Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/coreweave-local-dev-loop/SKILL.md). Install upstream with npx 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-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

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

• CoreWeave CKS Docs
• kubectl dry-run

Next Steps

See coreweave-sdk-patterns for inference client patterns.