Managing Coreweave

CoreWeave Management

Analyze CoreWeave GPU workloads, virtual servers, storage, and Kubernetes resources.

Phase 1: Discovery

#!/bin/bash
# CoreWeave uses standard Kubernetes API with their kubeconfig
export KUBECONFIG="${COREWEAVE_KUBECONFIG:-$HOME/.kube/coreweave}"

echo "=== Namespaces ==="
kubectl get namespaces -o json \
  | jq -r '.items[] | "\(.metadata.name)\t\(.status.phase)\t\(.metadata.creationTimestamp[0:10])"' \
  | column -t | head -15

echo ""
echo "=== GPU Workloads ==="
kubectl get pods --all-namespaces -o json \
  | jq -r '.items[] | select(.spec.containers[].resources.limits["nvidia.com/gpu"] != null) | "\(.metadata.namespace)\t\(.metadata.name[0:40])\t\(.status.phase)\t\(.spec.containers[0].resources.limits["nvidia.com/gpu"]) GPU(s)\t\(.spec.nodeName // "pending")"' \
  | column -t | head -20

echo ""
echo "=== Virtual Servers ==="
kubectl get virtualservers --all-namespaces -o json 2>/dev/null \
  | jq -r '.items[]? | "\(.metadata.namespace)\t\(.metadata.name)\t\(.status.state // "unknown")\t\(.spec.resources.gpu.type // "N/A")\t\(.spec.resources.gpu.count // 0) GPU(s)"' \
  | column -t | head -15

echo ""
echo "=== Persistent Volume Claims ==="
kubectl get pvc --all-namespaces -o json \
  | jq -r '.items[] | "\(.metadata.namespace)\t\(.metadata.name[0:30])\t\(.status.phase)\t\(.spec.resources.requests.storage)\t\(.spec.storageClassName)"' \
  | column -t | head -20

Phase 2: Analysis

#!/bin/bash
export KUBECONFIG="${COREWEAVE_KUBECONFIG:-$HOME/.kube/coreweave}"

echo "=== Node GPU Summary ==="
kubectl get nodes -o json \
  | jq -r '.items[] | select(.status.capacity["nvidia.com/gpu"] != null) | "\(.metadata.name[0:30])\t\(.metadata.labels["gpu.nvidia.com/class"] // "unknown")\tGPUs:\(.status.capacity["nvidia.com/gpu"])\tAllocatable:\(.status.allocatable["nvidia.com/gpu"])"' \
  | column -t | head -20

echo ""
echo "=== Deployments ==="
kubectl get deployments --all-namespaces -o json \
  | jq -r '.items[] | "\(.metadata.namespace)\t\(.metadata.name[0:30])\t\(.status.readyReplicas // 0)/\(.spec.replicas)\t\(.status.updatedReplicas // 0) updated"' \
  | column -t | head -15

echo ""
echo "=== Services & Endpoints ==="
kubectl get services --all-namespaces -o json \
  | jq -r '.items[] | select(.metadata.namespace != "kube-system") | "\(.metadata.namespace)\t\(.metadata.name[0:30])\t\(.spec.type)\t\(.spec.clusterIP)\t\(.status.loadBalancer.ingress[0].ip // "N/A")"' \
  | column -t | head -15

echo ""
echo "=== Resource Requests Summary ==="
kubectl get pods --all-namespaces -o json \
  | jq '{
    total_gpu_requests: [.items[].spec.containers[].resources.requests["nvidia.com/gpu"] // "0" | tonumber] | add,
    total_cpu_requests: [.items[].spec.containers[].resources.requests.cpu // "0" | gsub("m";"") | tonumber] | add,
    total_memory_requests_gi: ([.items[].spec.containers[].resources.requests.memory // "0" | gsub("Gi";"") | gsub("Mi";"") | tonumber] | add / 1024 | . * 10 | round / 10),
    pod_count: (.items | length)
  }'

echo ""
echo "=== InferenceService (KServe) ==="
kubectl get inferenceservices --all-namespaces -o json 2>/dev/null \
  | jq -r '.items[]? | "\(.metadata.namespace)\t\(.metadata.name[0:30])\t\(.status.conditions[-1].type // "unknown")\t\(.status.url // "N/A")"' \
  | column -t | head -10

echo ""
echo "=== Recent Events ==="
kubectl get events --all-namespaces --sort-by='.lastTimestamp' -o json \
  | jq -r '.items[-10:][] | "\(.metadata.namespace)\t\(.involvedObject.name[0:25])\t\(.type)\t\(.reason)\t\(.message[0:50])"' \
  | column -t

Output Format

COREWEAVE ANALYSIS
====================
Namespace        Workload           GPUs     Type      Status    Storage
──────────────────────────────────────────────────────────────────────────
ml-training      train-job-large    8xA100   Pod       Running   500Gi
inference        llm-server         4xA40    VS        Running   200Gi
dev              experiment-1       1xRTX    Pod       Running   50Gi

GPUs: 13 allocated (A100:8, A40:4, RTX:1)
Pods: 12 running | Virtual Servers: 2 | PVCs: 8 (750Gi total)
Services: 5 (2 LoadBalancer) | Namespaces: 4 active

Safety Rules

• Read-only: Only use kubectl get, describe, and logs commands
• Never create, delete, or scale workloads without confirmation
• Kubeconfig: Never output kubeconfig contents or tokens
• Secrets: Never read or output Kubernetes secret values

Anti-Hallucination Rules

1. NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
2. NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
3. NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
4. ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
5. ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations