Codefresh Runner

Run Codefresh pipelines on your private Kubernetes cluster

Install the Codefresh Runner on your Kubernetes cluster to run pipelines and access secure internal services without compromising on-premises security requirements. These pipelines run on your infrastructure, even behind the firewall, and keep code on your Kubernetes cluster secure.

Skip to quick installation →

Note: a runner installation is needed for each cluster running Codefresh pipelines. A runner is not needed in clusters used for deployment. It is possible to deploy applications on clusters other than the ones the runner is deployed on.

The installation process takes care of all runner components and other required resources (config-maps, secrets, volumes).

Prerequisites

To use the Codefresh runner the following is required:

1. A Kubernetes cluster with outgoing internet access (for version later than 1.10). Each node should have 50GB disk size.
2. A container runtime, such as docker, containerd or cri-o. Note that the runner is not dependent on any special dockershim features, so any compliant container runtime is acceptable. The docker socket/daemon used by Codefresh pipelines is NOT the one on the host node (as it might not exist at all in the case of containerd or cri-o), but instead an internal docker daemon created/managed by the pipeline itself.
3. A Codefresh account with the Hybrid feature enabled.
4. A Codefresh CLI token that will be used to authenticate your Codefresh account.

The runner can be installed from any workstation or laptop with access (i.e. via kubectl) to the Kubernetes cluster running Codefresh builds. The Codefresh runner will authenticate to your Codefresh account by using the Codefresh CLI token.

Installation with the Quick-start Wizard

Install the Codefresh CLI

npm install -g codefresh

Alternative install methods

Authenticate the CLI

codefresh auth create-context --api-key {API_KEY}

You can obtain an API Key from your user settings page.

Note: access to the Codefresh CLI is only needed once during the Runner installation. After that, the Runner will authenticate on it own using the details provided. You do NOT need to install the Codefresh CLI on the cluster that is running Codefresh pipelines.

Then run the wizard with the following command:

codefresh runner init

or

codefresh runner init --token <my-token>

Brefore proceeding with installation, the wizard asks you some basic questions.

The wizard also creates and runs a sample pipeline that you can see in your Codefresh UI.

That’s it! You can now start using the Runner.

You can also verify your installation with:

codefresh runner info

During installation you can see which API token will be used by the runner (if you don’t provide one). The printed token is used by the runner to talk to the Codefresh platform carrying permissions that allow the runner to run pipelines. If you save the token, it can later be used to restore the runner’s permissions without creating a new runner installation, if the deployment is deleted.

Customizing the Wizard Installation

You can customize the wizard installation by passing your own values in the init command. For example you can specify the runtime to be used in advance with:

codefresh runner init --kube-namespace my-codefresh-namespace

Here are all the possible installation options

Alternatively, you can pass a values.yml file to the init command that includes all installation settings:

codefresh runner init --values values.yaml

You can use this example as a starting point for your values file.

Inspecting the Manifests Before they are Installed

If you want to see what manifests are used by the installation wizard you can supply the --dry-run parameter in the installation process.

codefresh runner init --dry-run

This will execute the wizard in a special mode that will not actually install anything in your cluster. After all configuration questions are asked, all Kubernetes manifests used by the installer will be instead saved locally in a folder ./codefresh_manifests.

You can then inspect the manifests locally or edit/apply them manually if you wish.

Installing Codefresh Runner with Helm

To install the Codefresh Runner using Helm, follow these steps:

1. Download the Codefresh CLI and authenticate it with your Codefresh account. Click here for more detailed instructions.

2. Run the following command to create all of the necessary entities in Codefresh:

    codefresh runner init --generate-helm-values-file
   

   • This will not install anything on your cluster, except for running cluster acceptance tests, (which may be skipped using the --skip-cluster-test option).
   • This command will also generate a generated_values.yaml file in your current directory, which you will need to provide to the helm install command later.

3. Now run the following to complete the installation:

    helm repo add cf-runtime https://h.cfcr.io/codefresh-inc/runtime
       
    helm install cf-runtime cf-runtime/cf-runtime -f ./generated_values.yaml --create-namespace --namespace codefresh
   

4. At this point you should have a working Codefresh Runner. You can verify the installation by running:

    codefresh runner execute-test-pipeline --runtime-name <runtime-name>
   

Using the Codefresh Runner

Once installed, the Runner is fully automated. It polls the Codefresh SAAS (by default every ten seconds) on its own and automatically creates all resources needed for running pipelines.

Once installation is complete, you should see the cluster of the runner as a new Runtime environment in Codefresh in your Account Settings, in the respective tab.

If you have multiple environments available, you can change the default (shown with a thin blue border) by clicking on the 3 dot menu on the right of each environment. The Codefresh runner installer comes with a set-default option that is automatically set by default in the new runtime environment.

You can even override the runtime environment for a specific pipeline by specifying in the respective section in the pipeline settings.

Monitoring the Runner

Once installed, the runner is a normal Kubernetes application like all other applications. You can use your existing tools to monitor it.

Only the runner pod is long living inside your cluster. All other components (such as the engine) are short lived and exist only during pipeline builds. You can always see what the Runner is doing by listing the resources inside the namespace you chose during installation:

$ kubectl get pods -n codefresh-runtime
NAME                                              READY   STATUS    RESTARTS   AGE
dind-5ee7577017ef40908b784388                     1/1     Running   0          22s
dind-lv-monitor-runner-hn64g                      1/1     Running   0          3d
dind-lv-monitor-runner-pj84r                      1/1     Running   0          3d
dind-lv-monitor-runner-v2lhc                      1/1     Running   0          3d
dind-volume-provisioner-runner-64994bbb84-lgg7v   1/1     Running   7          3d
engine-5ee7577017ef40908b784388                   1/1     Running   0          22s
monitor-648b4778bd-tvzcr                          1/1     Running   0          3d
runner-5d549f8bc5-7h5rc                           1/1     Running   0          3d

In the same manner you can list secrets, config-maps, logs, volumes etc. for the Codefresh builds.

Removing the Codefresh Runner

You can uninstall the Codefresh runner from your cluster by running:

codefresh runner delete

A wizard, similar to the installation wizard, will ask you questions regarding your cluster before finishing with the removal.

Like the installation wizard, you can pass the following options in advance as command line parameters:

System Requirements

Once installed the runner uses the following pods:

• runner - responsible for picking tasks from the Codefresh UI
• engine - responsible for running pipelines
• dind - responsible for building and using Docker images
• dind-volume-provisioner
• dind-lv-monitor

CPU/Memory

The following table shows MINIMUM resources for each component:

Components that are always on consume resources all the time. Components that are not always on only consume resources when pipelines are running (they are created and destroyed automatically for each pipeline).

Node size and count will depend entirely on how many pipelines you want to be “ready” for and how many will use “burst” capacity.

• Ready (nodes): Lower initialization time and faster build times.
• Burst (nodes): High initialization time and slower build times. (Not recommended)

The size of your nodes directly relates to the size required for your pipelines and thus it is dynamic. If you find that only a few larger pipelines require larger nodes you may want to have two Codefresh Runners associated to different node pools.

Storage Space

For the storage space needed by the dind pod we suggest:

• Local SSD in the case of GCP
• EBS in the case of Amazon. See also the notes about getting caching working.

Networking Requirements

• dind - this pod will create an internal network in the cluster to run all the pipeline steps
• dind needs outgoing/egress access to Dockerhub and quay.io
• runner - this pod needs outgoing/egress access to g.codefresh.io
• runner needs network access to app-proxy (if app-proxy is used)
• engine - this pod needs outgoing/egress access to g.codefresh.io, *.firebaseio.com and quay.io
• engine - this pod needs network access to dind pod

All CNI providers/plugins are compatible with the runner components.

Upgrading from the previous version of the Runner

If you are still running the old version of the Codefresh runner (that is using the Venona installer) you can upgrade to the new version with

codefresh runner upgrade

and follow the wizard prompts.

Optional Installation of the App Proxy

The App Proxy is an optional component of the runner that once installed:

• Enables you to automatically create webhooks for Git in the Codefresh UI (same as the SAAS experience)
• Sends commit status information back to your Git provider (same as the SAAS experience)
• Makes all Git Operations in the GUI work exactly like the SAAS installation of Codefresh

The requirements for the App proxy is a Kubernetes cluster that:

1. has already the Codefresh runner installed
2. has an active ingress controller
3. allows incoming connections from the VPC/VPN where users are browsing the Codefresh UI. The ingress connection must have a hostname assigned for this route and must be configured to perform SSL termination

Currently the App-proxy works only for Github (SAAS and on-prem versions), Gitlab (SAAS and on-prem versions) and Bitbucket server. We are soon adding support for other Git providers.

Here is the architecture of the app-proxy:

Basically when a Git GET operation takes place, the Codefresh UI will contact the app-proxy (if it is present) and it will route the request to the backing Git provider. The confidential Git information never leaves the firewall premises and the connection between the browser and the ingress is SSL/HTTPS.

The app-proxy has to work over HTTPS and by default it will use the ingress controller to do its SSL termination. Therefore, the ingress controller will need to be configured to perform SSL termination. Check the documentation of your ingress controller (for example nginx ingress). This means that the app-proxy does not compromise security in any way.

To install the app-proxy on a Kubernetes cluster that already has a Codefresh runner use the following command:

codefresh install app-proxy --host=<hostname-of-ingress>

If you want to install the Codefresh runner and app-proxy in a single command use the following:

codefresh runner init --app-proxy --app-proxy-host=<hostname-of-ingress>

If you have multiple ingress controllers in the Kubernetes cluster you can use the ingress-class parameter to define which ingress will be used. For additional security you can also define an allowlist for IPs/ranges that are allowed to use the ingress (to further limit the web browsers that can access the Ingress). Check the documentation of your ingress controller for the exact details.

By default the app-proxy ingress will use the path hostname/app-proxy. You can change that default by using the values file in the installation with the flag --values values.yaml. See the AppProxy section in the example values.yaml.

Manual Installation of Runner Components

If you don’t want to use the wizard, you can also install the components of the runner yourself.

The Codefresh runner consists of the following:

• Runner - responsible for getting tasks from the platform and executing them. One per account. Can handle multiple runtimes
• Runtime - the components that are responsible on runtime for the workflow execution :
  • Volume provisioner - (pod’s name prefix dind-volume-provisioner-runner) - responsible for volume provisioning for dind pod
  • lv-monitor - (pod’s name perfix dind-lv-monitor-runner) - daemonset - responsible for cleaning volumes

To install the runner on a single cluster with both the runtime and the agent, execute the following:

kubectl create namespace codefresh
codefresh install agent --agent-kube-namespace codefresh --install-runtime

You can then follow the instructions for using the runner.

Installing Multiple runtimes with a Single Agent

It is also possible, for advanced users to install a single agent that can manage multiple runtime environments.

NOTE: Please make sure that the cluster where the agent is installed has network access to the other clusters of the runtimes

# 1. Create namespace for the agent: 
kubectl create namespace codefresh-agent

# 2. Install the agent on the namespace ( give your agent a unique name as $NAME):
# Note down the token and use it in the second command.
codefresh create agent $NAME
codefresh install agent --token $TOKEN --kube-namespace codefresh-agent
codefresh get agents

# 3. Create namespace for the first runtime:
kubectl create namespace codefresh-runtime-1

# 4. Install the first runtime on the namespace
# 5. the runtime name is printed
codefresh install runtime --runtime-kube-namespace codefresh-runtime-1

# 6. Attach the first runtime to agent:
codefresh attach runtime --agent-name $AGENT_NAME --agent-kube-namespace codefresh-agent --runtime-name $RUNTIME_NAME --runtime-kube-namespace codefresh-runtime-1

# 7. Restart the runner pod in namespace `codefresh-agent`
kubectl delete pods $RUNNER_POD

# 8. Create namespace for the second runtime
kubectl create namespace codefresh-runtime-2

# 9. Install the second runtime on the namespace
codefresh install runtime --runtime-kube-namespace codefresh-runtime-2

# 10. Attach the second runtime to agent and restart the Venoa pod automatically
codefresh attach runtime --agent-name $AGENT_NAME --agent-kube-namespace codefresh-agent --runtime-name $RUNTIME_NAME --runtime-kube-namespace codefresh-runtime-2 --restart-agent

Configuration Options

You can fine tune the installation of the runner to better match your environment and cloud provider.

Volume Reusage Policy

The behavior of how the volumes are reused depends on volume selector configuration. reuseVolumeSelector option is configurable in runtime environment spec.

The following options are available:

• reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName' - determined PV can be used by ANY pipeline of your account (it’s a default volume selector).
• reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName,pipeline_id' - determined PV can be used only by a single pipeline.
• reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName,pipeline_id,io.codefresh.branch_name' - determined PV can be used only by single pipeline AND single branch.
• reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName,pipeline_id,trigger' - determined PV can be used only by single pipeline AND single trigger.

To change volume selector follow this procedure:

#get runtime environmet spec yaml
codefresh get re $RUNTIME_NAME -o yaml > runtime.yaml

Under dockerDaemonScheduler.pvcs.dind block specify reuseVolumeSelector:

  pvcs:
    dind:
      volumeSize: 30Gi
      reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName,pipeline_id'

#apply changes to runtime environment
codefresh patch re -f runtime.yaml

Custom Global Environment Variables

You can add your own environment variables in the runtime environment, so that all pipeline steps have access to the same set of external files. A typical example would be a shared secret that you want to pass everywhere.

To get a list of all available runtimes execute:

codefresh get runtime-environments

Choose the runtime that you want to modify and get its yaml representation:

codefresh get runtime-environments [email protected]/codefresh-runtime -o yaml > runtime.yaml

Under the runtimeScheduler block you can add an additional element with names userEnvVars that follows the same syntax as secret/environment variables

runtime.yaml

runtimeScheduler:
  type: KubernetesPod
  imagePullPolicy: Always
  cluster:
    namespace: codefresh
    clusterProvider:
      accountId: 5c1658d1736122ee1114c842
      selector: docker-desktop
    serviceAccount: codefresh-engine
  envVars:
    LOGGER_LEVEL: debug
    NODE_ENV: kubernetes
    NODE_TLS_REJECT_UNAUTHORIZED: '0'
    DISABLE_WORKSPACE_CACHE: 'true'
    NO_EXT_MONITOR: 'true'
  userEnvVars:
    - name: SPECIAL_LEVEL_KEY
      valueFrom:
        secretKeyRef:
          name: dev-db-secret
          key: username

Update your runtime environment with the patch command:

codefresh patch runtime-environment [email protected]/codefresh-runtime -f runtime.yaml

Debug Timeout Duration

The default timeout for debug mode is 14 minutes, and even if the user is actively working, it is still 14 minutes. To change the duration of the debugger, you will need to update your Runtime Spec for the runtime you would like to change. To change the default duration, you will need to add DEBUGGER_TIMEOUT to the environment variable. The value you pass is a string value that will define the timeout in minutes. For example, you can pass ‘30’, which will be 30 minutes.

Steps to change the duration

Step 1 - Get the Runtime Envionment

To get the list of available runtimes:

codefresh get runtime-environments

Export the runtime from the list you want into a local YAML file:

codefresh get runtime-environments MyHybridRunner/codefresh-runtime -o yaml > runtime.yaml

Step 2 - Edit the Runtime Envionment YAML

Under .runtimeScheduler, add an envVars section, then add DEBUGGER_TIMEOUT under envVars with the value you want.

...
runtimeScheduler:
  envVars:
    DEBUGGER_TIMEOUT: '30'
...

Step 3 - Update the Runtime Envionment

Update your runtime environment with the patch command:

codefresh patch runtime-environment MyHybridRunner/codefresh-runtime -f runtime.yaml

Custom Volume Mounts

You can add your own volume mounts in the runtime environment, so that all pipeline steps have access to the same set of external files. A typical example of this scenario is when you want to make a set of SSL certificates available to all your pipelines. Rather than manually downlading the certificates in each pipeline, you can provide them centrally on the runtime level.

To get a list of all available runtimes execute:

codefresh get runtime-environments

Choose the runtime that you want to modify and get its yaml representation:

codefresh get runtime-environments [email protected]/codefresh-runtime -o yaml > runtime.yaml

Under the dockerDaemonScheduler block you can add two additional elements with names userVolumeMounts and userVolumes (they follow the same syntax as normal volumes and volumeMounts) and define your own global volumes

runtime.yaml

dockerDaemonScheduler:
  userVolumeMounts:
    my-test:
      name: test
      mountPath: /etc/ssl/cert
      readOnly: true
  userVolumes:
    test:
      name: test
      secret:
        secretName: test-secret

Update your runtime environment with the patch command:

codefresh patch runtime-environment [email protected]/codefresh-runtime -f runtime.yaml

Internal Registry Mirror

You can configure your Codefresh Runner to use an internal registry as a mirror for any container images that are mentioned in your pipelines.

First setup an internal registry as described in https://docs.docker.com/registry/recipes/mirror/.

Then locate the codefresh-dind-config config map in the namespace that houses the runner and edit it.

kubectl -n codefresh edit configmap codefresh-dind-config

Change the data field from:

data:
  daemon.json: "{\n  \"hosts\": [ \"unix:///var/run/docker.sock\",\n             \"tcp://0.0.0.0:1300\"],\n
    \ \"storage-driver\": \"overlay2\",\n  \"tlsverify\": true,  \n  \"tls\": true,\n
    \ \"tlscacert\": \"/etc/ssl/cf-client/ca.pem\",\n  \"tlscert\": \"/etc/ssl/cf/server-cert.pem\",\n
    \ \"tlskey\": \"/etc/ssl/cf/server-key.pem\",\n  \"insecure-registries\" : [\"192.168.99.100:5000\"],\n
    \ \"metrics-addr\" : \"0.0.0.0:9323\",\n  \"experimental\" : true\n}\n"

to

data:
  daemon.json: "{\n  \"hosts\": [ \"unix:///var/run/docker.sock\",\n             \"tcp://0.0.0.0:1300\"],\n
    \ \"storage-driver\": \"overlay2\",\n  \"tlsverify\": true,  \n  \"tls\": true,\n
    \ \"tlscacert\": \"/etc/ssl/cf-client/ca.pem\",\n  \"tlscert\": \"/etc/ssl/cf/server-cert.pem\",\n
    \ \"tlskey\": \"/etc/ssl/cf/server-key.pem\",\n  \"insecure-registries\" : [\"192.168.99.100:5000\"],\n
    \ \"registry-mirrors\": [ \"https://<my-docker-mirror-host>\" ], \n
    \ \"metrics-addr\" : \"0.0.0.0:9323\",\n  \"experimental\" : true\n}\n"

This adds the line \ \"registry-mirrors\": [ \"https://<my-docker-mirror-host>\" ], \n which contains a single registry to use as a mirror. Quit and Save by typing :wq.

Now any container image that is used in your pipeline and isn’t fully qualified, will be pulled through the Docker registry that is configured as a mirror.

Installing on AWS

If you install the Codefresh runner on EKS or any other custom cluster (e.g. with kops) in Amazon you need to configure it properly to work with EBS volume in order to gain caching.

Make sure that the node group where dind-volume-provisioner-runner deployment is running has the appropriate permissions to create, attach, detach volumes:

  {
      "Version": "2012-10-17",
      "Statement": [
          {
              "Effect": "Allow",
              "Action": [
                  "ec2:DescribeVolumes"
              ],
              "Resource": [
                  "*"
              ]
          },
          {
              "Effect": "Allow",
              "Action": [
                  "ec2:CreateVolume",
                  "ec2:ModifyVolume",
                  "ec2:CreateTags",
                  "ec2:DescribeTags",
                  "ec2:DetachVolume",
                  "ec2:AttachVolume"
              ],
              "Resource": [
                  "*"
              ]
          }
      ]
  }

Then in order to proceed with Storage Class installation please choose one of the Availability Zones you want to be used for your pipeline builds:

    apiVersion: storage.k8s.io/v1
    kind: StorageClass
    metadata:
      name: runner-ebs
    parameters:
      AvailabilityZone: us-west-2c # <----(Please change it to yours)
      volumeBackend: ebs
    provisioner: codefresh.io/dind-volume-provisioner-runner-<-NAMESPACE-> # <---- rename <-NAMESPACE-> with the runner namespace

Finally you need to change your Codefresh runtime configuration.

The same AZ you selected before should be used in nodeSelector inside Runtime Configuration:

To get a list of all available runtimes execute:

codefresh get runtime-environments

Choose the runtime you have just added and get its yaml representation:

codefresh get runtime-environments [email protected]/codefresh-runtime -o yaml > runtime.yaml

The nodeSelector failure-domain.beta.kubernetes.io/zone: us-west-2c (Please change it to yours) should be added to the dockerDaemonScheduler block. It should be at the same level as clusterProvider or namespace. Also the pvcs block should be modified to use the Storage Class you created above (runner-ebs). Here is the example:

runtime.yaml

version: null
metadata:
  agent: true
  trial:
    endingAt: 1577273400263
    reason: Codefresh hybrid runtime
    started: 1576063800333
  name: [email protected]/codefresh-runtime
  changedBy: ivan-codefresh
  creationTime: '2019/12/11 11:30:00'
runtimeScheduler:
  cluster:
    clusterProvider:
      accountId: 5cb563d0506083262ba1f327
      selector: [email protected]
    namespace: codefresh-runtime
  annotations: {}
dockerDaemonScheduler:
  cluster:
    clusterProvider:
      accountId: 5cb563d0506083262ba1f327
      selector: [email protected]
    namespace: codefresh-runtime
    nodeSelector:
      failure-domain.beta.kubernetes.io/zone: us-west-2c
  annotations: {}
  dockerDaemonParams: null
  pvcs:
    dind:
      volumeSize: 30Gi
      storageClassName: runner-ebs
      reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName'
      reuseVolumeSortOrder: 'pipeline_id,trigger'
  userAccess: true
extends:
  - system/default/hybrid/k8s
description: >-
  Runtime environment configure to cluster: [email protected]
  and namespace: codefresh-runtime
accountId: 5cb563d0506083262ba1f327

Update your runtime environment with the patch command:

codefresh patch runtime-environment [email protected]/codefresh-runtime -f codefresh-runner.yaml

Installing to EKS with Autoscaling

Step 1- EKS Cluster Creation

See below is a content of cluster.yaml file. We define separate node pools for dind, engine and other services(like runner, cluster-autoscaler etc).

Before creating the cluster we have created two separate IAM policies:

• one for our volume-provisioner controller(policy/runner-ebs) that should create and delete volumes
• one for dind pods(policy/dind-ebs) that should be able to attach/detach those volumes to the appropriate nodes using iam attachPolicyARNs options.

policy/dind-ebs:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ec2:DescribeVolumes"
            ],
            "Resource": [
                "*"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "ec2:DetachVolume",
                "ec2:AttachVolume"
            ],
            "Resource": [
                "*"
            ]
        }
    ]
}

policy/runner-ebs:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ec2:AttachVolume",
                "ec2:CreateSnapshot",
                "ec2:CreateTags",
                "ec2:CreateVolume",
                "ec2:DeleteSnapshot",
                "ec2:DeleteTags",
                "ec2:DeleteVolume",
                "ec2:DescribeInstances",
                "ec2:DescribeSnapshots",
                "ec2:DescribeTags",
                "ec2:DescribeVolumes",
                "ec2:DetachVolume"
            ],
            "Resource": "*"
        }
    ]
}

my-eks-cluster.yaml

apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig
metadata:
  name: my-eks
  region: us-west-2
  version: "1.15"

nodeGroups:
  - name: dind
    instanceType: m5.2xlarge
    desiredCapacity: 1
    iam:
      attachPolicyARNs:
        - arn:aws:iam::aws:policy/AmazonEKSWorkerNodePolicy
        - arn:aws:iam::aws:policy/AmazonEKS_CNI_Policy
        - arn:aws:iam::aws:policy/ElasticLoadBalancingFullAccess
        - arn:aws:iam::XXXXXXXXXXXX:policy/dind-ebs
      withAddonPolicies:
        autoScaler: true
    ssh: # import public key from file
      publicKeyPath: ~/.ssh/id_rsa.pub
    minSize: 1
    maxSize: 50
    volumeSize: 50
    volumeType: gp2
    ebsOptimized: true
    availabilityZones: ["us-west-2a"]
    kubeletExtraConfig:
        enableControllerAttachDetach: false
    labels:
      node-type: dind
    taints:
      codefresh.io: "dinds:NoSchedule"

  - name: engine
    instanceType: m5.large
    desiredCapacity: 1
    iam:
      withAddonPolicies:
        autoScaler: true
    minSize: 1
    maxSize: 10
    volumeSize: 50
    volumeType: gp2
    availabilityZones: ["us-west-2a"]
    labels:
      node-type: engine
    taints:
      codefresh.io: "engine:NoSchedule"

  - name: addons
    instanceType: m5.2xlarge
    desiredCapacity: 1
    ssh: # import public key from file
      publicKeyPath: ~/.ssh/id_rsa.pub
    minSize: 1
    maxSize: 10
    volumeSize: 50
    volumeType: gp2
    ebsOptimized: true
    availabilityZones: ["us-west-2a"]
    labels:
      node-type: addons
    iam:
      attachPolicyARNs:
        - arn:aws:iam::aws:policy/AmazonEKSWorkerNodePolicy
        - arn:aws:iam::aws:policy/AmazonEKS_CNI_Policy
        - arn:aws:iam::aws:policy/ElasticLoadBalancingFullAccess
        - arn:aws:iam::XXXXXXXXXXXX:policy/runner-ebs
      withAddonPolicies:
        autoScaler: true
availabilityZones: ["us-west-2a", "us-west-2b", "us-west-2c"]

Execute:

eksctl create cluster -f my-eks-cluster.yaml

The config above will leverage Amazon Linux 2 as the default operating system for the nodes in the nodegroup. To leverage Bottlerocket-based nodes, specify the AMI Family using amiFamily: Bottlerocket and add the following additional IAM Policies: arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly and arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore.

Bottlerocket is an open source Linux based Operating System specifically built to run containers. It focuses on security, simplicity and easy updates via transactions. Find more information in the official repository.

Step 2 - Autoscaler

Once the cluster is up and running we need to install the cluster autoscaler:

We used iam AddonPolicies "autoScaler: true" in the cluster.yaml file so there is no need to create a separate IAM policy or add Auto Scaling group tags, everything is done automatically.

Deploy the Cluster Autoscaler:

kubectl apply -f https://raw.githubusercontent.com/kubernetes/autoscaler/master/cluster-autoscaler/cloudprovider/aws/examples/cluster-autoscaler-autodiscover.yaml

Add the cluster-autoscaler.kubernetes.io/safe-to-evict annotation

kubectl -n kube-system annotate deployment.apps/cluster-autoscaler cluster-autoscaler.kubernetes.io/safe-to-evict="false"

Edit the cluster-autoscaler container command to replace <YOUR CLUSTER NAME> with my-eks(name of the cluster from cluster.yaml file), and add the following options: --balance-similar-node-groups and --skip-nodes-with-system-pods=false

kubectl -n kube-system edit deployment.apps/cluster-autoscaler

spec:
      containers:
      - command:
        - ./cluster-autoscaler
        - --v=4
        - --stderrthreshold=info
        - --cloud-provider=aws
        - --skip-nodes-with-local-storage=false
        - --expander=least-waste
        - --node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled,k8s.io/cluster-autoscaler/my-eks
        - --balance-similar-node-groups
        - --skip-nodes-with-system-pods=false

We created our EKS cluster with 1.15 version so the appropriate cluster autoscaler version from https://github.com/kubernetes/autoscaler/releases is 1.15.6

kubectl -n kube-system set image deployment.apps/cluster-autoscaler cluster-autoscaler=us.gcr.io/k8s-artifacts-prod/autoscaling/cluster-autoscaler:v1.15.6

Check your own version to make sure that the autoscaler version is appropriate.

Step 3 - Optional: We also advise to configure overprovisioning with Cluster Autoscaler

See details at the FAQ.

Step 4 - Adding an EKS cluster as a runner to the Codefresh platform with EBS support

Make sure that you are targeting the correct cluster

$ kubectl config current-context 
my-aws-runner

Install the runner passing additional options:

codefresh runner init \
--name my-aws-runner \
--kube-node-selector=failure-domain.beta.kubernetes.io/zone=us-west-2a \
--kube-namespace cf --kube-context-name my-aws-runner \
--set-value Storage.VolumeProvisioner.NodeSelector=node-type=addons \
--set-value=Storage.Backend=ebs \
--set-value=Storage.AvailabilityZone=us-west-2a

• You should specify the zone in which you want your volumes to be created, example: --set-value=Storage.AvailabilityZone=us-west-2a
• (Optional) - if you want to assign the volume-provisioner to a specific node, for example a specific node group what has an IAM role which allows to create EBS volumes, example: --set-value Storage.VolumeProvisioner.NodeSelector=node-type=addons

If you want to use encrypted EBS volumes (they are unencrypted by default) - add the custom value --set-value=Storage.Encrypted=true If you already have a key - add its ARN via --set-value=Storage.KmsKeyId=<key id> value, otherwise a key is generated by AWS. Here is the full command:

codefresh runner init \
--name my-aws-runner \
--kube-node-selector=failure-domain.beta.kubernetes.io/zone=us-west-2a \
--kube-namespace cf --kube-context-name my-aws-runner \
--set-value Storage.VolumeProvisioner.NodeSelector=node-type=addons \
--set-value=Storage.Backend=ebs \
--set-value=Storage.AvailabilityZone=us-west-2a\
--set-value=Storage.Encrypted=[false|true] \
--set-value=Storage.KmsKeyId=<key id>

For an explanation of all other options see the global parameter table.

At this point the quick start wizard will start the installation.

Once that is done we need to modify the runtime environment of my-aws-runner to specify the necessary toleration, nodeSelector and disk size:

codefresh get re --limit=100 my-aws-runner/cf -o yaml > my-runtime.yml

version: null
metadata:
  agent: true
  trial:
    endingAt: 1593596844167
    reason: Codefresh hybrid runtime
    started: 1592387244207
  name: my-aws-runner/cf
  changedBy: ivan-codefresh
  creationTime: '2020/06/17 09:47:24'
runtimeScheduler:
  cluster:
    clusterProvider:
      accountId: 5cb563d0506083262ba1f327
      selector: my-aws-runner
    namespace: cf
  annotations: {}
dockerDaemonScheduler:
  cluster:
    clusterProvider:
      accountId: 5cb563d0506083262ba1f327
      selector: my-aws-runner
    namespace: cf
  annotations: {}
  defaultDindResources:
    requests: ''
  pvcs:
    dind:
      storageClassName: dind-local-volumes-runner-cf
  userAccess: true
extends:
  - system/default/hybrid/k8s_low_limits
description: 'Runtime environment configure to cluster: my-aws-runner and namespace: cf'
accountId: 5cb563d0506083262ba1f327

Modify the file my-runtime.yml as shown below:

version: null
metadata:
  agent: true
  trial:
    endingAt: 1593596844167
    reason: Codefresh hybrid runtime
    started: 1592387244207
  name: my-aws-runner/cf
  changedBy: ivan-codefresh
  creationTime: '2020/06/17 09:47:24'
runtimeScheduler:
  cluster:
    clusterProvider:
      accountId: 5cb563d0506083262ba1f327
      selector: my-aws-runner
    namespace: cf
    nodeSelector:
      node-type: engine
  tolerations:
  - effect: NoSchedule
    key: codefresh.io
    operator: Equal
    value: engine
  annotations: {}
dockerDaemonScheduler:
  cluster:
    clusterProvider:
      accountId: 5cb563d0506083262ba1f327
      selector: my-aws-runner
    namespace: cf
    nodeSelector:
      node-type: dind
  annotations: {}
  defaultDindResources:
    requests: ''
  tolerations:
  - effect: NoSchedule
    key: codefresh.io
    operator: Equal
    value: dinds
  pvcs:
    dind:
      volumeSize: 30Gi
      reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName'
      storageClassName: dind-local-volumes-runner-cf
  userAccess: true
extends:
  - system/default/hybrid/k8s_low_limits
description: 'Runtime environment configure to cluster: my-aws-runner and namespace: cf'
accountId: 5cb563d0506083262ba1f327

Finally apply changes.

codefresh patch re my-aws-runner/cf -f my-runtime.yml

That’s all. Now you can go to UI and try to run a pipeline on RE my-aws-runner/cf

Injecting AWS arn roles into the cluster

Step 1 - Make sure the OIDC provider is connected to the cluster

See:

• https://docs.aws.amazon.com/eks/latest/userguide/enable-iam-roles-for-service-accounts.html
• https://aws.amazon.com/blogs/opensource/introducing-fine-grained-iam-roles-service-accounts/

Step 2 - Create IAM role and policy as explained in https://docs.aws.amazon.com/eks/latest/userguide/create-service-account-iam-policy-and-role.html

Here, in addition to the policy explained, you need a Trust Relationship established between this role and the OIDC entity.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Federated": "arn:aws:iam::${ACCOUNT_ID}:oidc-provider/${OIDC_PROVIDER}"
      },
      "Action": "sts:AssumeRoleWithWebIdentity",
      "Condition": {
        "StringEquals": {
          "${OIDC_PROVIDER}:sub": "system:serviceaccount:${CODEFRESH_NAMESPACE}:codefresh-engine"
        }
      }
    }
  ]
}

Step 3 - Annotate the codefresh-engine Kubernetes Service Account in the namespace where the Codefresh Runner is installed with the proper IAM role.

kubectl annotate -n ${CODEFRESH_NAMESPACE} sa codefresh-engine eks.amazonaws.com/role-arn=${ROLE_ARN}

Once the annotation is added, you should see it when you describe the Service Account.

kubectl describe -n ${CODEFRESH_NAMESPACE} sa codefresh-engine

Name:                codefresh-engine
Namespace:           codefresh
Labels:              app=app-proxy
                     version=1.6.8
Annotations:         eks.amazonaws.com/role-arn: arn:aws:iam::123456789012:role/Codefresh
Image pull secrets:  <none>
Mountable secrets:   codefresh-engine-token-msj8d
Tokens:              codefresh-engine-token-msj8d
Events:              <none>

Step 4 - Using the AWS assumed role identity

After annotating the Service Account, run a pipeline to test the AWS resource access:

RunAwsCli:
      title : Communication with AWS
      image : mesosphere/aws-cli
      stage: "build"
      commands :
         - apk update
         - apk add jq
         - env
         - cat /codefresh/volume/sensitive/.kube/web_id_token
         - aws sts assume-role-with-web-identity --role-arn $AWS_ROLE_ARN --role-session-name mh9test --web-identity-token file://$AWS_WEB_IDENTITY_TOKEN_FILE --duration-seconds 1000 > /tmp/irp-cred.txt
         - export AWS_ACCESS_KEY_ID="$(cat /tmp/irp-cred.txt | jq -r ".Credentials.AccessKeyId")"
         - export AWS_SECRET_ACCESS_KEY="$(cat /tmp/irp-cred.txt | jq -r ".Credentials.SecretAccessKey")"
         - export AWS_SESSION_TOKEN="$(cat /tmp/irp-cred.txt | jq -r ".Credentials.SessionToken")"
         - rm /tmp/irp-cred.txt
         - aws s3api get-object --bucket jags-cf-eks-pod-secrets-bucket --key  eks-pod2019-12-10-21-18-32-560931EEF8561BC4 getObjectNotWorks.txt

Installing behind a proxy

If you want to deploy the Codefresh runner on a Kubernetes cluster that doesn’t have direct access to g.codefresh.io, and has to go trough a proxy server to access g.codefresh.io, you will need to follow these additional steps:

Step 1 - Follow the installation instructions of the previous section

Step 2 - Run kubectl edit deployment runner -n codefresh-runtime and add the proxy variables like this

spec:
  containers:
  - env:
    - name: HTTP_PROXY
      value: http://<ip of proxy server>:port
    - name: HTTPS_PROXY
      value: http://<ip of proxy server>:port
    - name: http_proxy
      value: http://<ip of proxy server>:port
    - name: https_proxy
      value: http://<ip of proxy server>:port
    - name: no_proxy
      value: localhost,127.0.0.1,<local_ip_of_machine>
    - name: NO_PROXY
      value: localhost,127.0.0.1,<local_ip_of_machine>

Step 3 - Add the following variables to your runtime.yaml, both under the runtimeScheduler: and under dockerDaemonScheduler: blocks inside the envVars: section

HTTP_PROXY: http://<ip of proxy server>:port
http_proxy: http://<ip of proxy server>:port
HTTPS_PROXY: http://<ip of proxy server>:port
https_proxy: http://<ip of proxy server>:port
No_proxy: localhost, 127.0.0.1, <local_ip_of_machine>
NO_PROXY: localhost, 127.0.0.1, <local_ip_of_machine>

Step 4 - Add .firebaseio.com to the allowed-sites of the proxy server

Step 5 - Exec into the dind pod and run ifconfig

If the MTU value for docker0 is higher than the MTU value of eth0 (sometimes the docker0 MTU is 1500, while eth0 MTU is 1440) - you need to change this, the docker0 MTU should be lower than eth0 MTU

To fix this, edit the configmap in the codefresh-runtime namespace:

kubectl edit cm codefresh-dind-config -ncodefresh-runtime

And add this after one of the commas: \"mtu\":1440,

Installing on Rancher RKE 2.X

Step 1 - Configure the kubelet to work with the runner’s StorageClass

The runner’s default StorageClass creates the persistent cache volume from local storage on each node. We need to edit the cluster config to allow this.

In the Rancher UI, drill into the target cluster and then click the Edit Cluster button at the top-right.

On the edit cluster page, scroll down to the Cluster Options section and click its Edit as YAML button

Edit the YAML to include an extra mount in the kubelet service:

rancher_kubernetes_engine_config:
  ...  
  services:
    ...  
    kubelet:
      extra_binds:
        - '/var/lib/codefresh:/var/lib/codefresh:rshared'

Step 2 - Make sure your kubeconfig user is a ClusterAdmin

The user in your kubeconfig must be a cluster admin in order to install the runner. If you plan to have your pipelines connect to this cluster as a cluster admin, then you can go ahead and create a Codefresh user for this purpose in the Rancher UI with a non-expiring kubeconfig token. This is the easiest way to do the installation.

However, if you want your pipelines to connect to this cluster with less privileges, then you can use your personal user account with Cluster Admin privileges for the installation, and then we’ll create a Codefresh account with lesser privileges later (in Step 5). In that case, you can now move on to Step 3.

Follow these steps to create a Codefresh user with Cluster Admin rights, from the Rancher UI:

• Click Security at the top, and then choose Users

• Click the Add User button, and under Global Permissions check the box for Restricted Administrstor
• Log out of the Rancher UI, and then log back in as the new user
• Click your user icon at the top-right, and then choose API & Keys
• Click the Add Key button and create a kubeconfig token with Expires set to Never
• Copy the Bearer Token field (combines Access Key and Secret Key)
• Edit your kubeconfig and put the Bearer Token you copied in the token field of your user

Step 3 - Install the Runner

If you’ve created your kubeconfig from the Rancher UI, then it will contain an API endpoint that is not reachable internally, from within the cluster. To work around this, we need to tell the runner to instead use Kubernetes’ generic internal API endpoint. Also, if you didn’t create a Codefresh user in step 2 and your kubeconfig contains your personal user account, then you should also add the --skip-cluster-integration option.

Install the runner with a Codefresh user (ClusterAdmin, non-expiring token):

codefresh runner init \
  --set-value KubernetesHost=https://kubernetes.default.svc.cluster.local

Or install the runner with your personal user account:

codefresh runner init \
  --set-value KubernetesHost=https://kubernetes.default.svc.cluster.local \
  --skip-cluster-integration

The wizard will then ask you some basic questions.

Step 4 - Update the runner’s Docker MTU

By default, RKE nodes use the Canal CNI, which combines elements of Flannel and Calico, and uses VXLAN encapsulation. This VXLAN encapsulation has a 50-byte overhead, thus reducing the MTU of its virtual interfaces from the standard 1500 to 1450. For example, when running ifconfig on an RKE 2.5.5 node, you might see several interfaces like this. Note the MTU:1450.

cali0f8ac592086 Link encap:Ethernet  HWaddr ee:ee:ee:ee:ee:ee
          inet6 addr: fe80::ecee:eeff:feee:eeee/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1450  Metric:1
          RX packets:11106 errors:0 dropped:0 overruns:0 frame:0
          TX packets:10908 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:922373 (922.3 KB)  TX bytes:9825590 (9.8 MB)

We must reduce the Docker MTU used by the runner’s Docker in Docker (dind) pods to fit within this lower MTU. This is stored in a configmap in the namespace where the runner is installed. Assuming that you installed the runner into the codefresh namespace, you would edit the configmap like this:

kubectl edit cm codefresh-dind-config -n codefresh

In the editor, update the daemon.json field - add ,\"mtu\":1440 just before the last curley brace.

Step 5 - Create the Cluster Integration

If you created a user in Step 2 and used it to install the runner in Step 3, then you can skip this step - your installation is complete!

However, if you installed the runner with the --skip-cluster-integration option then you should follow the documentaion to Add a Rancher Cluster to your Kubernetes Integrations.

Once complete, you can go to the Codefresh UI and run a pipeline on the new runtime, including steps that deploy to the Kubernetes Integration.

Troubleshooting TLS Errors

Depending on your Rancher configuration, you may need to allow insecure HTTPS/TLS connections. You can do this by adding an environment variable to the runner deployment.

Assuming that you installed the runner into the codefresh namespace, you would edit the runner deployment like this:

kubectl edit deploy runner -n codefresh

In the editor, add this environment variable under spec.containers.env[]:

- name: NODE_TLS_REJECT_UNAUTHORIZED
  value: "0"

Installing on Google Kubernetes Engine

If you are installing Codefresh runner on the Kubernetes cluster on GKE

• make sure your user has Kubernetes Engine Cluster Admin role in google console and
• bind your user with cluster-admin Kubernetes cluster role.

kubectl create clusterrolebinding NAME --clusterrole cluster-admin --user <YOUR_USER>

Docker cache support for GKE

Local SSD

If you want to use LocalSSD in GKE:

Prerequisites: GKE cluster with local SSD

Install Runner using GKE Local SSD:

codefresh runner init [options] --set-value=Storage.LocalVolumeParentDir=/mnt/disks/ssd0/codefresh-volumes \
                            --build-node-selector=cloud.google.com/gke-local-ssd=true

values-example.yaml

...
### Storage parameters example for gke-local-ssd
 Storage:
   Backend: local
   LocalVolumeParentDir: /mnt/disks/ssd0/codefresh-volumes
 NodeSelector: cloud.google.com/gke-local-ssd=true
...
 Runtime:
   NodeSelector: # dind and engine pods node-selector (--build-node-selector)
     cloud.google.com/gke-local-ssd: "true"
...

To configure existing Runner with Local SSDs follow this article:

How-to: Configuring an existing Runtime Environment with Local SSDs (GKE only)

GCE Disks

If you want to use GCE Disks:

Prerequisites: volume provisioner (dind-volume-provisioner) should have permissions to create/delete/get GCE disks

There are 3 options to provide cloud credentials on GCE:

• run dind-volume-provisioner-runner pod on a node with IAM role which is allowed to create/delete/get GCE disks
• create Google Service Account with ComputeEngine.StorageAdmin role, download its key in JSON format and pass it to codefresh runner init with --set-file=Storage.GooogleServiceAccount=/path/to/google-service-account.json
• use Google Workload Identity to assign IAM role to volume-provisioner-runner service account

Notice that builds will be running in a single availability zone, so you must specify AvailabilityZone parameters.

Runner installation with GCE Disks (Google SA JSON key)

Using the Wizard:

codefresh runner init [options] \
  --set-value=Storage.Backend=gcedisk \
  --set-value=Storage.AvailabilityZone=us-central1-c \
  --kube-node-selector=topology.kubernetes.io/zone=us-central1-c \
  --build-node-selector=topology.kubernetes.io/zone=us-central1-c \
  --set-file=Storage.GoogleServiceAccount=/path/to/google-service-account.json

Using the values file: values-example.yaml

...
### Storage parameter example for GCE disks
 Storage:
   Backend: gcedisk
   AvailabilityZone: us-central1-c
   GoogleServiceAccount: > #serviceAccount.json content
     {
      "type": "service_account",
      "project_id": "...",
      "private_key_id": "...",
      "private_key": "...",
      "client_email": "...",
      "client_id": "...",
      "auth_uri": "...",
      "token_uri": "...",
      "auth_provider_x509_cert_url": "...",
      "client_x509_cert_url": "..."
      }
 NodeSelector: topology.kubernetes.io/zone=us-central1-c
...
 Runtime:
   NodeSelector: # dind and engine pods node-selector (--build-node-selector)
     topology.kubernetes.io/zone: us-central1-c
...

codefresh runner init [options] --values values-example.yaml

Runner installation with GCE Disks (Workload Identity with IAM role)

values-example.yaml

...
### Storage parameter example for GCE disks
 Storage:
   Backend: gcedisk
   AvailabilityZone: us-central1-c
   VolumeProvisioner:
     ServiceAccount:
       Annotations: #annotation to the volume-provisioner service account, using the email address of the Google service account
         iam.gke.io/gcp-service-account: <GSA_NAME>@<PROJECT_ID>.iam.gserviceaccount.com
 NodeSelector: topology.kubernetes.io/zone=us-central1-c
...
 Runtime:
   NodeSelector: # dind and engine pods node-selector (--build-node-selector)
     topology.kubernetes.io/zone: us-central1-c
...

Create the binding between Kubernetes service account and Google service account:

export K8S_NAMESPACE=codefresh
export KSA_NAME=volume-provisioner-runner
export GSA_NAME=<google_sa_name>
export PROJECT_ID=<google_project_name>

gcloud iam service-accounts add-iam-policy-binding \
  --role roles/iam.workloadIdentityUser \
  --member "serviceAccount:${PROJECT_ID}.svc.id.goog[${K8S_NAMESPACE}/${KSA_NAME}]" \
  ${GSA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com

To configure existing Runner with GCE Disks follow this article:

How-to: Configuring an existing Runtime Environment with GCE disks

Using multiple Availability Zones

Currently, to support effective caching with GCE disks, the builds/pods need to be scheduled in a single AZ (this is more related to a GCP limitation than a Codefresh runner issue).

If you have Kubernetes nodes running in multiple Availability Zones and wish to use the Codefresh runner we suggest the following:

Option A - Provision a new Kubernetes cluster: a cluster that runs in a single AZ only. - The cluster should be dedicated for usage with the Codefresh runner. This is the preferred solution and avoids extra complexity.

Option B - Install Codefresh runner in your multi-zone cluster, and let it run in the default Node Pool: - in this case, you must specify --build-node-selector=<node-az-label> (e.g.: --build-node-selector=failure-domain.beta.kubernetes.io/zone=us-central1-a) or simply modify the Runtime environment as below:

codefresh get runtime-environments gke_us-east4_my-gke-cluster/codefresh -o yaml > re.yaml

Edit the yaml:

version: 2metadata:
  agent: true
  trial:
    endingAt: 34534534
    reason: Codefresh hybrid runtime
    started: 23434
  name: gke_us-east4_my-gke-cluster/codefresh
  changedBy: kostis
  creationTime: '2020/04/01 21:04:11'
runtimeScheduler:
  cluster:
    clusterProvider:
      accountId: 34543545456
      selector: gke_us-east4_my-gke-cluster
    namespace: codefresh
    nodeSelector:
      failure-domain.beta.kubernetes.io/zone: us-east4-a
dockerDaemonScheduler:
  cluster:
    clusterProvider:
      accountId: 5cdd8937242f167387e5aa56
      selector: gke_us-east4_my-gke-cluster
    namespace: codefresh
    nodeSelector:
      failure-domain.beta.kubernetes.io/zone: us-east4-a
  dockerDaemonParams: null
  pvcs:
    dind:
      storageClassName: dind-gcedisk-us-east4-a-venona-codefresh
      reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName,pipeline_id'
      volumeSize: 30Gi
  userAccess: true
extends:
  - system/default/hybrid/k8s
description: >-
  Runtime environment configure to cluster:
  gke_us-east4_my-gke-cluster and namespace: codefresh
accountId: 45645k694353459

Apply changes with:

codefresh patch runtime-environments -f re.yaml

Option C - Like option B, but with a dedicated Node Pool

Option D - Have 2 separate Codefresh runner Runtimes, one for zone A, and the other for zone B, and so on: this technically works, but it will require you to manually set the RE to use for the pipelines that won’t use the default Codefresh runner RE. To distribute the pipeline’s builds across the Codefresh runner REs.

For example, let’s say Venona-zoneA is the default RE, then, that means that for the pipelines that you want to run in Venona-zoneB, then you’ll need to modify their RE settings, and explicitly set Venona-zoneB as the one to use.

Regarding Regional Persistent Disks, their support is not currently implemented in the Codefresh runner.

Runtime Cleaners

Key points

• Codefresh pipelines require disk space for:
  • Pipeline Shared Volume (/codefresh/volume, implemented as docker volume)
  • Docker containers - running and stopped
  • Docker images and cached layers
• To improve performance, volume-provisioner is able to provision previously used disk with docker images and pipeline volume from previously running builds. It improves performance by using docker cache and decreasing I/O rate.
• Least recently docker images and volumes should be cleaned to avoid out-of-space errors.
• There are several places where pipeline volume cleanup is required, so there are several kinds of cleaner.

Cleaners

• IN-DIND cleaner - deletes extra docker containers, volumes, images in dind pod
• External volumes cleaner - deletes unused external PVs (EBS, GCE/Azure disks)
• Local volumes cleaner - deletes local volumes in case node disk space is close to the threshold

IN-DIND cleaner

Purpose: Removes unneeded docker containers, images, volumes inside kubernetes volume mounted to the dind pod

Where it runs: Running inside each dind pod as script

Triggered by: SIGTERM and also during the run when disk usage (cleaner-agent ) > 90% (configurable)

Configured by: Environment Variables which can be set in Runtime Environment configuration

Configuration/Logic: README.md

Override dockerDaemonScheduler.envVars on Runtime Environment if necessary (the following are defaults):

dockerDaemonScheduler:
  envVars:
    CLEAN_DOCKER: 'true'
    CLEAN_PERIOD_BUILDS: '5'
    IMAGE_RETAIN_PERIOD: '14400'
    VOLUMES_RETAIN_PERIOD: '14400'

External volumes cleaner

Purpose: Removes unused kubernetes volumes and related backend volumes

Where it runs: On Runtime Cluster as CronJob (kubectl get cronjobs -n codefresh -l app=dind-volume-cleanup). Installed in case the Runner uses non-local volumes (Storage.Backend != local)

Triggered by: CronJob every 10min (configurable), part of runtime-cluster-monitor and runner deployment

Configuration:

Set codefresh.io/volume-retention annotation on Runtime Environment:

dockerDaemonScheduler:
  pvcs:
    dind:
      storageClassName: dind-ebs-volumes-runner-codefresh
      reuseVolumeSelector: 'codefresh-app,io.codefresh.accountName,pipeline_id'
      volumeSize: 32Gi
      annotations:
        codefresh.io/volume-retention: 7d

Override environment variables for dind-volume-cleanup cronjob if necessary:

• RETENTION_DAYS (defaults to 4)
• MOUNT_MIN (defaults to 3)
• PROVISIONED_BY (defaults to codefresh.io/dind-volume-provisioner)

About optional -m argument:

• dind-volume-cleanup to clean volumes that were last used more than RETENTION_DAYS ago
• dind-volume-cleanup-m to clean volumes that were used more than a day ago, but mounted less than MOUNT_MIN times

Local volumes cleaner

Purpose: Deletes local volumes in case node disk space is close to the threshold

Where it runs: On each node on runtime cluster as DaemonSet dind-lv-monitor. Installed in case the Runner use local volumes (Storage.Backend == local)

Triggered by: Starts clean if disk space usage or inodes usage is more than thresholds (configurable)

Configuration:

Override environment variables for dind-lv-monitor daemonset if necessary:

• VOLUME_PARENT_DIR - default /var/lib/codefresh/dind-volumes
• KB_USAGE_THRESHOLD - default 80 (percentage)
• INODE_USAGE_THRESHOLD - default 80

ARM Builds

With hybrid runner it’s possibe to run native ARM64v8 builds.

Note: Running both amd64 and arm64 images within the same pipeline - it is not possible. We do not support multi-architecture builds. One runtime configuration - one architecture. Considering one pipeline can map only to one runtime, it is possible to run either amd64 or arm64, but not both within a one pipeline

The following scenario is an example of how to set up ARM Runner on existing EKS cluster:

Step 1 - Preparing nodes

Create new ARM nodegroup:

eksctl utils update-coredns --cluster <cluster-name>
eksctl utils update-kube-proxy --cluster <cluster-name> --approve
eksctl utils update-aws-node --cluster <cluster-name> --approve

eksctl create nodegroup \
--cluster <cluster-name> \
--region <region> \
--name <arm-ng> \
--node-type <a1.2xlarge> \
--nodes <3>\
--nodes-min <2>\
--nodes-max <4>\
--managed

Check nodes status:

kubectl get nodes -l kubernetes.io/arch=arm64

Also it’s recommeded to label and taint the required ARM nodes:

kubectl taint nodes <node> arch=aarch64:NoSchedule
kubectl label nodes <node> arch=arm

Step 2 - Runner installation

Use values.yaml to inject tolerations, kube-node-selector, build-node-selector into the Runtime Environment spec.

values-arm.yaml

...
Namespace: codefresh

### NodeSelector --kube-node-selector: controls runner and dind-volume-provisioner pods
NodeSelector: arch=arm

### Tolerations --tolerations: controls runner, dind-volume-provisioner and dind-lv-monitor
Tolerations: 
- key: arch
  operator: Equal
  value: aarch64
  effect: NoSchedule
...
########################################################
###                Codefresh Runtime                 ###
###                                                  ###
###         configure engine and dind pods           ###
########################################################
Runtime:
### NodeSelector --build-node-selector: controls engine and dind pods
  NodeSelector:
    arch: arm
### Tolerations for engine and dind pods
  tolerations: 
  - key: arch
    operator: Equal
    value: aarch64
    effect: NoSchedule  
...    

Install the Runner with:

codefresh runner init --values values-arm.yaml --exec-demo-pipeline false --skip-cluster-integration true

Step 3 - Post-installation fixes

Change engine image version in Runtime Environment specification:

# get the latest engine ARM64 tag
curl -X GET "https://quay.io/api/v1/repository/codefresh/engine/tag/?limit=100" --silent | jq -r '.tags[].name' | grep "^1.*arm64$"
1.136.1-arm64

# get runtime spec
codefresh get re $RUNTIME_NAME -o yaml > runtime.yaml

under runtimeScheduler.image change image tag:

runtimeScheduler:
  image: 'quay.io/codefresh/engine:1.136.1-arm64'

# patch runtime spec
codefresh patch re -f runtime.yaml

For local storage patch dind-lv-monitor-runner DaemonSet and add nodeSelector:

kubectl edit ds dind-lv-monitor-runner

    spec:
      nodeSelector:
        arch: arm

Step 4 - Run Demo pipeline

Run a modified version of the CF_Runner_Demo pipeline:

version: '1.0'
stages:
  - test
steps:
  test:
    stage: test
    title: test
    image: 'arm64v8/alpine'
    commands:
      - echo hello Codefresh Runner!

Troubleshooting

• Problem: You receive an error regarding the provided token or CLI context used for this installation might not have enough permissions.

• Solution: Make sure when you generate the token used to authenticate with the CLI, you generate it with all scopes.

• Problem: the cluster does not meet the minimum requirements (Kubernetes 1.10+, required memory/CPU on at least one single node, service account to create ClusterRoleBinding).

• Solution: install on a different cluster

• Problem: Node process crashes during the installation or installation gets stuck on “Executing pipeline.“

• Solution: Try to run the codefresh runner init command from a different terminal window or restart your machine and try again.

• Problem: Builds will not run.
• Solution: Check:
  • That there is only one Codefresh Runner per Kubernetes namespace
  • That you selected the correct runtime for the pipeline
  • The CPU/Memory specification in the pipeline settings
• Problem: After installing the runner, your builds are stuck in the “pending” state and if you look at the runner pod logs you get the following message: HTTP request to Codefresh API rejected. Status-Code: 401
• Solution: You probably installed the runner in a namespace where there was previously another Codefresh runner. Delete the namespace and install the runner on a new namespace.

What to read next

• Codefresh installation options
• Access Control
• Codefresh API