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Version: Deploy 22.2

Installing Deploy on GCP GKE

This section describes how to install the Deploy application on GCP GKE.

Audience​

This guide is intended for administrators with cluster administrator credentials who are responsible for application deployment.

Before You Begin​

The following are the prerequisites required to migrate to the operator-based deployment:

  • Docker version 17.03 or later
  • The kubectl command-line tool
  • Access to a Kubernetes cluster version 1.19 or later
  • Kubernetes cluster configuration

Keycloak as the Default Authentication Manager for Deploy​

  • Keycloak is the default authentication manager with Deploy 22.1 and later.
  • This is defined by the spec.keycloak.install parameter that is set to true by default in the daideploy_cr.yaml file.
  • If you want to disable Keycloak as the default authentication manager for Digitial.ai Deploy, set the spec.keycloak.install parameter to false.
  • After you disable the Keycloak authentication, the default login credentials (admin/admin) will be applicable when you log in to the Digital.ai Deploy interface.
  • For more information about how to configure Keycloak for Kubernetes Operator-based Installer, see Keycloak Configuration for Kubernetes Operator Installer.

Step 1—Create a Folder for Installation Tasks​

Create a folder on your workstation from where you will execute the installation tasks, for example, DeployInstallation.

Step 2—Download the Operator ZIP​

  1. Download the Digital.ai Deploy Operator-based installer zip file from the Deploy Software Distribution site. For example, deploy-operator-gcp-gke-22.2.0.zip.
  2. Extract the ZIP file to the DeployInstallation folder.

Step 3—Update the GCP GKE Cluster Resource Files​

To deploy the Deploy application on the Kubernetes cluster, update the infrastructure.yaml file parameters (Infrastructure File Parameters) in DeployInstallation folder with the parameters corresponding to the kubeconfig file (GCP GKE Kubernetes Cluster Configuration File Parameters) as described in the table below. You can find the Kubernetes cluster information in the default location ~/.kube/config. Ensure the location of the kubeconfig configuration file is your home directory.

Note: The deployment will not proceed further if the infrastructure.yaml is updated with wrong details.

Infrastructure File ParametersGCP GKE Kubernetes Cluster Configuration File ParametersSteps to Follow
apiServerURLserverEnter the server parameter value.
caCertcertificate-authority-dataBefore updating the parameter value, decode to base 64 format.
tokenaccess tokenEnter the access token details.

Step 4—Convert License and Repository Keystore Files to Base64 Format​

  1. Run the following command to get the storage class list:

    kubectl get sc

  2. Run the keytool command below to generate the RepositoryKeystore:

    keytool -genseckey {-alias alias} {-keyalg keyalg} {-keysize keysize} [-keypass keypass] {-storetype storetype} {-keystore keystore} [-storepass storepass]

    Example

    keytool -genseckey -alias deployit-passsword-key -keyalg aes -keysize 128 -keypass deployit -keystore /tmp/repository-keystore.jceks -storetype jceks -storepass test123

  3. Convert the Deploy license and the repository keystore files to the base64 format:

    • To convert the xldLicense into base64 format, run:

      cat <License.lic> | base64 -w 0

    • To convert RepositoryKeystore to base64 format, run:

      cat <repository-keystore.jceks> | base64 -w 0

      Note: The above commands are for Linux-based systems. For Windows, there is no built-in command to directly perform Base64 encoding and decoding. However, you can use the built-in command certutil -encode/-decode to indirectly perform Base64 encoding and decoding.

Step 5—Update the Default Digitial.ai Deploy Custom Resource Definitions​

  1. Go to \digitalai-deploy\kubernetes and open the daideploy_cr.yaml file.

  2. Update the mandatory parameters as described in the following table:

    Note: For deployments on test environments, you can use most of the parameters with their default values in the daideploy_cr.yaml file.

    ParameterDescription
    AdminPasswordAdmin password for xl-deploy
    KeystorePassphraseThe passphrase for the RepositoryKeystore.
    Persistence.StorageClassThe storage class that must be defined as GCP GKE cluster.
    RepositoryKeystoreConvert the license file for Digital.ai Deploy to the base64 format.
    ingress.hostsDNS name for accessing UI of Digital.ai Deploy.
    spec.keycloak.ingress.rules[0].hostDNS name for accessing UI of embedded Keycloak.
    postgresql.persistence.storageClassStorage Class to be defined as PostgreSQL.
    rabbitmq.persistence.storageClassStorage Class to be defined as RabbitMQ.
    xldLicenseDeploy license

    Note: For deployments on production environments, you must configure all the relevant/required parameters for your GCP GKE production setup, in the daideploy_cr.yaml file. See Default Parameters to know more about the parameters available in the Digital.ai Deploy's daideploy_cr.yaml file and their default values. You must update the default values for the parameters per your requirements.

    To configure the Keycloak parameters for OIDC authentication, see Keycloak Configuration for Kubernetes Operator Installer.

  3. Update the relevant/required parameters for your GCP GKE production setup in the daideploy_cr.yaml file. See Default Parameters.

    If you want to use an existing database and messaging queue, refer Using Existing DB and Using Existing MQ topics, and update the daideploy_cr.yaml file. For information on how to configure SSL/TLS with Digital.ai Deploy, see Configuring SSL/TLS.

Step 6—Download and Set up the XL CLI​

See Install the XL-CLI.

Note: Use the version that matches your product version in the public folder.

Step 7—Set up the Namespace​

You can use any namespace for the installation. By default, the digitalai namespace is used.

kubectl create namespace digitalai

To use a custom namespace, create a namespace and replace digitalai with your custom namespace.

If you would like to install multiple Deploy instances on the same cluster, you need to use a custom namespace.

See Install Deploy in a Custom Namespace.

Step 8—Set up the Digital.ai Deploy Container Instance​

  1. Run the following command to download and start the Digital.ai Deploy instance:

    Note: A local instance of Digital.ai Deploy is used to automate the product installation on the Kubernetes cluster.

    docker run -d -e "ADMIN_PASSWORD=admin" -e "ACCEPT_EULA=Y" -p 4516:4516 --name xld xebialabs/xl-deploy:22.2.0

    Note: Before running the command check if there is already running docker containers with name xld or the same port with docker ps command. Stop and delete the container with commands, for example with name xld: docker stop xld; docker rm xld.

  2. Wait Deploy has started and access the Deploy interface, go to:
    http://<host IP address>:4516/

Step 9—Start the Deployment​

Go to the deploy-operator-gcp-gke folder of the extracted ZIP file and run the following command:

xl apply -v -f digital-ai.yaml

Step 10—Verify the Deployment Status​

  1. Check the deployment job completion using XL CLI.
    The deployment job starts the execution of various tasks as defined in the digital-ai.yaml file in a sequential manner. If you encounter an execution error while running the scripts, the system displays error messages. The average time to complete the job is around 10 minutes.

    Note: The running time depends on the environment.

    Deployment Status

    To troubleshoot runtime errors, see Troubleshooting Operator Based Installer.

Verify the deployment succeeded, do one of the following:

  • Open the Deploy application, go to the Explorer tab, and from Library, click Monitoring > Deployment tasks

    Successful Deploy Deployment

  • Run the following command in a terminal or command prompt:

    Deployment Verification Using CLI Command

Step 11—Perform Sanity Checks​

Open the Deploy application and perform the required deployment sanity checks.

Configure the User Permissions​