ml-monitoring

Jeremy Jordan

This repository provides an example setup for monitoring an ML system deployed on Kubernetes.

Blog post: https://www.jeremyjordan.me/ml-monitoring/

Components:

• ML model served via FastAPI
• Export server metrics via prometheus-fastapi-instrumentator
• Simulate production traffic via locust
• Monitor and store metrics via Prometheus
• Visualize metrics via Grafana

Setup

1. Ensure you can connect to a Kubernetes cluster and have kubectl and helm installed.
   • You can easily spin up a Kubernetes cluster on your local machine using minikube.

minikube start --driver=docker --memory 4g --nodes 2

2. Deploy Prometheus and Grafana onto the cluster using the community Helm chart.

kubectl create namespace monitoring
helm install prometheus-stack prometheus-community/kube-prometheus-stack -n monitoring

3. Verify the resources were deployed successfully.

kubectl get all -n monitoring

4. Connect to the Grafana dashboard.

kubectl port-forward svc/prometheus-stack-grafana 8000:80 -n monitoring

• Go to http://127.0.0.1:8000/
• Log in with the credentials:
  • Username: admin
  • Password: prom-operator
  • (This password can be configured in the Helm chart values.yaml file)

5. Import the model dashboard.
   • On the left sidebar, click the "+" and select "Import".
   • Copy and paste the JSON defined in dashboards/model.json in the text area.

Deploy a model

This repository includes an example REST service which exposes an ML model trained on the UCI Wine Quality dataset.

You can launch the service on Kubernetes by running:

kubectl apply -f kubernetes/models/

You can also build and run the Docker container locally.

docker build -t wine-quality-model -f model/Dockerfile .
docker run -d -p 3000:80 -e ENABLE_METRICS=true wine-quality-model

Note: In order for Prometheus to scrape metrics from this service, we need to define a ServiceMonitor resource. This resource must have the label release: prometheus-stack in order to be discovered. This is configured in the Prometheus resource spec via the serviceMonitorSelector attribute.

You can verify the label required by running:

kubectl get prometheuses.monitoring.coreos.com prometheus-stack-kube-prom-prometheus -n monitoring -o yaml

Simulate production traffic

We can simulate production traffic using a Python load testing tool called locust. This will make HTTP requests to our model server and provide us with data to view in the monitoring dashboard.

You can begin the load test by running:

kubectl apply -f kubernetes/load_tests/

By default, production traffic will be simulated for a duration of 5 minutes. This can be changed by updating the image arguments in the kubernetes/load_tests/locust_master.yaml manifest.

You can also modify the community Helm chart instead of using the manifests defined in this repo.

Uploading new images

This process can eventually be automated with a Github action, but remains manual for now.

1. Obtain a personal access token to connect with the Github container registry.

echo "INSERT_TOKEN_HERE" >> ~/.github/cr_token

2. Authenticate with the Github container registry.

cat ~/.github/cr_token | docker login ghcr.io -u jeremyjordan --password-stdin

3. Build and tag new Docker images.

docker build -t wine-quality-model:0.3 -f model/Dockerfile .
docker tag wine-quality-model:0.3 ghcr.io/jeremyjordan/wine-quality-model:0.3

docker build -t locust-load-test:0.2 -f load_test/Dockerfile .
docker tag locust-load-test:0.2 ghcr.io/jeremyjordan/locust-load-test:0.2

4. Push Docker images to container registery.

docker push ghcr.io/jeremyjordan/wine-quality-model:0.3
docker push ghcr.io/jeremyjordan/locust-load-test:0.2

5. Update Kubernetes manifests to use the new image tag.

Teardown instructions

To stop the model REST server, run:

kubectl delete -f kubernetes/models/

To stop the load tests, run:

kubectl delete -f kubernetes/load_tests/

To remove the Prometheus stack, run:

helm uninstall prometheus-stack -n monitoring