# Save kubeconfig
mkdir -p ~/.kube
cp ~/Downloads/coreweave-kubeconfig.yaml ~/.kube/coreweave

# Set as active context
export KUBECONFIG=~/.kube/coreweave

# Verify connection
kubectl get nodes
kubectl get namespaces

# CoreWeave API token for programmatic access
export COREWEAVE_API_TOKEN="your-api-token"

# Store securely
echo "COREWEAVE_API_TOKEN=${COREWEAVE_API_TOKEN}" >> .env
echo "KUBECONFIG=~/.kube/coreweave" >> .env

# List available GPU nodes
kubectl get nodes -l gpu.nvidia.com/class -o custom-columns=\
NAME:.metadata.name,GPU:.metadata.labels.gpu\.nvidia\.com/class,\
STATUS:.status.conditions[-1].type

# Check GPU allocatable resources
kubectl describe nodes | grep -A5 "Allocatable:" | grep nvidia

# test-gpu.yaml
apiVersion: v1
kind: Pod
metadata:
  name: gpu-test
spec:
  restartPolicy: Never
  containers:
    - name: cuda-test
      image: nvidia/cuda:12.2.0-base-ubuntu22.04
      command: ["nvidia-smi"]
      resources:
        limits:
          nvidia.com/gpu: 1
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
          - matchExpressions:
              - key: gpu.nvidia.com/class
                operator: In
                values: ["A100_PCIE_80GB"]

kubectl apply -f test-gpu.yaml
kubectl logs gpu-test  # Should show nvidia-smi output
kubectl delete pod gpu-test