coreweave-local-dev-loop

# 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 ```bash # 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 ```bash # 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 ```bash kubectl apply -f ...