This example contains a simple workflow service that illustrate consecutive invocation of two knative python function calls. The first function initialize a tensor of the specified size with random content using torch and return its contents. The second performs an addition of all the element returned by the previous step using numpy library.
You will need:
- Java 11+ installed
- Maven 3.8.6+ installed
- Docker
- Minikube
- Knative CLI
Open a terminal, go to test directory and type
kn func build
Once it finished, make sure minikube is running with knative profile activated (if you have setup it before, you just need to start minikube with minikube start -p profile and configure the tunnel running minikube tunnel --profile knative)
Once minikube is running, to load the image from your local docker into minikube registry type in a terminal
minikube image load dev.local/test -p knative
Now run that image as a knative service called test
kn service create test --image=dev.local/test --pull-policy=IfNotPresent
Open a terminal, go to test directory and type
kn func build
Once it finished, make sure minikube is running with knative profile activated (if you have setup it before, you just need to start minikube with minikube start -p profile and configure the tunnel running minikube tunnel --profile knative)
Once minikube is running, to load the image from your local docker into minikube registry type in a terminal
minikube image load dev.local/receiver -p knative
Now run that image as a knative service called receiver
kn service create receiver --image=dev.local/receiver --pull-policy=IfNotPresent
Open a terminal, go to workflow directory and run
mvn clean package
In some terminals, you need to ensure the local image is loaded into minikube by running
minikube image load dev.local/serverless-workflow-knative-python-quarkus:1.0-SNAPSHOT -p knative
and update the service accordingly
kn service update serverless-workflow-knative-python-quarkus --image=dev.local/serverless-workflow-knative-python-quarkus:1.0-SNAPSHOT --pull-policy=IfNotPresent
Once done, your workflow service should be available in knative, you need to find out the uri
kn service list | grep serverless-workflow-knative-python-quarkus
The URI of the service will be the one in the second column
To invoke the flow, you need to execute the following REST invocation, replacing the uri by the one resolved in the previous step and specifying the x and y dimension of the tensor.
curl -X POST -H 'Content-Type:application/json' -H 'Accept:application/json' -d '{"x":3,"y":3}' <uri>/TensorTest
The result is a float number with the sum of the randomly generated matrix.
{"id":"e80c8f2f-3753-45f0-b477-15812a3fe982","workflowdata":6.1767255663871765}