Development Guide

Set up a Karpenter development environment


The following tools are required for contributing to the Karpenter project.

kubectlbrew install kubectl
helmbrew install helm
Other toolsmake toolchain


Setup / Teardown

Based on how you are running your Kubernetes cluster, follow the Environment specific setup to configure your environment before you continue. You can choose to either run the Karpenter controller locally on your machine, pointing to the Kubernetes cluster specified in your ~/.kube/config or inside the Kubernetes cluster specified in your ~/.kube/config deployed with Helm.


Once you have your environment set up, run the following commands to run the Karpenter Go binary against the Kubernetes cluster specified in your ~/.kube/config

make run

Inside a Kubernetes Cluster

Once you have your environment set up, to install Karpenter in the Kubernetes cluster specified in your ~/.kube/config run the following commands.

make apply # Install Karpenter
make delete # Uninstall Karpenter

Developer Loop

  • Make sure dependencies are installed
    • Run make codegen to make sure yaml manifests are generated (requires a working set of AWS credentials, see Specifying Credentials)
    • Run make toolchain to install cli tools for building and testing the project
  • You will need a personal development image repository (e.g. ECR)
    • Make sure you have valid credentials to your development repository.
    • $KO_DOCKER_REPO must point to your development repository
    • Your cluster must have permissions to read from the repository

Build and Deploy

Note: these commands do not rely on each other and may be executed independently

make apply # quickly deploy changes to your cluster
make presubmit # run codegen, lint, and tests

If you are only interested in building the Karpenter images and not deploying the updated release to your cluster immediately with Helm, you can run

make image # build and push the karpenter images


make test       # E2E correctness tests

Change Log Level

By default, make apply will set the log level to debug. You can change the log level by setting the log level in your Helm values.

--set logLevel=debug

Debugging Metrics


open http://localhost:8000/metrics && kubectl port-forward service/karpenter -n karpenter 8000


gio open http://localhost:8000/metrics && kubectl port-forward service/karpenter -n karpenter 8000

Tailing Logs

While you can tail Karpenter’s logs with kubectl, there’s a number of tools out there that enhance the experience. We recommend Stern:

stern -n karpenter -l

Environment specific setup


For local development on Karpenter you will need a Docker repo which can manage your images for Karpenter components. You can use the following command to provision an ECR repository. We recommend using a single “dev” repository for development across multiple projects, and to use specific image hashes instead of image tags.

aws ecr create-repository \
    --repository-name dev \
    --image-scanning-configuration scanOnPush=true \
    --region "${AWS_DEFAULT_REGION}"

Once you have your ECR repository provisioned, configure your Docker daemon to authenticate with your newly created repository.

aws ecr get-login-password --region "${AWS_DEFAULT_REGION}" | docker login --username AWS --password-stdin "${KO_DOCKER_REPO}"

Finally, to deploy the correct IAM permissions, including the instance profile for provisioned nodes, run

make setup

Profiling memory

Karpenter exposes a pprof endpoint on its metrics port.

Learn about profiling with pprof:


brew install graphviz
go install

Get a profile

# Connect to the metrics endpoint
kubectl port-forward service/karpenter -n karpenter 8000
open http://localhost:8000/debug/pprof/
# Visualize the memory
go tool pprof -http localhost:8000/debug/pprof/heap