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 premise security requirements. These pipelines run on your infrastructure, even behind the firewall, and keep code on your Kubernetes cluster.

Skip to quick installation →

Notice that a runner installation is needed for each cluster that will run Codefresh pipelines. A runner is not needed in clusters that are used for deployment. It is possible to deploy applications on different clusters other than the ones the runner is running on.

The installation process takes care of all the components of the runner as well as the other resources (config-maps, secrets, volumes) needed by them.

Prerequisites

In order to use the Codefresh runner you need the following:

1. A Kubernetes cluster with outgoing Internet access (with version later than 1.10). Each node should have 50GB disk size.
2. A Codefresh account with the Hybrid feature enabled.
3. A Codefresh CLI token that will be used to authenticate to your Codefresh account.

Installation can happen from any workstation or laptop that has access (i.e. via kubectl) to the Kubernetes cluster that will run 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.

Notice that access to the Codefresh CLI is only needed once during the Runner installation. After that, the Runner with 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>

The wizard will ask you some basic questions and proceed with the installation.

The wizard will also create and run 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 will also see which API token will be used by the runner (if you don’t provide one by yourself). The token that is printed is used by the runner to talk to the Codefresh platform and it carries permissions that allows the runner to run pipelines. If you note down the token, it can later be used if the deployment is deleted to restore the runner’s permissions without creating a new runner installation.

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 that will be used in advance with:

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

Here are all the possible installation options

Using the Codefresh Runner

Once installed the Runner is fully automated. It polls on its own the Codefresh SAAS (by default every ten seconds) and creates automatically 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 at your Account Settings, in the respective tab.

If you have multiple environments available, you can change the default one (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 an option set-default that will automatically set as default 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 one, 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 resource when pipelines are running (they are created and destroyed automatically for each pipeline).

Node size and count will entirely depend 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
• runner - this pod needs outgoing/egress access to g.codefresh.io
• engine - this pod needs outgoing/egress access to g.codefresh.io

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.

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 --agent-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 --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 --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

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

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 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

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.

Step 3 - Create a new namespace where the runner will be instlled (e.g. codefresh-runtime) and annotate the default Kubernetes Service Account on the newly created namespace with the proper IAM role

Step 4 - Install the Codefresh runner using the instructions of the previous section

Step 5 - Using the AWS assumed role identity

After the Codefresh runner is installed 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

Legacy installation of the Runner using the Venona installer

This method of installing the runner is deprecated. If you are starting out with the Codefresh runner, please use the method in the previous section using the Codefresh CLI

The Codefresh runner installer is available at https://github.com/codefresh-io/venona. You can use Venona to create, upgrade and remove runner installations on any internal Kubernetes cluster.

Command line installation

First setup Codefresh CLI access first, in the machine where you want to install the runner from.

You can see if the CLI works correctly by running any command such as:

codefresh get pipelines

This should list the pipelines of your Codefresh account. Notice that access to the Codefresh CLI is only needed once during the Runner installation. After that, the Runner with 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.

Download the Runner installer (called Venona) from the releases page or by using homebrew if you are on a Mac:

brew tap codefresh-io/venona
brew install venona

Create a namespace in your cluster where you want the Codefresh runner to be installed:

kubectl create namespace codefresh-runtime

And finally run the installer passing as argument the namespace you just created:

venona install --kube-namespace codefresh-runtime

After a while you should see a message that the installation process has finished with success. You can run venona --help to get additional installation options.

As an example, you can define your own location for kubeconfig and/or CLI config:

venona install --kube-namespace my-codefresh-runtime --verbose --kube-config-path c:/users/kostis/.kube/config --cfconfig c:/Users/Kostis/.cfconfig

To check the installation result type venona status --verbose and you will get a list of all installations.

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 venona -ncodefresh-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 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

If you want to use LocalSSD in GKE:

Prerequisite: GKE cluster with local SSD

Install runner using GKE Local SSD:

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

If you want to use GCE Disks:

Prerequisite: volume provisioner (dind-volume-provisioner) should have permissions to create/delete/get of Google disks

There are 3 options to provide cloud credentials on GCE:

• run venona dind-volume-provisioniner on node with iam role which is allowed to create/delete/get of Google disks
• create Google Service Account with ComputeEngine.StorageAdmin, download its key and pass it to venona installed with --set-file=Storage.GooogleServiceAccount=/path/to/google-service-account.json
• use Google Workload Identity to assign iam role to volume-provisioner-venona service account

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

Install Runner using GKE Disks:

venona install [options] --set-value=Storage.Backend=gcedisk \
                            --set-value=Storage.AvailabilityZone=us-central1-a \
                            --build-node-selector=failure-domain.beta.kubernetes.io/zone=us-central1-a \
                            [--set-file=Storage.GoogleServiceAccount=/path/to/google-service-account.json]

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.

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-venona 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: venona-ebs
    parameters:
      AvailabilityZone: us-west-2c # <----(Please change it to yours)
      volumeBackend: ebs
    provisioner: codefresh.io/dind-volume-provisioner-venona-codefresh-runtime

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 (venona-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: venona-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

Security roles

Installation of the Codefresh runner on a Kubernetes cluster is also setting up 2 groups of objects. Each one has own RBAC needs and therefore, expected created roles(and cluster-roles)

You can see the exact resource descriptors on GitHub.

Here is a list of the resources that are created during a Runner installation:

• Agent (grouped by /.*.venona.yaml/)
  • service-account.venona.yaml - The service account that the agent’s pod will use at the end.
  • cluster-role-binding.venona.yaml - The agent discovering K8S apis by calling to openapi/v2, this ClusterRoleBinding binds bootstraped ClusterRole by Kubernetes system:discovery to service-account.venona.yaml. This role has only permissions to make a GET calls to non-resources URLs.
  • role.venona.yaml - Allow to GET, CREATE and DELETE pods and persistent volume claims.
  • role-binding.venona.yaml - The agent is spinning up pods and pvc, this binding binds role.venona.yaml to service-account.venona.yaml.
• Runtime-environment (grouped by /.*.re.yaml/) - Kubernetes controller that spins up all required resources to provide a good caching experience during pipeline execution.
  • service-account.dind-volume-provisioner.re.yaml - The service account that the controller will use.
  • cluster-role.dind-volume-provisioner.re.yaml Defines all the permission needed for the controller to operate correctly.
  • cluster-role-binding.dind-volume-provisioner.yaml - Binds the ClusterRole to service-account.dind-volume-provisioner.re.yaml.

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 unner 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

What to read next

• Codefresh installation options
• Account management
• Access Control
• Codefresh API