CoreWeave Hello World Skill

CoreWeave Hello World

Overview

Deploy your first GPU workload on CoreWeave: a simple inference service using vLLM or a batch CUDA job. CoreWeave runs Kubernetes on bare-metal GPU nodes with A100, H100, and L40 GPUs.

Prerequisites

Completed coreweave-install-auth setup
kubectl configured with CoreWeave kubeconfig
Namespace with GPU quota

Instructions

Step 1: Deploy a vLLM Inference Server

# vllm-inference.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: vllm-server
  template:
    metadata:
      labels:
        app: vllm-server
    spec:
      containers:
        - name: vllm
          image: vllm/vllm-openai:latest
          args:
            - "--model"
            - "meta-llama/Llama-3.1-8B-Instruct"
            - "--port"
            - "8000"
          ports:
            - containerPort: 8000
          resources:
            limits:
              nvidia.com/gpu: 1
              memory: 48Gi
              cpu: "8"
            requests:
              nvidia.com/gpu: 1
              memory: 32Gi
              cpu: "4"
          env:
            - name: HUGGING_FACE_HUB_TOKEN
              valueFrom:
                secretKeyRef:
                  name: hf-token
                  key: token
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                  - key: gpu.nvidia.com/class
                    operator: In
                    values: ["A100_PCIE_80GB"]
---
apiVersion: v1
kind: Service
metadata:
  name: vllm-server
spec:
  selector:
    app: vllm-server
  ports:
    - port: 8000
      targetPort: 8000
  type: ClusterIP

# Create HuggingFace token secret
kubectl create secret generic hf-token --from-literal=token="${HF_TOKEN}"

# Deploy
kubectl apply -f vllm-inference.yaml
kubectl get pods -w  # Wait for Running state

# Port-forward and test
kubectl port-forward svc/vllm-server 8000:8000 &
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-3.1-8B-Instruct", "messages": [{"role": "user", "content": "Hello!"}]}'

Step 2: Batch GPU Job

# gpu-batch-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: gpu-benchmark
spec:
  template:
    spec:
      restartPolicy: Never
      containers:
        - name: benchmark
          image: pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime
          command: ["python3", "-c"]
          args:
            - |
              import torch
              print(f"CUDA available: {torch.cuda.is_available()}")
              print(f"GPU: {torch.cuda.get_device_name(0)}")
              x = torch.randn(10000, 10000, device="cuda")
              y = torch.matmul(x, x)
              print(f"Matrix multiply result shape: {y.shape}")
              print("CoreWeave GPU test passed!")
          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 gpu-batch-job.yaml
kubectl logs job/gpu-benchmark --follow

Error Handling

| Error | Cause | Solution | |-------|-------|----------| | Pod stuck Pending | No GPU capacity | Try different GPU type or check quota | | nvidia-smi not found | Wrong base image | Use NVIDIA CUDA images | | OOMKilled | Insufficient GPU memory | Use larger GPU (80GB A100) | | Image pull error | Registry auth | Create imagePullSecret |

Resources

Next Steps

Proceed to coreweave-local-dev-loop for development workflow setup.

Agent Skills: CoreWeave Hello World

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Skill Files