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- TC-INFERENCE-003: Add unload step for gemma3:4b at end - TC-INFERENCE-004: Remove redundant 4b unload at start - TC-INFERENCE-005: Remove redundant 12b unload at start Each model size test now handles its own VRAM cleanup. Workflow-level unload remains as safety fallback for failures. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
114 lines
3.5 KiB
YAML
114 lines
3.5 KiB
YAML
id: TC-INFERENCE-004
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name: Medium Model (12b) Inference
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suite: inference
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priority: 4
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timeout: 600000
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dependencies:
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- TC-INFERENCE-003
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steps:
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- name: Check if gemma3:12b model exists
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command: docker exec ollama37 ollama list | grep -q "gemma3:12b" && echo "Model exists" || echo "Model not found"
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- name: Pull gemma3:12b model if needed
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command: docker exec ollama37 ollama list | grep -q "gemma3:12b" || docker exec ollama37 ollama pull gemma3:12b
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timeout: 900000
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- name: Verify model available
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command: docker exec ollama37 ollama list | grep gemma3:12b
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- name: Warmup model (preload into GPU)
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command: |
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curl -s http://localhost:11434/api/generate \
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-d '{"model":"gemma3:12b","prompt":"hi","stream":false}' \
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| jq -r '.response' | head -c 100
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timeout: 300000
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- name: Verify model loaded to GPU
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command: |
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cd docker
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LOGS=$(docker compose logs --since=5m 2>&1)
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echo "=== Model Loading Check for gemma3:12b ==="
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# Check for layer offloading to GPU
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if echo "$LOGS" | grep -q "offloaded.*layers to GPU"; then
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echo "SUCCESS: Model layers offloaded to GPU"
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echo "$LOGS" | grep "offloaded.*layers to GPU" | tail -1
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else
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echo "ERROR: Model layers not offloaded to GPU"
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exit 1
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fi
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# Check llama runner started
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if echo "$LOGS" | grep -q "llama runner started"; then
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echo "SUCCESS: Llama runner started"
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else
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echo "ERROR: Llama runner not started"
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exit 1
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fi
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- name: Run inference test
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command: docker exec ollama37 ollama run gemma3:12b "What is the capital of France? Answer in one word." 2>&1
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timeout: 180000
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- name: Check GPU memory usage
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command: |
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echo "=== GPU Memory Usage ==="
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docker exec ollama37 nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv
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echo ""
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echo "=== GPU Processes ==="
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docker exec ollama37 nvidia-smi --query-compute-apps=pid,used_memory --format=csv 2>/dev/null || echo "No GPU processes listed"
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- name: Check for inference errors
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command: |
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cd docker
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LOGS=$(docker compose logs --since=5m 2>&1)
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echo "=== Inference Error Check ==="
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if echo "$LOGS" | grep -qE "CUBLAS_STATUS_"; then
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echo "CRITICAL: CUBLAS error during inference:"
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echo "$LOGS" | grep -E "CUBLAS_STATUS_"
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exit 1
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fi
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if echo "$LOGS" | grep -qE "CUDA error"; then
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echo "CRITICAL: CUDA error during inference:"
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echo "$LOGS" | grep -E "CUDA error"
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exit 1
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fi
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if echo "$LOGS" | grep -qi "out of memory"; then
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echo "ERROR: Out of memory"
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echo "$LOGS" | grep -i "out of memory"
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exit 1
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fi
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echo "SUCCESS: No inference errors"
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- name: Unload model after test
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command: |
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echo "Unloading gemma3:12b from VRAM..."
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curl -s http://localhost:11434/api/generate -d '{"model":"gemma3:12b","keep_alive":0}' || true
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sleep 2
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echo "Model unloaded"
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criteria: |
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The gemma3:12b model should run inference on Tesla K80.
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Expected:
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- Model downloads successfully (~8GB)
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- Model loads into GPU (single GPU should be sufficient)
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- Logs show "offloaded X/Y layers to GPU"
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- Logs show "llama runner started"
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- Inference returns a response mentioning "Paris"
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- NO CUBLAS_STATUS_ errors
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- NO CUDA errors
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- NO out of memory errors
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- GPU memory shows allocation (~10GB)
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This is a medium-sized model that should fit in a single K80 GPU.
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Accept any reasonable answer about France's capital.
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