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Deploying the Medical Diagnosis pattern

Table of contents

  1. Deploying the Medical Diagnosis pattern
  2. Prerequisites
    1. Setting up an S3 Bucket for the xray-images
  3. Utilities
  4. Preparation
    1. Check the values files before deployment
  5. Deploy
    1. Using OpenShift GitOps to check on Application progress
    2. Viewing the Grafana based dashboard
    3. Making some changes on the dashboard
  6. Next Steps

Prerequisites

  1. An OpenShift cluster ( Go to the OpenShift console). See also sizing your cluster.
  2. A GitHub account (and a token for it with repositories permissions, to read from and write to your forks)
  3. S3-capable Storage set up in your public/private cloud for the x-ray images
  4. The helm binary, see here

The following packages need to be installed on your local system to seed the pattern correctly:

dnf install -y git make python3-pip

Use pip3 to install the following python packages required for ansible:

pip3 install --user --upgrade pip ansible kubernetes openshift awscli

Install the ansible-galaxy collection:

ansible-galaxy collection install kubernetes.core

The use of this pattern depends on having a Red Hat OpenShift cluster. In this version of the validated pattern there is no dedicated Hub / Edge cluster for the Medical Diagnosis pattern.

If you do not have a running Red Hat OpenShift cluster you can start one on a public or private cloud by using Red Hat’s cloud service.

Setting up an S3 Bucket for the xray-images

An S3 bucket is required for image processing. Please see the Utilities section below for creating a bucket in AWS S3. The following links provide information on how to create the buckets required for this validated pattern on several cloud providers.

Utilities

A number of utilities have been built by the validated patterns team to lower the barrier to entry for using the community or Red Hat Validated Patterns. To use these utilities you will need to export some environment variables for your cloud provider:

For AWS (replace with your keys):

export AWS_ACCESS_KEY_ID=AKXXXXXXXXXXXXX
export AWS_SECRET_ACCESS_KEY=gkXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Then we need to create the S3 bucket and copy over the data from the validated patterns public bucket to the created bucket for your demo. You can do this on the cloud providers console or use the scripts provided on validated-patterns-utilities repository.

python s3-create.py -b mytest-bucket -r us-west-2 -p
python s3-sync-buckets.py -s com.validated-patterns.xray-source -t mytest-bucket -r us-west-2

The output should look similar to this edited/compressed output.

Bucket setup]

Keep note of the name of the bucket you created, as you will need it for further pattern configuration. There is some key information you will need to take note of that is required by the ‘values-global.yaml’ file. You will need the URL for the bucket and its name. At the very end of the values-global.yaml file you will see a section for s3: were these values need to be changed.

Preparation

  1. Fork the medical-diagnosis repo on GitHub. It is necessary to fork because your fork will be updated as part of the GitOps and DevOps processes.

  2. Clone the forked copy of this repository.

    git clone git@github.com:<your-username>/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 credentials to GitHub.

    cp values-secret.yaml.template ~/values-secret.yaml
vi ~/values-secret.yaml

values-secret.yaml example

secrets:
  xraylab:
    database-user: xraylab
    database-password: ## Insert your custom password here ##
    database-root-password: ## Insert your custom password here ##
    database-host: xraylabdb
    database-db: xraylabdb
    database-master-user: xraylab
    database-master-password: ## Insert your custom password here ##

  grafana:
    GF_DATASRC_USER: xraylab
    GF_DATASRC_PASSWORD: ## Insert your custom password here ##
    GF_DATASRC_DB: xraylabdb
    GF_DATASRC_URL: xraylabdb
    GF_SECURITY_ADMIN_PASSWORD: ## Insert your custom password here ##
    GF_SECURITY_ADMIN_USER: root

When you edit the file you can make changes to the various DB and Grafana passwords if you wish.

  1. Customize the values-global.yaml for your deployment

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

Replace instances of PROVIDE_ with your specific configuration

   ...omitted
   datacenter:
     cloudProvider: PROVIDE_CLOUD_PROVIDER #aws, azure
     storageClassName: PROVIDE_STORAGECLASS_NAME #gp2 (aws)
     region: PROVIDE_CLOUD_REGION #us-east-1
     clustername: PROVIDE_CLUSTER_NAME #OpenShift clusterName
     domain: PROVIDE_DNS_DOMAIN #blueprints.rhecoeng.com
   
    s3:
      # Values for S3 bucket access
      # Replace <region> with AWS region where S3 bucket was created
      # Replace <cluster-name> and <domain> with your OpenShift cluster values
      # bucketSource: "https://s3.<region>.amazonaws.com/<s3_bucket_name>"
      bucketSource: PROVIDE_BUCKET_SOURCE #"https://s3.us-east-2.amazonaws.com/com.validated-patterns.xray-source"
      # Bucket base name used for xray images
      bucketBaseName: "xray-source" 

   git add values-global.yaml
   git commit values-global.yaml
   git push origin my-branch

  1. You can deploy the pattern using the validated pattern operator. If you do use the operator then skip to Validating the Environment below.

  2. Preview the changes that will be made to the Helm charts.

    make show

  3. Login to your cluster using oc login or exporting the KUBECONFIG

    oc login

    or set KUBECONFIG to the path to your kubeconfig file. For example:

    export KUBECONFIG=~/my-ocp-env/auth/kubconfig

Check the values files before deployment

You can run a check before deployment to make sure that you have the required variables to deploy the Medical Diagnosis Validated Pattern.

You can run make predeploy to check your values. This will allow you to review your values and changed them in the case there are typos or old values. The values files that should be reviewed prior to deploying the Medical Diagnosis Validated Pattern are:

Values File Description
values-secret.yaml This is the values file that will include the xraylab section with all the database secrets
values-global.yaml File that is used to contain all the global values used by Helm

Make sure you have the correct domain, clustername, externalUrl, targetBucket and bucketSource values.

Predeploy

Deploy

  1. Apply the changes to your cluster

    make install

    If the install fails and you go back over the instructions and see what was missed and change it, then run make update to continue the installation.

  2. This takes some time. Especially for the OpenShift Data Foundation operator components to install and synchronize. The make install provides some progress updates during the install. It can take up to twenty minutes. Compare your make install run progress with the following video showing a successful install.

    Demo

  3. Check that the operators have been installed in the UI.

    OpenShift UI -> Installed Operators

    The main operator to watch is the OpenShift Data Foundation.

Using OpenShift GitOps to check on Application progress

You can also check on the progress using OpenShift GitOps to check on the various applications deployed.

  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/"}{.metadata.name}{" --to=-\\n"}{end}' | grep gitops-cluster`
CMD=`echo $ARGO_CMD | sed 's|- oc|-;oc|g'`
eval $CMD

    The result should look something like:

    NAME                       HOST/PORT                                                                                      PATH   SERVICES                   PORT    TERMINATION            WILDCARD
datacenter-gitops-server   datacenter-gitops-server-medical-diagnosis-datacenter.apps.wh-medctr.blueprints.rhecoeng.com          datacenter-gitops-server   https   passthrough/Redirect   None
# admin.password
xsyYU6eSWtwniEk1X3jL0c2TGfQgVpDH
NAME                      HOST/PORT                                                                         PATH   SERVICES                  PORT    TERMINATION            WILDCARD
cluster                   cluster-openshift-gitops.apps.wh-medctr.blueprints.rhecoeng.com                          cluster                   8080    reencrypt/Allow        None
kam                       kam-openshift-gitops.apps.wh-medctr.blueprints.rhecoeng.com                              kam                       8443    passthrough/None       None
openshift-gitops-server   openshift-gitops-server-openshift-gitops.apps.wh-medctr.blueprints.rhecoeng.com          openshift-gitops-server   https   passthrough/Redirect   None
# admin.password
FdGgWHsBYkeqOczE3PuRpU1jLn7C2fD6

    The most important ArgoCD instance to examine at this point is multicloud-gitops-datacenter. This is where all the applications for the pattern can be tracked.

  2. Check all applications are synchronised. There are thirteen different ArgoCD “applications” deployed as part of this pattern.

Viewing the Grafana based dashboard

  1. First we need to accept SSL certificates on the browser for the dashboard. In the OpenShift console go to the Routes for project openshift-storage. Click on the URL for the s3-rgw.

    Storage Routes)

    Make sure that you see some XML and not an access denied message.

    Storage Routes)

  2. While still looking at Routes, change the project to xraylab-1. Click on the URL for the image-server. Make sure you do not see an access denied message. You ought to see a Hello World message.

    Storage 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.

    Xraylab-1 Topology)

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

    Pod menu)

    Up the pod count from 0 to 1 and save.

    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 on image-generator and increase the pod count to 1.

    Image Pod)

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 service.serving.knative.dev/risk-assessment --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, as well as the revisionTimestamp in the annotations, which triggers a redeployment of the service.

Next Steps

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