--- name: coreweave-hello-world-e361a6b1b110 description: 'Deploy a GPU workload on CoreWeave with kubectl. Use when running your first GPU job, testing inference, or verifying CoreWeave cluster access. Trigger with phrases like "coreweave hello world", "coreweave first deploy", "coreweave gpu test", "run on coreweave". ' allowed-tools: Read, Write, Edit, Bash(kubectl:*) version: 1.11.0 license: MIT author: Jeremy Longshore tags: - saas - gpu-cloud - kubernetes - inference - coreweave compatibility: Designed for Claude Code --- # CoreWeave Hello World > **Community-contributed.** Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc. ## 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 ```yaml # 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 ``` ```bash # 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 ```yaml # 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"] ``` ```bash 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 | ## Output - A minimal, namespace-scoped GPU workload or inference endpoint with one declared GPU. - A visible readiness signal and a bounded smoke-test response that confirms the GPU runtime path without placing a model token or customer prompt in source control. ## Examples Validate the batch path first because it is cheaper and easier to roll back than a public endpoint: ```bash kubectl -n sandbox apply -f gpu-batch-job.yaml kubectl -n sandbox wait --for=condition=complete job/gpu-benchmark --timeout=15m kubectl -n sandbox logs job/gpu-benchmark ``` Delete the smoke job after recording its redacted result. If it remains Pending, inspect its events and namespace quota—do not broaden cluster permissions or embed registry credentials in the manifest to force it through. ## Resources - [CoreWeave GPU Instances](https://docs.coreweave.com/docs/platform/instances/gpu-instances) - [Deploy vLLM](https://docs.coreweave.com/docs/products/cks/tutorials/deploy-vllm-inference) - [CoreWeave Examples](https://github.com/coreweave/kubernetes-cloud) ## Next Steps Proceed to `coreweave-local-dev-loop` for development workflow setup. --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/coreweave-pack/skills/coreweave-hello-world/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Jeremy Longshore Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/coreweave-pack/skills/coreweave-hello-world/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Backend Developer, DevOps / SRE, ML / AI Engineer Specialties: Backend, DevOps Tools: Kubernetes, Coreweave Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 0f1d871e256647baac6c0e5e45c2c0c6574e8fac001b7c6d4215461ea39e9f3a