scripts/start_tei_service.sh

#!/bin/bash
#
# Start Hugging Face TEI service for Qwen3-Embedding-0.6B using Docker.
#
set -euo pipefail
PROJECT_ROOT="$(cd "$(dirname "$0")/.." && pwd)"
cd "${PROJECT_ROOT}"
# Load .env.
# shellcheck source=scripts/lib/load_env.sh
source "${PROJECT_ROOT}/scripts/lib/load_env.sh"
load_env_file "${PROJECT_ROOT}/.env"
if ! command -v docker >/dev/null 2>&1; then
  echo "ERROR: docker is required to run TEI service." >&2
  exit 1
fi
TEI_CONTAINER_NAME="${TEI_CONTAINER_NAME:-saas-search-tei}"
TEI_PORT="${TEI_PORT:-8080}"
TEI_MODEL_ID="${TEI_MODEL_ID:-Qwen/Qwen3-Embedding-0.6B}"
TEI_VERSION="${TEI_VERSION:-1.9}"
TEI_MAX_BATCH_TOKENS="${TEI_MAX_BATCH_TOKENS:-4096}"
TEI_MAX_CLIENT_BATCH_SIZE="${TEI_MAX_CLIENT_BATCH_SIZE:-24}"
TEI_DTYPE="${TEI_DTYPE:-float16}"
HF_CACHE_DIR="${HF_CACHE_DIR:-$HOME/.cache/huggingface}"
TEI_HEALTH_TIMEOUT_SEC="${TEI_HEALTH_TIMEOUT_SEC:-300}"
TEI_DEVICE_RAW="${TEI_DEVICE:-cuda}"
TEI_DEVICE="$(echo "${TEI_DEVICE_RAW}" | tr '[:upper:]' '[:lower:]')"
if [[ "${TEI_DEVICE}" != "cuda" && "${TEI_DEVICE}" != "cpu" ]]; then
  echo "ERROR: invalid TEI_DEVICE=${TEI_DEVICE_RAW}. Use cuda/cpu." >&2
  exit 1
fi
detect_gpu_tei_image() {
  # Prefer turing image for pre-Ampere GPUs (e.g. Tesla T4, compute capability 7.5).
  local compute_cap major
  compute_cap="$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null | head -n1 || true)"
  major="${compute_cap%%.*}"
  if [[ -n "${major}" && "${major}" -lt 8 ]]; then
    echo "ghcr.io/huggingface/text-embeddings-inference:turing-${TEI_VERSION}"
  else
    echo "ghcr.io/huggingface/text-embeddings-inference:cuda-${TEI_VERSION}"
  fi
}
if [[ "${TEI_DEVICE}" == "cuda" ]]; then
  if ! command -v nvidia-smi >/dev/null 2>&1 || ! nvidia-smi >/dev/null 2>&1; then
    echo "ERROR: TEI_DEVICE=cuda but NVIDIA GPU is not available. No CPU fallback." >&2
    exit 1
  fi
  if ! docker info --format '{{json .Runtimes}}' 2>/dev/null | grep -q 'nvidia'; then
    echo "ERROR: TEI_DEVICE=cuda but Docker nvidia runtime is not configured." >&2
    echo "Install and configure nvidia-container-toolkit, then restart Docker." >&2
    exit 1
  fi
  TEI_IMAGE="${TEI_IMAGE:-$(detect_gpu_tei_image)}"
  GPU_ARGS=(--gpus all)
  TEI_MODE="cuda"
else
  TEI_IMAGE="${TEI_IMAGE:-ghcr.io/huggingface/text-embeddings-inference:${TEI_VERSION}}"
  GPU_ARGS=()
  TEI_MODE="cpu"
fi
mkdir -p "${HF_CACHE_DIR}"
existing_id="$(docker ps -aq -f name=^/${TEI_CONTAINER_NAME}$)"
if [[ -n "${existing_id}" ]]; then
  running_id="$(docker ps -q -f name=^/${TEI_CONTAINER_NAME}$)"
  if [[ -n "${running_id}" ]]; then
    current_image="$(docker inspect "${TEI_CONTAINER_NAME}" --format '{{.Config.Image}}' 2>/dev/null || true)"
    device_req="$(docker inspect "${TEI_CONTAINER_NAME}" --format '{{json .HostConfig.DeviceRequests}}' 2>/dev/null || true)"
    current_is_gpu_image=0
    if [[ "${current_image}" == *":cuda-"* || "${current_image}" == *":turing-"* ]]; then
      current_is_gpu_image=1
    fi
    if [[ "${TEI_DEVICE}" == "cuda" ]]; then
      if [[ "${current_is_gpu_image}" -eq 1 ]] && [[ "${device_req}" != "null" ]] && [[ "${current_image}" == "${TEI_IMAGE}" ]]; then
        echo "TEI already running (CUDA): ${TEI_CONTAINER_NAME}"
        exit 0
      fi
      echo "TEI running with different mode/image; recreating container ${TEI_CONTAINER_NAME}"
      echo "  current_image=${current_image:-unknown}"
      echo "  target_image=${TEI_IMAGE}"
      docker rm -f "${TEI_CONTAINER_NAME}" >/dev/null 2>&1 || true
    else
      if [[ "${current_is_gpu_image}" -eq 0 ]] && [[ "${device_req}" == "null" ]] && [[ "${current_image}" == "${TEI_IMAGE}" ]]; then
        echo "TEI already running (CPU): ${TEI_CONTAINER_NAME}"
        exit 0
      fi
      echo "TEI running with different mode/image; recreating container ${TEI_CONTAINER_NAME}"
      echo "  current_image=${current_image:-unknown}"
      echo "  target_image=${TEI_IMAGE}"
      docker rm -f "${TEI_CONTAINER_NAME}" >/dev/null 2>&1 || true
    fi
  fi
  if docker ps -aq -f name=^/${TEI_CONTAINER_NAME}$ | grep -q .; then
    docker rm "${TEI_CONTAINER_NAME}" >/dev/null
  fi
fi
echo "Starting TEI container: ${TEI_CONTAINER_NAME}"
echo "Image: ${TEI_IMAGE}"
echo "Model: ${TEI_MODEL_ID}"
echo "Port: ${TEI_PORT}"
echo "Mode: ${TEI_MODE}"
docker run -d \
  --name "${TEI_CONTAINER_NAME}" \
  -p "${TEI_PORT}:80" \
  "${GPU_ARGS[@]}" \
  -v "${HF_CACHE_DIR}:/data" \
  -e HF_TOKEN="${HF_TOKEN:-}" \
  "${TEI_IMAGE}" \
  --model-id "${TEI_MODEL_ID}" \
  --dtype "${TEI_DTYPE}" \
  --max-batch-tokens "${TEI_MAX_BATCH_TOKENS}" \
  --max-client-batch-size "${TEI_MAX_CLIENT_BATCH_SIZE}" >/dev/null
echo "Waiting for TEI health..."
for i in $(seq 1 "${TEI_HEALTH_TIMEOUT_SEC}"); do
  if curl -sf "http://127.0.0.1:${TEI_PORT}/health" >/dev/null 2>&1; then
    echo "TEI health is ready: http://127.0.0.1:${TEI_PORT}"
    break
  fi
  sleep 1
  if [[ "${i}" == "${TEI_HEALTH_TIMEOUT_SEC}" ]]; then
    echo "ERROR: TEI failed to become healthy in time." >&2
    docker logs --tail 100 "${TEI_CONTAINER_NAME}" >&2 || true
    exit 1
  fi
done
echo "Running TEI output probe..."
for probe_idx in 1 2; do
  probe_resp="$(curl -sf -X POST "http://127.0.0.1:${TEI_PORT}/embed" \
    -H "Content-Type: application/json" \
    -d '{"inputs":["health check","芭比娃娃 儿童玩具"]}' || true)"
  if [[ -z "${probe_resp}" ]]; then
    echo "ERROR: TEI probe ${probe_idx} failed: empty response" >&2
    docker logs --tail 120 "${TEI_CONTAINER_NAME}" >&2 || true
    docker rm -f "${TEI_CONTAINER_NAME}" >/dev/null 2>&1 || true
    exit 1
  fi
  # Detect non-finite-like payloads (observed as null/NaN on incompatible CUDA image + GPU).
  if echo "${probe_resp}" | rg -qi '(null|nan|inf)'; then
    echo "ERROR: TEI probe ${probe_idx} detected invalid embedding values (null/NaN/Inf)." >&2
    echo "Response preview: $(echo "${probe_resp}" | head -c 220)" >&2
    docker logs --tail 120 "${TEI_CONTAINER_NAME}" >&2 || true
    docker rm -f "${TEI_CONTAINER_NAME}" >/dev/null 2>&1 || true
    exit 1
  fi
done
echo "TEI is ready and output probe passed: http://127.0.0.1:${TEI_PORT}"
exit 0