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-auth setup
- 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
docker build -t my-inference:latest .
docker tag my-inference:latest ghcr.io/myorg/my-inference:v1.0.0
docker push ghcr.io/myorg/my-inference:v1.0.0
Step 3: Validate Manifests Before Deploy
kubectl apply -f k8s/deployment.yaml --dry-run=server
kubectl diff -f k8s/deployment.yaml
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 |
Resources
Next Steps
See coreweave-sdk-patterns for inference client patterns.
