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1a185f7926
Test case enhancements: - TC-RUNTIME-001: Add startup log error checking (CUDA, CUBLAS, CPU fallback) - TC-RUNTIME-002: Add GPU detection verification, CUDA init checks, error detection - TC-RUNTIME-003: Add server listening verification, runtime error checks - TC-INFERENCE-001: Add model loading logs, layer offload verification - TC-INFERENCE-002: Add inference error checking (CUBLAS/CUDA errors) - TC-INFERENCE-003: Add API request log verification, response time display Workflow enhancements: - Add judge_mode input (simple/llm/dual) to all workflows - Add judge_model input to specify LLM model for judging - Configurable via GitHub Actions UI without code changes 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
122 lines
3.7 KiB
YAML
122 lines
3.7 KiB
YAML
id: TC-INFERENCE-001
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name: Model Pull
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suite: inference
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priority: 1
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timeout: 600000
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dependencies:
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- TC-RUNTIME-003
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steps:
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- name: Check if model exists
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command: docker exec ollama37 ollama list | grep -q "gemma3:4b" && echo "Model exists" || echo "Model not found"
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- name: Pull model if needed
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command: docker exec ollama37 ollama list | grep -q "gemma3:4b" || docker exec ollama37 ollama pull gemma3:4b
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timeout: 600000
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- name: Verify model available
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command: docker exec ollama37 ollama list
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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":"gemma3:4b","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 loading in logs
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command: |
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cd docker
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LOGS=$(docker compose logs 2>&1)
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echo "=== Model Loading Check ==="
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# Check for model loading message
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if echo "$LOGS" | grep -q 'msg="loading model"'; then
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echo "SUCCESS: Model loading initiated"
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echo "$LOGS" | grep 'msg="loading model"' | tail -1
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else
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echo "WARNING: Model loading message not found"
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fi
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# Check for layer offloading to GPU
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if echo "$LOGS" | grep -q "offloaded.*layers to GPU"; then
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echo "SUCCESS: Model layers offloaded to GPU"
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echo "$LOGS" | grep "offloaded.*layers to GPU" | tail -1
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else
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echo "ERROR: Model layers not offloaded to GPU"
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exit 1
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fi
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# Check model weights loaded
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if echo "$LOGS" | grep -q 'msg="model weights loaded successfully"'; then
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echo "SUCCESS: Model weights loaded"
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else
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echo "WARNING: Model weights loaded message not found"
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fi
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- name: Verify llama runner started
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command: |
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cd docker
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LOGS=$(docker compose logs 2>&1)
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echo "=== Llama Runner Check ==="
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# Check llama runner started
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if echo "$LOGS" | grep -q "llama runner started"; then
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echo "SUCCESS: Llama runner started"
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echo "$LOGS" | grep "llama runner started" | tail -1
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else
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echo "ERROR: Llama runner not started"
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exit 1
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fi
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- name: Check for model loading errors
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command: |
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cd docker
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LOGS=$(docker compose logs 2>&1)
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echo "=== Model Loading Error Check ==="
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# Check for CUDA/CUBLAS errors during model load
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if echo "$LOGS" | grep -qE "(CUBLAS_STATUS_|CUDA error|cudaMalloc failed)"; then
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echo "CRITICAL CUDA ERRORS during model load:"
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echo "$LOGS" | grep -E "(CUBLAS_STATUS_|CUDA error|cudaMalloc failed)"
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exit 1
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fi
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# Check for out of memory
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if echo "$LOGS" | grep -qi "out of memory"; then
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echo "ERROR: Out of memory during model load"
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echo "$LOGS" | grep -i "out of memory"
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exit 1
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fi
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echo "SUCCESS: No model loading errors"
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- name: Display model memory allocation from logs
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command: |
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cd docker
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LOGS=$(docker compose logs 2>&1)
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echo "=== Model Memory Allocation ==="
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echo "$LOGS" | grep -E '(model weights|kv cache|compute graph|total memory).*device=' | tail -8
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criteria: |
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The gemma3:4b model should be available for inference.
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Expected:
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- Model is either already present or successfully downloaded
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- "ollama list" shows gemma3:4b in the output
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- No download errors
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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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- Logs show model weights on CUDA device (not CPU only)
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- NO CUBLAS_STATUS_ errors during model load
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- NO out of memory errors
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Accept if model already exists (skip download).
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Model size is ~3GB, download may take time.
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First inference loads model into VRAM - subsequent inferences will be fast.
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