Sieve Takehome

This is a simple example of an API to orchestrate a mock ML Model on Kubernetes and store details on Redis. The project is deployed on Minikube, but can be deployed on any K8s cluster!

Note: this project will run in the default namespace

Prerequisites

• docker
• minikube
• kubectl cli

Steps

1. Start Minikube

minikube start

2. Make new Docker image Accessible within Minikube

eval $(minikube docker-env)

3. Build API and Model Images

make build-api
make build-model

3. Deploy Application Stack

make deploy

4. Port-foward the API in a new terminal

kubectl port-forward $(kubectl get pod -l app=sieve-api  -o=jsonpath='{.items[0].metadata.name}') 8080:8080

5. Trigger a new Model

# curl -X POST http://localhost:8080/push -H 'Content-Type: application/json' -d '{"input": "dommy"}'
{"id":"edbf2f12-9148-4623-8f6e-ec35d254dbf4"}

6. Query Status

# curl http://localhost:8080/status/<id>
{"status":"queued"}

7. Query Result

# curl http://localhost:8080/data/<id>
{"input":"dommy","latency":"48","output":"world0d2bfd51-347a-4221-9b6a-60a9e7873ee5"}

Test

While the API port is forwarded, to trigger the test file run. (I'm using python3 cli)

python3 -m pip install requests
make test

After a test run, you can clear out the Completed Model Pods with:

kubectl delete jobs `kubectl get jobs -o custom-columns=:.metadata.name`

Teardown

Finally, to remove the application stack

make cleanup