ReddRadar
Agent skill
coreweave-local-dev-loop
Set up local development workflow for CoreWeave GPU deployments. Use when building containers locally, testing YAML manifests, or iterating on model serving configurations before deploying. Trigger with phrases like "coreweave dev setup", "coreweave local testing", "develop for coreweave", "coreweave container build".
Install this agent skill to your Project
npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/coreweave-pack/skills/coreweave-local-dev-loop
SKILL.md
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-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 |
Resources
Next Steps
See coreweave-sdk-patterns for inference client patterns.
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