CoreWeave Local Dev Loop

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

# 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

Resources

• CoreWeave CKS Docs
• kubectl dry-run

Next Steps

See coreweave-sdk-patterns for inference client patterns.