Validated Patterns

Deploying the Intel AMX accelerated Medical Diagnosis pattern

  • An OpenShift cluster

  • A GitHub account and a token for it with repositories permissions, to read from and write to your forks.

  • An S3-capable Storage (OpenShift Data Foundation is recommended) set up in your private cloud for the x-ray images

  • The Helm binary, see Installing Helm For installation tooling dependencies, see Patterns quick start.


The Intel AMX accelerated Medical Diagnosis pattern does not have a dedicated hub or edge cluster.

Preparing for deployment

  1. Fork the medical-diagnosis repository on GitHub. You must fork the repository because your fork will be updated as part of the GitOps and DevOps processes.

  2. Clone the forked copy of this repository.

    $ git clone<your-username>/amx-accelerated-medical-diagnosis.git
  3. Create a local copy of the Helm values file that can safely include credentials.

    Do not commit this file. You do not want to push personal credentials to GitHub.

    Run the following commands:

    $ cp values-secret.yaml.template ~/values-secret-medical-diagnosis.yaml
    $ vi ~/values-secret-medical-diagnosis.yaml
    Example values-secret.yaml file
    version "2.0"
      # Database login credentials and configuration
      - name: xraylab
        - name: database-user
          value: xraylab
        - name: database-host
          value: xraylabdb
        - name: database-db
          value: xraylabdb
        - name: database-master-user
          value: xraylab
        - name: database-password
          onMissingValue: generate
          vaultPolicy: validatedPatternDefaultPolicy
        - name: database-root-password
          onMissingValue: generate
          vaultPolicy: validatedPatternDefaultPolicy
        - name: database-master-password
          onMissingValue: generate
          vaultPolicy: validatedPatternDefaultPolicy
      # Grafana Dashboard admin user/password
      - name: grafana
          - name: GF_SECURITY_ADMIN_USER:
            value: root
            onMissingValue: generate
            vaultPolicy: validatedPatternDefaultPolicy

    By default, Vault password policy generates the passwords for you. However, you can create your own passwords.

    When defining a custom password for the database users, avoid using the $ special character as it gets interpreted by the shell and will ultimately set the incorrect desired password.

  4. To customize the deployment for your cluster, update the values-global.yaml file by running the following commands:

    $ git checkout -b my-branch
    $ vi values-global.yaml

    Replace 'bucketSource' value. User can set any bucket name without special signs (besides '-') and numbers.

        cloudProvider: PROVIDE_CLOUDPROVIDER # Not required for on-prem
        storageClassName: "ocs-storagecluster-cephfs" # Default filesystem storage used on on-prem cluster, can be changed by user
        region: PROVIDE_REGION # Not required for on-prem
        clustername: "" # Not required for on-prem, pattern uses on-prem cluster value instead
        domain: "" # Not required for on-prem, pattern uses on-prem cluster value instead
          # Values for S3 bucket access
          # bucketSource: "provide s3 bucket name where images are stored"
          bucketSource: "PROVIDE_BUCKET_SOURCE"
          # Bucket base name used for image-generator and image-server applications.
          bucketBaseName: "xray-source"
    $ git add values-global.yaml
    $ git commit values-global.yaml
    $ git push origin my-branch
  5. To deploy the pattern, you can use the Validated Patterns Operator. If you do use the Operator, skip to validating the environment.

    Installing Validated Pattern this way may cause other components dependent on Vault to not start properly.

    After Validated pattern is installed using operator from OperatorHub user must type in secrets (from values-secret.yaml) into vault manually.

  6. To preview the changes that will be implemented to the Helm charts, run the following command:

    $ ./ make show
  7. Login to your cluster by running the following command:

    $ oc login

    Optional: Set the KUBECONFIG variable for the kubeconfig file path:

     export KUBECONFIG=~/<path_to_kubeconfig>

Check the values files before deployment

To ensure that you have the required variables to deploy the Medical Diagnosis pattern, run the ./ make predeploy command. You can review your values and make updates, if required.

You must review the following values files before deploying the Medical Diagnosis pattern:

Values FileDescription


Values file that includes the secret parameters required by the pattern


File that contains all the global values used by Helm to deploy the pattern

Before you run the ./pattern.msh make install command, ensure that you have the correct values for:

- bucketSource


  1. To apply the changes to your cluster, run the following command:

    $ ./ make install

    If the installation fails, you can go over the instructions and make updates, if required. To continue the installation, run the following command:

    $ ./ make update

    This step might take some time, especially for the OpenShift Data Foundation Operator components to install and synchronize. The ./ make install command provides some progress updates during the installation process. It can take up to twenty minutes. Compare your ./ make install run progress with the following video that shows a successful installation.

  2. Verify that the Operators have been installed.

    1. To verify, in the OpenShift Container Platform web console, navigate to OperatorsInstalled Operators page.

    2. Check that the Operator is installed in the openshift-operators namespace and its status is Succeeded. Ensure that OpenShift Data Foundation is listed in the list of installed Operators.

(Optional) Typing secrets into Vault manually

Log into the Vault using the root token, which can be found by executing the command:

oc -n vault get route vault -ojsonpath='{}'

Log into the Vault using root token. Root token to vault can be found by executing command:

oc -n imperative get secrets vaultkeys -ojsonpath='{.data.vault_data_json}' | base64 -d

At this point user can type into the Vault secret values specified in 'values-secret.yaml'

Using OpenShift GitOps to check on Application progress

To check the various applications that are being deployed, you can view the progress of the OpenShift GitOps Operator.

  1. Obtain the ArgoCD URLs and passwords.

    The URLs and login credentials for ArgoCD change depending on the pattern name and the site names they control. Follow the instructions below to find them, however you choose to deploy the pattern.

    Display the fully qualified domain names, and matching login credentials, for all ArgoCD instances:

    ARGO_CMD=`oc get secrets -A -o jsonpath='{range .items[*]}{"oc get -n "}{.metadata.namespace}{" routes; oc -n "}{.metadata.namespace}{" extract secrets/"}{}{" --to=-\\n"}{end}' | grep gitops-cluster`
    CMD=`echo $ARGO_CMD | sed 's|- oc|-;oc|g'`
    eval $CMD
    Example output
    NAME                       HOST/PORT                                                                                      PATH   SERVICES                   PORT    TERMINATION            WILDCARD
    hub-gitops-server          hub-gitops-server   https   passthrough/Redirect   None
    # admin.password
    NAME                      HOST/PORT                                                                         PATH   SERVICES                  PORT    TERMINATION            WILDCARD
    cluster                                   cluster                   8080    reencrypt/Allow        None
    kam                                           kam                       8443    passthrough/None       None
    openshift-gitops-server          openshift-gitops-server   https   passthrough/Redirect   None
    # admin.password

    Examine the medical-diagnosis-hub ArgoCD instance. You can track all the applications for the pattern in this instance.

  2. Check that all applications are synchronized. There are thirteen different ArgoCD applications that are deployed as part of this pattern.

Set up object storage

Modified version of a Medical Diagnosis pattern requires to use on-prem object storage. Instead of a AWS S3 (or other cloud equivalent) user can set up the Ceph RGW object storage. To communicate with its API user can utilize aws-cli. The installation manual is available on Amazon website

Set up local S3 object storage only after ODF is properly deployed by validated pattern.

User can extract CEPH_RGW_ENDPOINT by executing the command:

oc -n openshift-storage get route s3-rgw -ojsonpath='{}'

AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY of RGW object store can be found by performing following commands:

oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_ACCESS_KEY_ID}' | base64 -d
oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_SECRET_ACCESS_KEY}' | base64 -d

These values are required to properly set up object storage, if any of them is not accessible (user get error while trying to retrieve them) that may be indicator that ODF is not working properly.

First thing to check is s3-rgw. Please go to ArgoCD dashboard, to odf application and resync s3-rgw component.

Second thing to do is to go to ArgoCD dashboard, to xraylab-init application and check if job bucket-init and create-s3-secret are done. If not please resync whole application.

Clone the repository with xray images and push them to the bucket:

git clone

Set environment variables. Create and configure the bucket:

export AWS_ACCESS_KEY_ID=$(oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_ACCESS_KEY_ID}' | base64 -d)
export AWS_SECRET_ACCESS_KEY=$(oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_SECRET_ACCESS_KEY}' | base64 -d)
export CEPH_RGW_ENDPOINT=$(oc -n openshift-storage get route s3-rgw -ojsonpath='{}')

cd jumpstart-library/demo1-xray-pipeline/base_elements/containers/image-init
aws --endpoint https://${CEPH_RGW_ENDPOINT} --no-verify-ssl s3api create-bucket --bucket ${CEPH_BUCKET_NAME}
aws --endpoint https://${CEPH_RGW_ENDPOINT} --no-verify-ssl s3 cp base_images/ s3://${CEPH_BUCKET_NAME}/ --recursive

Ceph RGW bucket needs specific bucket policy to be applied. To apply policy execute following commands:

export AWS_ACCESS_KEY_ID=$(oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_ACCESS_KEY_ID}' | base64 -d)
export AWS_SECRET_ACCESS_KEY=$(oc -n xraylab-1 get secret s3-secret-bck -ojsonpath='{.data.AWS_SECRET_ACCESS_KEY}' | base64 -d)
export CEPH_RGW_ENDPOINT=$(oc -n openshift-storage get route s3-rgw -ojsonpath='{}')

cd medical-diagnosis
aws --endpoint https://${CEPH_RGW_ENDPOINT} --no-verify-ssl s3api put-bucket-policy --bucket ${CEPH_BUCKET_NAME} --policy file://./bucket-policy.json

Viewing the Grafana based dashboard

  1. Accept the SSL certificates on the browser for the dashboard. In the OpenShift Container Platform web console, go to the Routes for project openshift-storage`. Click the URL for the s3-rgw.

    storage route

    Ensure that you see some XML and not the access denied error message.

    storage rgw route
  2. While still looking at Routes, change the project to xraylab-1. Click the URL for the image-server. Ensure that you do not see an access denied error message. You must to see a Hello World message.

    grafana routes
  3. Turn on the image file flow. There are three ways to go about this.

    You can go to the command-line (make sure you have KUBECONFIG set, or are logged into the cluster.

    $ oc scale deploymentconfig/image-generator --replicas=1 -n xraylab-1

    Or you can go to the OpenShift UI and change the view from Administrator to Developer and select Topology. From there select the xraylab-1 project.

    dev topology

    Right-click on the image-generator pod icon and select Edit Pod count.

    dev topology menu

    Up the pod count from 0 to 1 and save.

    dev topology pod count

    Alternatively, you can have the same outcome on the Administrator console.

    Go to the OpenShift UI under Workloads, select Deploymentconfigs for Project xraylab-1. Click image-generator and increase the pod count to 1.

    start image flow

    OpenShift GitOps view should be similar to the following:

    gitops view

    All applications should be healthy for pattern to work correctly, even if some applications may be OutOfSync. If any application is in 'unhealthy' state common solution is to sync the application. For other issues please refer to

Making some changes on the dashboard

You can change some of the parameters and watch how the changes effect the dashboard.

  1. You can increase or decrease the number of image generators.

    $ oc scale deploymentconfig/image-generator --replicas=2

    Check the dashboard.

    $ oc scale deploymentconfig/image-generator --replicas=0

    Watch the dashboard stop processing images.

  2. You can also simulate the change of the AI model version - as it’s only an environment variable in the Serverless Service configuration.

    $ oc patch --type=json -p '[{"op":"replace","path":"/spec/template/metadata/annotations/revisionTimestamp","value":"'"$(date +%F_%T)"'"},{"op":"replace","path":"/spec/template/spec/containers/0/env/0/value","value":"v2"}]'

    This changes the model version value, and the revisionTimestamp in the annotations, which triggers a redeployment of the service.