityZones: ["us-west-2a", "us-west-2b", "us-west-2c"]
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```
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{:start="2"}
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1. Execute:
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```shell
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eksctl create cluster -f my-eks-cluster.yaml
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#### Step 2: Install autoscaler on EKS cluster
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Once the cluster is up and running, install the [cluster autoscaler](https://docs.aws.amazon.com/eks/latest/userguide/cluster-autoscaler.html){:target="\_blank"}:
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Once the cluster is up and running, install the [cluster autoscaler](https://docs.aws.amazon.com/eks/latest/userguide/cluster-autoscaler.html){:target="\_blank"}.
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Because we used IAM AddonPolicies`"autoScaler: true"` in the`cluster.yaml` file, everything is done automatically, and there is no need to create a separate IAM policy or add Auto Scaling group tags.
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--set-value=Storage.KmsKeyId=<key id>
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```
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For descriptions of the other options, run`codefresh runner init --help` ([global parameter table](#customizing-the-wizard-installation)).
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{:start="3"}
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1. When the Wizard completes the installation, modify the runtime environment of`my-aws-runner` to specify the necessary toleration, nodeSelector and disk size:
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* Run:
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**How to**
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1. If you use AKS with managed [identitiesfor node group](https://docs.microsoft.com/en-us/azure/aks/use-managed-identity), you can run the script below to assign`CodefreshDindVolumeProvisioner` role to AKS node identity:
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```
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export ROLE_DEFINITIN_FILE=dind-volume-provisioner-role.json
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export SUBSCRIPTION_ID=$(az account show --query"id"| xargsecho)
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az role definition create --role-definition @${ROLE_DEFINITIN_FILE}
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az role assignment create --assignee$NODE_SERVICE_PRINCIPAL --scope /subscriptions/$SUBSCRIPTION_ID/resourceGroups/$NODES_RESOURCE_GROUP --role CodefreshDindVolumeProvisioner
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```
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{:start="2"}
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1. Install Codefresh Runner using one of these options:
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* CLI Wizard:
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```
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codefresh runner init --set-value Storage.Backend=azuredisk --set Storage.VolumeProvisioner.MountAzureJson=true
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```
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* [values-example.yaml](https://github.com/codefresh-io/venona/blob/release-1.0/venonactl/example/values-example.yaml){:target="\_blank"}:
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```yaml
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* [values-example.yaml](https://github.com/codefresh-io/venona/blob/release-1.0/venonactl/example/values-example.yaml){:target="\_blank"}:
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```
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Storage:
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Backend: azuredisk
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VolumeProvisioner:
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codefresh runner init --values values-example.yaml
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```
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* Helm chart [values.yaml](https://github.com/codefresh-io/venona/blob/release-1.0/charts/cf-runtime/values.yaml){:target="\_blank"}:
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```yaml
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```
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storage:
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backend: azuredisk
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azuredisk:
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volumeProvisioner:
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mountAzureJson: true
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```
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```
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helm install cf-runtime cf-runtime/cf-runtime -f ./generated_values.yaml -f values.yaml --create-namespace --namespace codefresh
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```
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The following scenario is an example of how to set up ARM Runner on existing EKS cluster:
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**Step 1: Preparing nodes**
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**Step 1: Preparing nodes**
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1. Create new ARM nodegroup:
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1. Create new ARM nodegroup:
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```shell
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eksctl utils update-coredns --cluster <cluster-name>
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eksctl utils update-kube-proxy --cluster <cluster-name> --approve
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--nodes-max <4>\
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--managed
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```
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1. Check nodes status:
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```shell
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kubectl get nodes -l kubernetes.io/arch=arm64
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```
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1. Also it's recommeded to label and taint the required ARM nodes:
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```shell
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kubectl taint nodes<node> arch=aarch64:NoSchedule
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kubectl label nodes<node> arch=arm
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```
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1. Install the Runner:
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```shell
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codefresh runner init --values values-arm.yaml --exec-demo-pipelinefalse --skip-cluster-integrationtrue
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```
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**Step 3 - Post-installation fixes**
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1. Change`engine` image versionin Runtime Environment specification:
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```shell
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# get the latest engine ARM64 tag
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curl -X GET"https://quay.io/api/v1/repository/codefresh/engine/tag/?limit=100" --silent| jq -r'.tags[].name'| grep"^1.*arm64$"
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1.136.1-arm64
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```
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```shell
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# get runtime spec
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codefresh get re$RUNTIME_NAME -o yaml> runtime.yaml
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```
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1. Under`runtimeScheduler.image` change image tag:
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```yaml
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runtimeScheduler:
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image:'quay.io/codefresh/engine:1.136.1-arm64'
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```
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```shell
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# patch runtime spec
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codefresh patch re -f runtime.yaml
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```
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1. For`local` storage patch`dind-lv-monitor-runner` DaemonSet and add`nodeSelector`:
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```shell

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