multiplication-eval-deepseek-r1-distill-llama-70b.yaml

Kubernetes Job to evaluate how well a language model solves basic

multiplication problems using vLLM. The Job downloads and runs a Python script

from SCRIPT_URL, which:

- Downloads the specified Hugging Face model (MODEL_ID)

- Generates 1,000 random multiplication problems

- Queries the model for answers

- Computes and prints accuracy to stdout

apiVersion: batch/v1 kind: Job metadata: name: multiplication-eval-deepseek-r1-distill-llama-70b namespace: mk8s-docs-examples spec: template: spec: runtimeClassName: nvidia tolerations: - key: "nvidia.com/gpu" operator: "Equal" value: "true" effect: "NoSchedule" containers: - name: vllm-eval-runner image: vllm/vllm-openai:v0.9.1 command: ["/bin/bash"] args: - -c - | curl -L "$SCRIPT_URL" -o eval_multiplication.py &&
python3 eval_multiplication.py "$MODEL_ID" --stdout env: - name: MODEL_ID value: deepseek-ai/DeepSeek-R1-Distill-Llama-70B - name: SCRIPT_URL value: https://docs.lambda.ai/assets/code/eval_multiplication.py - name: HF_HOME value: /root/.cache/huggingface volumeMounts: - name: hf-cache mountPath: /root/.cache/huggingface - name: dev-shm mountPath: /dev/shm resources: limits: memory: "32Gi" nvidia.com/gpu: "4" volumes: - name: hf-cache persistentVolumeClaim: claimName: huggingface-cache - name: dev-shm emptyDir: medium: Memory sizeLimit: 1Gi restartPolicy: Never backoffLimit: 2 ttlSecondsAfterFinished: 300