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143e6fa8e4
- Rename step to "Verify UVM device files" for clarity - Add "WARNING:" prefix when UVM device is missing - Add "SUCCESS:" prefix when device is present - Add confirmation message after UVM fix is applied - Separate ls command for cleaner output 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
48 lines
1.5 KiB
YAML
48 lines
1.5 KiB
YAML
id: TC-RUNTIME-002
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name: GPU Detection
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suite: runtime
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priority: 2
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timeout: 120000
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dependencies:
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- TC-RUNTIME-001
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steps:
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- name: Check nvidia-smi inside container
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command: docker exec ollama37 nvidia-smi
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- name: Check CUDA libraries
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command: docker exec ollama37 ldconfig -p | grep -i cuda | head -5
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- name: Verify UVM device files
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command: |
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if [ ! -e /dev/nvidia-uvm ]; then
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echo "WARNING: UVM device missing, creating with nvidia-modprobe..."
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sudo nvidia-modprobe -u -c=0
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echo "Restarting container to pick up UVM devices..."
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cd docker && docker compose restart
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sleep 15
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echo "UVM device fix applied"
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else
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echo "SUCCESS: UVM device file present"
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ls -l /dev/nvidia-uvm
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fi
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- name: Check Ollama GPU detection in logs
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command: |
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cd docker && docker compose logs 2>&1 | grep -E "(inference compute|GPU detected)" | tail -5
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criteria: |
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Tesla K80 GPU should be detected by both nvidia-smi AND Ollama CUDA runtime.
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Expected:
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- nvidia-smi shows Tesla K80 GPU(s) with Driver 470.x
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- CUDA libraries are available (libcuda, libcublas, etc.)
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- /dev/nvidia-uvm device file exists (required for CUDA runtime)
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- Ollama logs show GPU detection, NOT "id=cpu library=cpu"
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NOTE: If nvidia-smi works but Ollama shows only CPU, the UVM device
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files are missing. The test will auto-fix with nvidia-modprobe -u -c=0.
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The K80 has 12GB VRAM per GPU. Accept variations in reported memory.
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