通过Open WebUI将自建vLLM接入VS Code（Cline）！无Token开发

#!/bin/bash
set -euo pipefail

# ==============================================================================
# 02_ec2_userdata.sh
# EC2 启动时执行的 UserData 脚本
#
# 前提:
#   - AMI: Ubuntu 22.04 Deep Learning AMI (已安装 NVIDIA 驱动 & Docker)
#   - 实例类型: g6.xlarge (NVIDIA L4 GPU: 24GB VRAM, 250GB NVMe SSD)
#   - IAM 角色: 已附加 S3(ReadOnly/FullAccess) 与 ECR(ReadOnly) 权限
# ==============================================================================

LOG_FILE="/var/log/userdata-vllm.log"
exec > >(tee -a "${LOG_FILE}") 2>&1
echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] UserData 执行开始 ==="

# 配置参数
AWS_REGION="${AWS_REGION:-ap-northeast-1}"
S3_BUCKET_NAME="${S3_BUCKET_NAME:-my-llm-models-tokyo}"
HF_MODEL_ID="${HF_MODEL_ID:-Qwen/Qwen2.5-Coder-7B-Instruct}"
SERVED_MODEL_NAME="${SERVED_MODEL_NAME:-Qwen/Qwen2.5-Coder-7B-Instruct}"
VLLM_PORT="8000"
GPU_MEMORY_UTILIZATION="0.90"
MAX_MODEL_LEN="16384"
DOCKER_IMAGE="${DOCKER_IMAGE:-vllm/vllm-openai:latest}"

echo "获取模型(HF) : ${HF_MODEL_ID}"
echo "公开模型名   : ${SERVED_MODEL_NAME}"
echo "S3 桶       : s3://${S3_BUCKET_NAME}"

# 1. 检测并使用本地 NVMe 实例存储（250GB）
# ※ AWS Deep Learning AMI (DLAMI) 会在启动时自动将本地 NVMe 挂载到 /opt/dlami/nvme
NVME_DIR="/opt/dlami/nvme"
if mountpoint -q "${NVME_DIR}" || [ -d "${NVME_DIR}" ]; then
    echo "检测到 DLAMI 默认的 NVMe 挂载 (${NVME_DIR})，将其用作模型 & Docker 存储..."
    mkdir -p "${NVME_DIR}/models" "${NVME_DIR}/docker"
    mkdir -p /data
    ln -sfn "${NVME_DIR}/models" /data/models
else
    # 非 DLAMI 或未挂载时的回退处理
    NVME_DEV=$(lsblk -d -n -o NAME,SIZE | grep -E '250G|232G' | head -n1 | awk '{print $1}')
    if [ -n "${NVME_DEV}" ]; then
        echo "检测到本地 NVMe SSD (/dev/${NVME_DEV})，将挂载到 /data..."
        mkfs.ext4 -F "/dev/${NVME_DEV}" || true
        mkdir -p /data
        mount -o noatime "/dev/${NVME_DEV}" /data || true
    else
        mkdir -p /data
    fi
    mkdir -p /data/models /data/docker
    NVME_DIR="/data"
fi

LOCAL_MODEL_ROOT="/data/models"
LOCAL_MODEL_DIR="${LOCAL_MODEL_ROOT}/${HF_MODEL_ID}"
mkdir -p "${LOCAL_MODEL_DIR}"
chmod 777 "${LOCAL_MODEL_ROOT}"

# 2. 将 Docker & containerd 数据目录调整到 NVMe，以防 EBS 空间耗尽 & 加快层展开
echo "停止 Docker/containerd 并设置 NVMe 绑定挂载..."
systemctl stop docker containerd || true

mkdir -p "${NVME_DIR}/docker" "${NVME_DIR}/containerd"
mkdir -p /var/lib/docker /var/lib/containerd

# 若存在旧数据则迁移
cp -a /var/lib/docker/* "${NVME_DIR}/docker/" 2>/dev/null || true
cp -a /var/lib/containerd/* "${NVME_DIR}/containerd/" 2>/dev/null || true

mount --bind "${NVME_DIR}/docker" /var/lib/docker
mount --bind "${NVME_DIR}/containerd" /var/lib/containerd

if ! grep -q "/var/lib/docker" /etc/fstab; then
    echo "${NVME_DIR}/docker /var/lib/docker none defaults,bind 0 0" >> /etc/fstab
fi
if ! grep -q "/var/lib/containerd" /etc/fstab; then
    echo "${NVME_DIR}/containerd /var/lib/containerd none defaults,bind 0 0" >> /etc/fstab
fi

mkdir -p /etc/docker
cat <<EOF > /etc/docker/daemon.json
{
  "data-root": "/var/lib/docker"
}
EOF

systemctl daemon-reload
systemctl start containerd
systemctl start docker

# 3. 模型数据准备 (若 S3 存在则高速同步，否则 EC2 上从 Hugging Face 下载并备份至 S3)
echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] 开始检查模型数据... ==="
S3_SRC="s3://${S3_BUCKET_NAME}/models/${HF_MODEL_ID}"
echo "S3 目标: ${S3_SRC}/ (区域: ${AWS_REGION})"
aws configure set default.s3.max_concurrent_requests 20

# 等待 IAM 凭证及 S3 通信 (启动时元数据服务更新 STS token 可能需要几秒)
echo "检查 IAM 认证与 S3 连接..."
for i in {1..15}; do
    if aws s3 ls "s3://${S3_BUCKET_NAME}" --region "${AWS_REGION}" >/dev/null 2>&1; then
        echo "已确认可访问 S3 桶。"
        break
    fi
    echo "等待 S3 连接/IAM 认证 ($i/15)..."
    sleep 2
done

HAS_S3_MODEL=false
echo "检查 S3 上模型是否存在: aws s3 ls ${S3_SRC}/ --region ${AWS_REGION}"
S3_CHECK=$(aws s3 ls "${S3_SRC}/" --region "${AWS_REGION}" 2>/dev/null || true)
echo "S3 检查结果:"
echo "${S3_CHECK}"

if echo "${S3_CHECK}" | grep -E '(\.safetensors|\.bin|\.json)' >/dev/null; then
    HAS_S3_MODEL=true
fi

if [ "${HAS_S3_MODEL}" = "true" ]; then
    echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] 在 S3 上发现模型，从 S3 高速同步... ==="
    aws s3 sync "${S3_SRC}" "${LOCAL_MODEL_DIR}" \
        --region "${AWS_REGION}" \
        --no-progress
else
    echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] S3 上未发现模型，直接从 Hugging Face 下载... ==="
    python3 -m pip install -U "huggingface_hub[cli]" || pip3 install -U "huggingface_hub[cli]" || true
    
    echo "从 Hugging Face ('${HF_MODEL_ID}') 下载模型..."
    python3 -c "
import sys
from huggingface_hub import snapshot_download
try:
    snapshot_download(repo_id='${HF_MODEL_ID}', local_dir='${LOCAL_MODEL_DIR}', local_dir_use_symlinks=False)
    print('成功从 Hugging Face 下载模型。')
except Exception as e:
    print(f'下载错误: {e}', file=sys.stderr)
    sys.exit(1)
"
    echo "下载完成，占用空间:"
    du -sh "${LOCAL_MODEL_DIR}"
fi

echo "模型准备完毕，本地磁盘使用情况:"
du -sh "${LOCAL_MODEL_DIR}"

# 4. 启动 vLLM 容器
echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] 启动 vLLM 容器 ==="
CONTAINER_NAME="vllm-server"

# 若使用 ECR 镜像，则先登录
if [[ "${DOCKER_IMAGE}" == *".dkr.ecr."* ]]; then
    echo "检测到 ECR 镜像，执行登录认证..."
    ECR_REGISTRY=$(echo "${DOCKER_IMAGE}" | cut -d'/' -f1)
    aws ecr get-login-password --region "${AWS_REGION}" | docker login --username AWS --password-stdin "${ECR_REGISTRY}" || true
fi

# 若已有同名容器则停止并移除
if docker ps -a --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then
    echo "停止并移除现有容器 ${CONTAINER_NAME}..."
    docker rm -f "${CONTAINER_NAME}"
fi

docker run -d \
    --name "${CONTAINER_NAME}" \
    --restart unless-stopped \
    --gpus all \
    --ipc=host \
    -p "${VLLM_PORT}:8000" \
    -v "${LOCAL_MODEL_ROOT}:/models" \
    "${DOCKER_IMAGE}" \
    --model "/models/${HF_MODEL_ID}" \
    --served-model-name "${SERVED_MODEL_NAME}" \
    --gpu-memory-utilization "${GPU_MEMORY_UTILIZATION}" \
    --max-model-len "${MAX_MODEL_LEN}" \
    --trust-remote-code \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

# 5. 健康检查 (等待启动)
echo "开始检查 vLLM 服务健康状态 (端口 ${VLLM_PORT})..."
MAX_RETRIES=120 # 留出最多10分钟的时间用于第一次启动与 CUDA 图构建
RETRY_COUNT=0

while [ ${RETRY_COUNT} -lt ${MAX_RETRIES} ]; do
    if curl -s "http://127.0.0.1:${VLLM_PORT}/health" > /dev/null 2>&1; then
        echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] vLLM 服务已正常启动！ ==="
        break
    fi
    echo "等待启动... (${RETRY_COUNT}/${MAX_RETRIES})"
    sleep 5
    RETRY_COUNT=$((RETRY_COUNT + 1))
done

if [ ${RETRY_COUNT} -eq ${MAX_RETRIES} ]; then
    echo "警告: vLLM 健康检查超时，请查看 'docker logs ${CONTAINER_NAME}'。"
else
    # 若首次下载未使用 S3，则后台同步至 S3 以优化下次启动 (降低 I/O 优先级，不影响推理)
    if [ "${HAS_S3_MODEL}" = "false" ]; then
        echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] 启动完成，后台同步模型到 S3... ==="
        (
            if ! aws s3 ls "s3://${S3_BUCKET_NAME}" --region "${AWS_REGION}" 2>/dev/null; then
                if [ "${AWS_REGION}" = "us-east-1" ]; then
                    aws s3api create-bucket --bucket "${S3_BUCKET_NAME}" 2>/dev/null || true
                else
                    aws s3api create-bucket --bucket "${S3_BUCKET_NAME}" --region "${AWS_REGION}" --create-bucket-configuration LocationConstraint="${AWS_REGION}" 2>/dev/null || true
                fi
            fi
            ionice -c 3 aws s3 sync "${LOCAL_MODEL_DIR}" "${S3_SRC}" --region "${AWS_REGION}" --no-progress 2>/dev/null || \
            aws s3 sync "${LOCAL_MODEL_DIR}" "${S3_SRC}" --region "${AWS_REGION}" --no-progress 2>/dev/null || true
            echo "[$(date '+%Y-%m-%d %H:%M:%S')] S3 模型备份完成！下次可快速同步。"
        ) > /var/log/s3-backup.log 2>&1 &
    fi
fi

# 6. 启动空闲自动关机（自爆 Terminate）守护进程
echo "=== 设置并启动空闲自动关机守护程序 ==="
cat <<'EOF' > /usr/local/bin/auto-idle-shutdown.sh
#!/bin/bash
IDLE_LIMIT_SEC=3600  # 1 小时 (3600 秒)
IDLE_COUNT=0
CHECK_INTERVAL=300   # 每 5 分钟检查一次

while true; do
    sleep "${CHECK_INTERVAL}"
    
    # 统计最近 5 分钟内发送到 vLLM 的推理请求数
    REQ_COUNT=$(docker logs --since 5m vllm-server 2>&1 | grep -c "POST /v1" || true)
    
    if [ "${REQ_COUNT}" -eq 0 ]; then
        IDLE_COUNT=$((IDLE_COUNT + CHECK_INTERVAL))
        echo "[$(date '+%Y-%m-%d %H:%M:%S')] 空闲中: ${IDLE_COUNT}s / ${IDLE_LIMIT_SEC}s"
        
        if [ "${IDLE_COUNT}" -ge "${IDLE_LIMIT_SEC}" ]; then
            echo "[$(date '+%Y-%m-%d %H:%M:%S')] 已连续空闲 1 小时，执行自动关机 (Terminate)。"
            shutdown -h now
            exit 0
        fi
    else
        IDLE_COUNT=0  # 若有请求则重置计时
    fi
done
EOF

chmod +x /usr/local/bin/auto-idle-shutdown.sh
nohup /usr/local/bin/auto-idle-shutdown.sh > /var/log/auto-idle-shutdown.log 2>&1 &
echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] UserData 全部完成 (空闲监控已启动) ==="

#!/bin/bash
set -euo pipefail

# ==============================================================================
# 02_ec2_launch_and_tunnel.sh
# 启动 EC2 GPU 实例，并通过 SSH 端口转发将 vLLM 安全连接到本地(8000)的脚本
#
# 特点:
#   - 不对外公开 EC2 的端口8000，仅开放端口22 (SSH)
#   - 本地自动建立 SSH 隧道(-L 8000:localhost:8000)
#   - Open WebUI 仅需指向本地 (http://127.0.0.1:8000/v1)，无需更改
# ==============================================================================

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
USER_DATA_FILE="${USER_DATA_FILE:-${SCRIPT_DIR}/02_ec2_userdata.sh}"
INSTANCE_STATE_FILE="${SCRIPT_DIR}/.current_instance_id"
TUNNEL_PID_FILE="${SCRIPT_DIR}/.current_ssh_tunnel_pid"

# --- AWS 设置 ---
AWS_REGION="${AWS_REGION:-ap-northeast-1}"
INSTANCE_TYPE="${INSTANCE_TYPE:-g6.xlarge}" # NVIDIA L4 GPU (24GB VRAM)
AMI_ID="${AMI_ID:-}"
IAM_ROLE_NAME="${IAM_ROLE_NAME:-EC2-S3-ReadOnly-Profile}"
SECURITY_GROUP_IDS="${SECURITY_GROUP_IDS:-}"
SUBNET_ID="${SUBNET_ID:-}"
KEY_NAME="${KEY_NAME:-my-vllm-models-hackathon-2026}"
KEY_PATH="${KEY_PATH:-${HOME}/.ssh/${KEY_NAME}.pem}"
EBS_SIZE_GB="${EBS_SIZE_GB:-40}"

# --- 模型 & UserData 设置 ---
S3_BUCKET_NAME="${S3_BUCKET_NAME:-my-vllm-models-hackathon-2026-$(aws sts get-caller-identity --query Account --output text)-ap-northeast-1-an}"
HF_MODEL_ID="${HF_MODEL_ID:-Qwen/Qwen2.5-Coder-7B-Instruct}"
SERVED_MODEL_NAME="${SERVED_MODEL_NAME:-Qwen/Qwen2.5-Coder-7B-Instruct}"
MAX_MODEL_LEN="${MAX_MODEL_LEN:-16384}"
GPU_MEMORY_UTILIZATION="${GPU_MEMORY_UTILIZATION:-0.90}"
DOCKER_IMAGE="${DOCKER_IMAGE:-vllm/vllm-openai:latest}"

echo "=== 1. 配置确认 ==="
if [ ! -f "${USER_DATA_FILE}" ]; then
    echo "错误: 未找到 UserData 脚本: ${USER_DATA_FILE}"
    echo "请放置第 1 步的 02_ec2_userdata.sh，或通过环境变量 USER_DATA_FILE 指定。"
    exit 1
fi

echo "使用的 SSH 私钥: ${KEY_PATH}"

# 生成带注入环境变量的临时 UserData 脚本
TEMP_USERDATA=$(mktemp)
trap 'rm -f "${TEMP_USERDATA}"' EXIT

sed -e "s|^AWS_REGION=.*|AWS_REGION=\"${AWS_REGION}\"|" \
    -e "s|^S3_BUCKET_NAME=.*|S3_BUCKET_NAME=\"${S3_BUCKET_NAME}\"|" \
    -e "s|^HF_MODEL_ID=.*|HF_MODEL_ID=\"${HF_MODEL_ID}\"|" \
    -e "s|^SERVED_MODEL_NAME=.*|SERVED_MODEL_NAME=\"${SERVED_MODEL_NAME}\"|" \
    -e "s|^MAX_MODEL_LEN=.*|MAX_MODEL_LEN=\"${MAX_MODEL_LEN}\"|" \
    -e "s|^GPU_MEMORY_UTILIZATION=.*|GPU_MEMORY_UTILIZATION=\"${GPU_MEMORY_UTILIZATION}\"|" \
    -e "s|^DOCKER_IMAGE=.*|DOCKER_IMAGE=\"${DOCKER_IMAGE}\"|" \
    "${USER_DATA_FILE}" > "${TEMP_USERDATA}"

if [ -z "${AMI_ID}" ]; then
    echo "自动搜索 Ubuntu 22.04 Deep Learning AMI..."
    AMI_ID=$(aws ec2 describe-images \
        --region "${AWS_REGION}" \
        --owners amazon \
        --filters "Name=name,Values=Deep Learning OSS Nvidia Driver AMI GPU PyTorch * (Ubuntu 22.04)*" "Name=state,Values=available" \
        --query 'sort_by(Images, &CreationDate)[-1].ImageId' \
        --output text)
    echo "使用的 AMI ID: ${AMI_ID}"
fi

# 自动获取或创建仅开放 SSH 的安全组
if [ -z "${SECURITY_GROUP_IDS}" ]; then
    SG_NAME="vllm-ssh-tunnel-sg"
    SG_ID=$(aws ec2 describe-security-groups \
        --region "${AWS_REGION}" \
        --filters "Name=group-name,Values=${SG_NAME}" \
        --query 'SecurityGroups[0].GroupId' --output text 2>/dev/null || true)
    
    if [ -z "${SG_ID}" ] || [ "${SG_ID}" = "None" ]; then
        echo "创建 SSH 专用安全组 (${SG_NAME})..."
        DEFAULT_VPC=$(aws ec2 describe-vpcs --region "${AWS_REGION}" --filters "Name=isDefault,Values=true" --query 'Vpcs[0].VpcId' --output text)
        SG_ID=$(aws ec2 create-security-group \
            --region "${AWS_REGION}" \
            --group-name "${SG_NAME}" \
            --description "仅允许 SSH 22 端口 (vLLM 隧道)" \
            --vpc-id "${DEFAULT_VPC}" \
            --query 'GroupId' --output text)
        aws ec2 authorize-security-group-ingress \
            --region "${AWS_REGION}" --group-id "${SG_ID}" \
            --protocol tcp --port 22 --cidr "0.0.0.0/0"
    fi
    SECURITY_GROUP_IDS="${SG_ID}"
fi

# --- 2. 启动 EC2 实例 ---
echo "=== 2. 启动 EC2 实例 (一次性使用) ==="
INSTANCE_ID=$(aws ec2 run-instances \
    --region "${AWS_REGION}" \
    --image-id "${AMI_ID}" \
    --instance-type "${INSTANCE_TYPE}" \
    --key-name "${KEY_NAME}" \
    --iam-instance-profile "Name=${IAM_ROLE_NAME}" \
    --security-group-ids ${SECURITY_GROUP_IDS} \
    --instance-initiated-shutdown-behavior terminate \
    --block-device-mappings "[{\"DeviceName\":\"/dev/sda1\",\"Ebs\":{\"VolumeSize\":${EBS_SIZE_GB},\"VolumeType\":\"gp3\",\"DeleteOnTermination\":true}}]" \
    --user-data "file://${TEMP_USERDATA}" \
    --tag-specifications "ResourceType=instance,Tags=[{Key=Name,Value=vllm-openwebui-server}]" \
    --query 'Instances[0].InstanceId' \
    --output text)

echo "实例启动请求已提交: ${INSTANCE_ID}"
echo "${INSTANCE_ID}" > "${INSTANCE_STATE_FILE}"

echo "等待实例启动..."
aws ec2 wait instance-running --region "${AWS_REGION}" --instance-ids "${INSTANCE_ID}"

PUBLIC_IP=$(aws ec2 describe-instances \
    --region "${AWS_REGION}" \
    --instance-ids "${INSTANCE_ID}" \
    --query 'Reservations[0].Instances[0].PublicIpAddress' \
    --output text)
echo "公网 IP     : ${PUBLIC_IP}"

# --- 3. 建立 SSH 端口转发 (加密隧道) ---
echo "=== 3. 建立 SSH 端口转发 (加密隧道) ==="
while ! nc -z -w 3 "${PUBLIC_IP}" 22 2>/dev/null; do
    sleep 3
done
echo "EC2 SSHD 响应正常！"

if [ -f "${TUNNEL_PID_FILE}" ]; then
    OLD_PID=$(cat "${TUNNEL_PID_FILE}" | tr -d '[:space:]')
    kill -9 "${OLD_PID}" 2>/dev/null || true
    rm -f "${TUNNEL_PID_FILE}"
fi

ssh -i "${KEY_PATH}" \
    -o StrictHostKeyChecking=no \
    -o UserKnownHostsFile=/dev/null \
    -o ServerAliveInterval=15 \
    -o ServerAliveCountMax=3 \
    -N -f -L 8000:localhost:8000 \
    "ubuntu@${PUBLIC_IP}"

TUNNEL_PID=$(pgrep -f "ssh.*-L 8000:localhost:8000.*ubuntu@${PUBLIC_IP}" | head -n1 || true)
if [ -n "${TUNNEL_PID}" ]; then
    echo "${TUNNEL_PID}" > "${TUNNEL_PID_FILE}"
fi
echo "SSH 隧道已建立！ (PID: ${TUNNEL_PID})"

# --- 4. 等待 vLLM 服务启动 (http://localhost:8000/health) ---
echo "=== 4. 等待 vLLM 服务启动 (http://localhost:8000/health) ==="
while ! curl -s -m 3 "http://localhost:8000/health" > /dev/null 2>&1; do
    sleep 10
done

echo "=========================================================="
echo " 全部流程已完成！"
echo " EC2 实例 ID: ${INSTANCE_ID}"
echo " SSH 隧道   : localhost:8000 -> EC2:8000 (加密中)"
echo " Web UI URL : http://localhost:3000"
echo "=========================================================="

#!/bin/bash
set -euo pipefail

# ==============================================================================
# 04_ec2_terminate.sh
# 验证结束时立即彻底销毁 EC2 实例的脚本
#
# 要点:
#   - EBS 卷一并删除，实现停止时费用归零
# ==============================================================================

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
INSTANCE_STATE_FILE="${SCRIPT_DIR}/.current_instance_id"
TUNNEL_PID_FILE="${SCRIPT_DIR}/.current_ssh_tunnel_pid"
AWS_REGION="${AWS_REGION:-ap-northeast-1}"

INSTANCE_ID="${1:-}"
if [ -z "${INSTANCE_ID}" ] && [ -f "${INSTANCE_STATE_FILE}" ]; then
    INSTANCE_ID=$(cat "${INSTANCE_STATE_FILE}" | tr -d '[:space:]')
fi

if [ -z "${INSTANCE_ID}" ]; then
    echo "错误: 未指定要结束的实例 ID。"
    echo "用法: ./04_ec2_terminate.sh <i-xxxxxxxxxxxxxxxxx>"
    exit 1
fi

echo "=== 彻底销毁 EC2 实例 (Terminate) ==="
echo "目标实例 ID: ${INSTANCE_ID}"
echo "区域       : ${AWS_REGION}"
echo ""
echo "※ EBS 卷 (DeleteOnTermination=true) 会一并删除。"

aws ec2 terminate-instances \
    --region "${AWS_REGION}" \
    --instance-ids "${INSTANCE_ID}" \
    --output table

echo "等待实例终止..."
aws ec2 wait instance-terminated \
    --region "${AWS_REGION}" \
    --instance-ids "${INSTANCE_ID}"

rm -f "${INSTANCE_STATE_FILE}"

# 停止后台的 SSH 隧道进程
if [ -f "${TUNNEL_PID_FILE}" ]; then
    TUNNEL_PID=$(cat "${TUNNEL_PID_FILE}" || true)
    if [ -n "${TUNNEL_PID}" ] && kill -0 "${TUNNEL_PID}" 2>/dev/null; then
        echo "停止 SSH 隧道进程 (PID: ${TUNNEL_PID})..."
        kill "${TUNNEL_PID}" 2>/dev/null || true
    fi
    rm -f "${TUNNEL_PID_FILE}"
fi

echo "=========================================================="
echo " 已彻底销毁实例及 EBS 卷！"
echo " 此后不会产生任何计费（计算/存储）。"
echo "=========================================================="