---

name: coreweave-local-dev-loop description: '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".

' allowed-tools: Read, Write, Edit, Bash(kubectl:), Bash(docker:), Grep version: 1.11.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• gpu-cloud
• kubernetes
• inference
• coreweave compatibility: Designed for Claude Code

---

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

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

• CoreWeave CKS Docs
• kubectl dry-run

Next Steps

See coreweave-sdk-patterns for inference client patterns.