File size: 2,154 Bytes
b9a10ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | #!/usr/bin/env bash
# Smoke test the AMD GPU droplet (vLLM + riprap-models).
# Usage: bash scripts/smoke_test_gpu.sh <ip> <token>
set -euo pipefail
IP="${1:?Usage: smoke_test_gpu.sh <ip> <token>}"
TOKEN="${2:?Usage: smoke_test_gpu.sh <ip> <token>}"
VLLM_URL="http://${IP}:8001"
ML_URL="http://${IP}:7860"
PASS=0
FAIL=0
check() {
local label="$1"; shift
local status
if status=$(eval "$@" 2>&1); then
echo " PASS $label"
PASS=$((PASS+1))
else
echo " FAIL $label"
echo " $status"
FAIL=$((FAIL+1))
fi
}
echo "=== Smoke test: $IP ==="
echo ""
echo "--- vLLM (port 8001) ---"
check "vLLM /v1/models" \
"curl -sf -H 'Authorization: Bearer $TOKEN' $VLLM_URL/v1/models | python3 -c 'import sys,json; d=json.load(sys.stdin); assert len(d[\"data\"]) > 0'"
check "vLLM /v1/chat/completions" \
"curl -sf -H 'Authorization: Bearer $TOKEN' -H 'Content-Type: application/json' \
-d '{\"model\":\"granite-4.1-8b\",\"messages\":[{\"role\":\"user\",\"content\":\"ping\"}],\"max_tokens\":5}' \
$VLLM_URL/v1/chat/completions | python3 -c 'import sys,json; d=json.load(sys.stdin); assert d[\"choices\"][0][\"message\"][\"content\"]'"
echo ""
echo "--- riprap-models (port 7860) ---"
check "riprap-models /healthz" \
"curl -sf $ML_URL/healthz | python3 -c 'import sys,json; d=json.load(sys.stdin); assert d.get(\"ok\") == True'"
check "riprap-models /v1/granite-embed" \
"curl -sf -H 'Authorization: Bearer $TOKEN' -H 'Content-Type: application/json' \
-d '{\"texts\":[\"flood risk in NYC\"]}' \
$ML_URL/v1/granite-embed | python3 -c 'import sys,json; d=json.load(sys.stdin); assert d.get(\"ok\") and len(d[\"vectors\"]) == 1 and len(d[\"vectors\"][0]) > 0'"
check "riprap-models /v1/gliner-extract" \
"curl -sf -H 'Authorization: Bearer $TOKEN' -H 'Content-Type: application/json' \
-d '{\"text\":\"Hurricane Sandy flooded 80 Pioneer Street in Red Hook Brooklyn.\",\"labels\":[\"location\",\"event\"]}' \
$ML_URL/v1/gliner-extract | python3 -c 'import sys,json; d=json.load(sys.stdin); assert \"entities\" in d'"
echo ""
echo "=== Results: ${PASS} PASS, ${FAIL} FAIL ==="
[ "$FAIL" -eq 0 ]
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