Fork hhttps://github.com/nexus-Six/edge_anomaly_detection_demo.git into your own Git repo (you will need to make changes in this repo).
Clone the forked repo to your local home directory
cd ~
git clone <your_git_organisation>/edge_anomaly_detection_demo.git
Replace the git repo paths in
- config/instances/manuela-quickstart/manuela-quickstart-line-dashboard-application.yaml
- config/instances/manuela-quickstart/manuela-quickstart-messaging-application.yaml
- config/instances/manuela-quickstart/manuela-quickstart-machine-sensor-application.yaml
Replace the OCP paths in
- config/instances/manuela-quickstart/line-dashboard/line-dashboard-configmap-config.json
- config/instances/manuela-quickstart/line-dashboard/line-dashboard-route.yaml
- config/instances/manuela-quickstart/machine-sensor/machine-sensor-1-configmap.properties
- config/instances/manuela-quickstart/machine-sensor/machine-sensor-2-configmap.properties
- config/instances/manuela-quickstart/messaging/route.yaml
Push the changes
git add .
git commit -m "adapt repo URLs"
git push
Installing the OpenShift GitOps Operator is a manual step in this quickstart guide.
Follow the OpenShift Documentation. I.e. Installing OpenShift GitOps Operator in web console
Install with default settings
Wait until the OpenShift GitOps operator is installed. This might take a short while.
After the Red Hat OpenShift GitOps Operator is installed, it automatically sets up a ready-to-use Argo CD instance that is available in the openshift-gitops namespace, and an Argo CD icon is displayed in the console toolbar.
After the installation is complete, ensure that all the pods in the openshift-gitops namespace are running:
oc get pods -n openshift-gitops-operator
Example output:
NAME READY STATUS RESTARTS AGE
openshift-gitops-operator-controller-manager-6849445bc8-gg49r 2/2 Running 0 21s
Note, in this quickstart deployment, we will use the cluster Argo CD instance and won't deploy any Manuela specific Argo CD instance.
Check that you can login into the Argo CD instance by using the Argo CD admin account:
- Follow the instructions in the documentation: Logging in to the Argo CD instance by using the Argo CD admin account
- Or, pull the
admin
password with
oc get secret openshift-gitops-cluster -o 'go-template={{index .data "admin.password"}}' -n openshift-gitops | base64 -d
The admin credentials are needed because the manual sync runs into problems, when you login into ArgoCD with an OpenShift user.
We will deploy the three part of the core Manuele Applicatoion step by step:
- Messaging component inclusive the AMQ Broker Operator and instance
- Line Dashboard
- Senor Simulators
Deploy the Messaging component:
oc apply -f config/instances/manuela-quickstart/manuela-quickstart-messaging-application.yaml
Example output:
application.argoproj.io/manuela-quickstart-messaging created
Check the status:
oc get application -n openshift-gitops
Example output:
NAME SYNC STATUS HEALTH STATUS
manuela-quickstart-messaging Synced Progressing
Use the Argo CD UI to follow the installation.
Deploy the line dashboard component
oc apply -f config/instances/manuela-quickstart/manuela-quickstart-line-dashboard-application.yaml
Example output:
application.argoproj.io/manuela-quickstart-line-dashboard created
Check the status:
oc get application -n openshift-gitops
Example output:
NAME SYNC STATUS HEALTH STATUS
manuela-quickstart-line-dashboard Synced Progressing
manuela-quickstart-messaging Synced Healthy
Wait until the application is Healthy
.
Open the Manuela line dashboard web application and view the sensor data by selecting the "Realtime Data" menu item. Retrieve the UI URL via:
oc -n manuela-quickstart-line-dashboard get route line-dashboard
Note, no sensor data is displayed , because the senor simulators are not deployed yet.
Deploy the line machine sensor application:
oc apply -f config/instances/manuela-quickstart/manuela-quickstart-machine-sensor-application.yaml
Example output:
application.argoproj.io/manuela-quickstart-machine-sensor created
Wait until the application is Healthy
.
oc get application -n openshift-gitops
Example output:
NAME SYNC STATUS HEALTH STATUS
manuela-quickstart-line-dashboard Synced Healthy
manuela-quickstart-machine-sensor Synced Healthy
manuela-quickstart-messaging Synced Healthy
- Install the OpenShift DataScience Operator from the Operatorhub with default settings
Add additional serving runtimes to OpenShift Datascience
oc apply -f config/rhods/custom-serving-runtimes.yaml
Install Minio S3 Storage
oc new-project minio
oc apply -f config/minio/minio.yaml
- Look for the minio-ui Route in the minio project and log in with
- User : minio
- Pass : minio123
- "Create a Bucket"
- Bucket Name : ml-edge-demo
- Switch to the Object Browser
- Upload the initial ml model ml/mode.joblib
Open the OpenShift Datascience Dasboard by clicking on the link provided at the top right menu under the square icon
- In the menu on the left click on Data Science Projects
- Create Data Science Project
- Name : ml edge demo
- Create
- Create Data Connection
- Name : ml edge demo
- Access key : minio
- Secret key : minio123
- Endpoint : http://minio-service.minio.svc:9000
- Region : eu-central-1
- Bucket : ml-edge-demo
- Add dataconnection
- Click on Models and model > Add server
- Model server name : mlserver
- Serving runtime : mlserver
- Check :
Make deployed models available through an external route
- Add
In the menu on the left click on Model Serving
- Click : Deploy Model
- Project : ml edge demo
- Model Name : ml edge demo model
- Model servers : mlserver
- Model Framework : skloearn - 0
- Existing data connection : ml edge demo
- Path : model.joblib
- Deploy
- Wait for Model to deploy
Test the model on the command line :
curl -k -X POST -H 'Content-Type: application/json' -d '{"inputs": [{ "name": "predict", "shape": [1, 5], "datatype": "FP32", "data": [0.0, 0.0, 0.0, 0.0 , 12.0 ]}]}' <your_model_serving endpoint>
Add a non https Route for your app
- In the OpenSHift Consol go to Project ml-edge-demo
- Create new Route
- Name : plain
- Service : modelmesh-serving
- Target port : 8008
- Create
Test the new Route as well, adding v2/models/ml-edge-demo/infer
to the Route
Add the new endpoint to the Edge-Consumer via GitOPS
In the cloned Repository go to config/templates/manuela/messaging/messaging-configmap.properties
and set the Variable ANOMALY_DETECTION_URL with the full plain URL that you have just used for testing