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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>
91 lines
2.9 KiB
YAML
91 lines
2.9 KiB
YAML
id: TC-INFERENCE-002
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name: Basic Inference
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suite: inference
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priority: 2
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timeout: 180000
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dependencies:
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- TC-INFERENCE-001
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steps:
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- name: Run simple math question
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command: docker exec ollama37 ollama run gemma3:4b "What is 2+2? Answer with just the number." 2>&1
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timeout: 120000
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- name: Check GPU memory usage
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command: docker exec ollama37 nvidia-smi --query-compute-apps=pid,used_memory --format=csv 2>/dev/null || echo "No GPU processes"
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- name: Check for inference errors in logs
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command: |
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cd docker
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LOGS=$(docker compose logs --since=5m 2>&1)
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echo "=== Inference Error Check ==="
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# Check for CUBLAS errors (critical for K80)
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if echo "$LOGS" | grep -qE "CUBLAS_STATUS_"; then
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echo "CRITICAL: CUBLAS error during inference:"
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echo "$LOGS" | grep -E "CUBLAS_STATUS_"
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exit 1
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fi
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# Check for CUDA errors
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if echo "$LOGS" | grep -qE "CUDA error"; then
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echo "CRITICAL: CUDA error during inference:"
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echo "$LOGS" | grep -E "CUDA error"
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exit 1
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fi
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# Check for compute graph errors
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if echo "$LOGS" | grep -qiE "(compute.*failed|graph.*error)"; then
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echo "ERROR: Compute graph error:"
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echo "$LOGS" | grep -iE "(compute.*failed|graph.*error)"
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exit 1
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fi
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echo "SUCCESS: No inference errors in logs"
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- name: Verify inference request in logs
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command: |
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cd docker
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LOGS=$(docker compose logs --since=5m 2>&1)
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echo "=== Inference Request Verification ==="
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# Check for generate API call
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if echo "$LOGS" | grep -qE '\[GIN\].*POST.*/api/generate'; then
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echo "SUCCESS: Generate API request logged"
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echo "$LOGS" | grep -E '\[GIN\].*POST.*/api/generate' | tail -2
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else
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echo "WARNING: Generate API request not found in recent logs"
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fi
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# Check for successful response (200 status)
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if echo "$LOGS" | grep -qE '\[GIN\].*200.*POST'; then
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echo "SUCCESS: Inference returned 200 status"
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else
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echo "WARNING: Could not verify 200 status"
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fi
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- name: Display recent CUDA activity from logs
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command: |
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cd docker
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LOGS=$(docker compose logs --since=5m 2>&1)
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echo "=== Recent CUDA Activity ==="
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echo "$LOGS" | grep -iE "(CUDA|cuda|device=CUDA)" | tail -5 || echo "No recent CUDA activity logged"
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criteria: |
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Basic inference should work on Tesla K80.
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Expected:
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- Model responds to the math question
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- Response should indicate "4" (accept variations: "4", "four", "The answer is 4", etc.)
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- GPU memory should be allocated during inference
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- NO CUBLAS_STATUS_ errors in logs (critical for K80 compatibility)
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- NO CUDA error messages in logs
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- Generate API request logged with 200 status
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This is AI-generated output - accept reasonable variations.
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Focus on the model producing a coherent response without GPU errors.
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