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Coreweave multi env setup

Skill jeremylongshore/claude-code-plugins-plus-skills/skills/.curated/coreweave-multi-env-setup

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Install
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill coreweave-multi-env-setup

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What its author says it does

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'Configure CoreWeave across development, staging, and production environments.

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SKILL.md

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CoreWeave Multi-Environment Setup

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

Overview

CoreWeave GPU cloud requires strict environment separation to control infrastructure costs and prevent resource contention. Each environment maps to an isolated Kubernetes namespace with its own GPU quota, scaling policy, and access controls. Development uses cheaper GPU tiers for iteration speed, staging mirrors production GPU types for accurate benchmarking, and production runs full-scale with no scale-to-zero to guarantee inference latency SLAs.

Environment Configuration

const coreweaveConfig = (env: string) => ({
  development: {
    namespace: "app-dev", apiEndpoint: process.env.CW_API_ENDPOINT_DEV!,
    token: process.env.CW_TOKEN_DEV!, gpuType: "L40", scaleToZero: true, replicas: [0, 1],
  },
  staging: {
    namespace: "app-staging", apiEndpoint: process.env.CW_API_ENDPOINT_STG!,
    token: process.env.CW_TOKEN_STG!, gpuType: "A100_PCIE_40GB", scaleToZero: true, replicas: [0, 2],
  },
  production: {
    namespace: "app-prod", apiEndpoint: process.env.CW_API_ENDPOINT_PROD!,
    token: process.env.CW_TOKEN_PROD!, gpuType: "A100_PCIE_80GB", scaleToZero: false, replicas: [2, 10],
  },
}[env]);

Environment Files

# Per-env files: .env.development, .env.staging, .env.production
CW_API_ENDPOINT_{DEV|STG|PROD}=https://k8s.{ord1|ord1|las1}.coreweave.com
CW_TOKEN_{DEV|STG|PROD}=<service-account-token>
CW_NAMESPACE={app-dev|app-staging|app-prod}
CW_GPU_TYPE={L40|A100_PCIE_40GB|A100_PCIE_80GB}

Environment Validation

function validateCoreWeaveEnv(env: string): void {
  const required = ["CW_API_ENDPOINT", "CW_TOKEN", "CW_NAMESPACE", "CW_GPU_TYPE"];
  const suffix = { development: "_DEV", staging: "_STG", production: "_PROD" }[env];
  const missing = required
    .map((k) => (k.includes("NAMESPACE") ? k : `${k}${suffix}`))
    .filter((k) => !process.env[k]);
  if (missing.length) throw new Error(`Missing env vars for ${env}: ${missing.join(", ")}`);
}

Promotion Workflow

# 1. Validate model in dev namespace
kubectl -n app-dev get inferenceservice my-model -o jsonpath='{.status.conditions}'

# 2. Apply staging overlay with production GPU type
kustomize build k8s/overlays/staging | kubectl apply -f -

# 3. Run inference benchmarks against staging endpoint
curl -X POST https://staging.myapp.coreweave.cloud/v1/predict -d @test-payload.json

# 4. Promote to production (blue-green via namespace switch)
kustomize build k8s/overlays/prod | kubectl apply -f -
kubectl -n app-prod rollout status deployment/my-model

Environment Matrix

SettingDevStagingProd
GPU TypeL40A100 40GBA100 80GB
Scale-to-ZeroYesYesNo
Replicas0-10-22-10
Namespaceapp-devapp-stagingapp-prod
Regionord1ord1las1
Spot InstancesYesNoNo

Error Handling

IssueCauseFix
GPU quota exceededNamespace limit reachedRequest quota increase via CW support portal
Pod stuck PendingGPU type unavailable in regionCheck kubectl describe node for capacity; switch region
Scale-to-zero not wakingHPA misconfiguredVerify minReplicas: 0 and KEDA scaler settings
Namespace access deniedRBAC not applied to overlayApply RoleBinding in kustomize overlay

Resources

Next Steps

See coreweave-deploy-integration.

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