Imported from jeremylongshore/tons-of-skills-marketplace (
skills/.curated/coreweave-local-dev-loop/SKILL.md). Install upstream withnpx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-local-dev-loop. Copyright stays with the author (MIT).
CoreWeave Local Dev Loop
Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
Overview
Local development workflow for CoreWeave: build containers, test YAML manifests with dry-run, push to registry, and deploy to CoreWeave CKS.
Prerequisites
- Completed
coreweave-install-authsetup - Docker installed locally
- Container registry access (Docker Hub, GHCR, or CoreWeave registry)
Instructions
Step 1: Project Structure
my-inference-service/
├── Dockerfile
├── src/
│ ├── server.py # Inference server code
│ └── model_config.py # Model configuration
├── k8s/
│ ├── deployment.yaml # GPU deployment manifest
│ ├── service.yaml # Service and ingress
│ └── hpa.yaml # Horizontal pod autoscaler
├── scripts/
│ ├── build.sh # Build and push container
│ └── deploy.sh # Deploy to CoreWeave
├── .env.local
└── Makefile
Step 2: Build and Push Container
# Build locally
docker build -t my-inference:latest .
# Tag for registry
docker tag my-inference:latest ghcr.io/myorg/my-inference:v1.0.0
# Push
docker push ghcr.io/myorg/my-inference:v1.0.0
Step 3: Validate Manifests Before Deploy
# Dry-run against CoreWeave cluster
kubectl apply -f k8s/deployment.yaml --dry-run=server
# Diff against current state
kubectl diff -f k8s/deployment.yaml
# Check resource requests match available GPU types
kubectl get nodes -l gpu.nvidia.com/class=A100_PCIE_80GB --no-headers | wc -l
Step 4: Deploy and Watch
kubectl apply -f k8s/
kubectl rollout status deployment/my-inference
kubectl logs -f deployment/my-inference
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Image pull backoff | Wrong registry or no pull secret | Create imagePullSecret |
| CUDA mismatch | Driver vs container version | Match CUDA version to node drivers |
| Dry-run fails | Invalid manifest | Fix YAML syntax |
Output
- A locally built, versioned image and a server-validated deployment manifest.
- A staging rollout receipt with readiness and bounded log evidence.
- A repeatable local-to-cluster loop that leaves production credentials and data out of the workspace.
Examples
Use the staging namespace for the complete loop and inspect the diff before apply:
docker build -t ghcr.io/myorg/my-inference:dev-20260826 .
kubectl -n inference-staging diff -f k8s/deployment.yaml
kubectl -n inference-staging apply --dry-run=server -f k8s/
kubectl -n inference-staging apply -f k8s/
If the server dry-run fails, correct the manifest before pushing an image or changing GPU quota. Do not point local development at production namespaces or copy kubeconfigs between environments.
Resources
Next Steps
See coreweave-sdk-patterns for inference client patterns.