AWS×UserData让vLLM自动启动！停止时成本几乎为零的本地LLM环境

#!/bin/bash
set -euo pipefail

AWS_REGION="ap-northeast-1"
AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
S3_BUCKET_NAME="my-llm-models-tokyo"
MODEL_ID="Qwen/Qwen2.5-Coder-7B-Instruct"
LOCAL_MODEL_DIR="/tmp/models/${MODEL_ID}"
ECR_REPO_NAME="vllm-openai"
ECR_IMAGE="${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/${ECR_REPO_NAME}:latest"

# 1. 在东京区域创建 S3 存储桶
if ! aws s3api head-bucket --bucket "${S3_BUCKET_NAME}" 2>/dev/null; then
    aws s3api create-bucket \
        --bucket "${S3_BUCKET_NAME}" \
        --region "${AWS_REGION}" \
        --create-bucket-configuration LocationConstraint="${AWS_REGION}"
fi

# 2. 从 Hugging Face 下载模型并同步到 S3
mkdir -p "${LOCAL_MODEL_DIR}"
huggingface-cli download "${MODEL_ID}" --local-dir "${LOCAL_MODEL_DIR}" --local-dir-use-symlinks False
aws s3 sync "${LOCAL_MODEL_DIR}" "s3://${S3_BUCKET_NAME}/models/${MODEL_ID}" \
    --region "${AWS_REGION}" \
    --no-progress

# 3. 创建 Amazon ECR 仓库并注册容器镜像
if ! aws ecr describe-repositories --repository-names "${ECR_REPO_NAME}" --region "${AWS_REGION}" 2>/dev/null; then
    aws ecr create-repository \
        --repository-name "${ECR_REPO_NAME}" \
        --region "${AWS_REGION}" \
        --image-scanning-configuration scanOnPush=true
fi

# 登录到 ECR
aws ecr get-login-password --region "${AWS_REGION}" | \
    docker login --username AWS --password-stdin "${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com"

# 拉取 vLLM 官方镜像并推送到 ECR
docker pull vllm/vllm-openai:latest
docker tag vllm/vllm-openai:latest "${ECR_IMAGE}"
docker push "${ECR_IMAGE}"

#!/bin/bash
set -euo pipefail

# ==============================================================================
# 02_ec2_userdata.sh
# 在 EC2 启动时执行的 UserData 脚本
#
# 前提条件：
#   - AMI: Ubuntu 22.04 Deep Learning AMI（已预装 NVIDIA 驱动 & Docker）
#   - 实例类型: g6.xlarge（NVIDIA L4 GPU: 24GB VRAM, 附带 250GB NVMe SSD）
#   - 已附加 IAM 角色: 包含 S3（只读/完全访问）及 ECR（只读）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="0.85"
MAX_MODEL_LEN="4096"
DOCKER_IMAGE="${DOCKER_IMAGE:-vllm/vllm-openai:latest}"

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

# 1. 检测并利用本地 NVMe 实例存储（250GB）
# ※ AWS Deep Learning AMI 会自动将本地 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 NVMe 挂载 (${NVME_DIR})。将其用作模型 & Docker 存储区域..."
    mkdir -p "${NVME_DIR}/models" "${NVME_DIR}/docker"
    mkdir -p /data
    ln -sfn "${NVME_DIR}/models" /data/models
else
    # 回退处理：非 DLAMI AMI 或未挂载
    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/${NVME_DEV})。挂载到 /data..."
        mkfs.ext4 -F "/dev/${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 数据目录放到 NVMe 上，防止 EBS 耗尽 & 加速层展开
# ※ Docker 24+ & containerd 会在 /var/lib/containerd 展开快照，绑定挂载到 NVMe
echo "停止 Docker/containerd 并设置绑定挂载到 NVMe 区域..."
systemctl stop docker containerd || true
mkdir -p "${NVME_DIR}/docker" "${NVME_DIR}/containerd"
mkdir -p /var/lib/docker /var/lib/containerd
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 (S3已有则同步，否则直接从 Hugging Face 获取并备份)
echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] 确认模型数据准备... ==="
S3_SRC="s3://${S3_BUCKET_NAME}/models/${HF_MODEL_ID}"
echo "S3 目标: ${S3_SRC}/ (区域: ${AWS_REGION})"
aws configure set default.s3.max_concurrent_requests 20
echo "正在确认 IAM 认证及 S3 存储桶连接..."
for i in {1..15}; do
    if aws s3 ls "s3://${S3_BUCKET_NAME}" --region "${AWS_REGION}" >/dev/null 2>&1; then
        echo "已确认对 S3 存储桶的连接。"
        break
    fi
    echo "等待 S3 连接 / IAM 认证 ($i/15)..."
    sleep 2
done
HAS_S3_MODEL=false
echo "正在执行 S3 上模型存在检查..."
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 "=== [$(date '+%Y-%m-%d %H:%M:%S')] 在 S3 上发现模型。正在同步... ==="
    aws s3 sync "${S3_SRC}" "${LOCAL_MODEL_DIR}" --region "${AWS_REGION}" --no-progress
else
    echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] S3 上未发现模型。正在直接下载... ==="
    python3 -m pip install -U "huggingface_hub[cli]" || pip3 install -U "huggingface_hub[cli]" || true
    echo "正在从 Hugging Face ('${HF_MODEL_ID}') 直接下载模型..."
    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。容量："
    du -sh "${LOCAL_MODEL_DIR}"
fi
echo "模型准备完成。本地容量确认："
du -sh "${LOCAL_MODEL_DIR}"

# 4. 启动 vLLM 容器
echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] 启动 vLLM 容器 ==="
CONTAINER_NAME="vllm-server"
if [[ "${DOCKER_IMAGE}" == *".dkr.ecr."* ]]; then
    echo "检测到 ECR 镜像。正在登录认证..."
    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
if docker ps -a --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then
    echo "正在停止并删除已有的 ${CONTAINER_NAME}..."
    docker rm -f "${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

# 5. 启动健康检查
echo "开始健康检查 (端口 ${VLLM_PORT})..."
MAX_RETRIES=120 # 最多等待10分钟
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 "=== [$(date '+%Y-%m-%d %H:%M:%S')] vLLM 服务器正常启动！ ==="
        break
    fi
    echo "等待启动… (${RETRY_COUNT}/${MAX_RETRIES})"
    sleep 5
    RETRY_COUNT=$((RETRY_COUNT + 1))
done
if [ ${RETRY_COUNT} -eq ${MAX_RETRIES} ]; then
    echo "警告: 健康检查超时，请查看 'docker logs ${CONTAINER_NAME}'。"
else
    if [ "${HAS_S3_MODEL}" = "false" ]; then
        echo "=== [$(date '+%Y-%m-%d %H:%M:%S')] 启动完成，后台开始 S3 备份… ==="
        (
            ionice -c 3 aws s3 sync "${LOCAL_MODEL_DIR}" "${S3_SRC}" --region "${AWS_REGION}" --no-progress || true
            echo "[$(date '+%Y-%m-%d %H:%M:%S')] S3 模型备份完成！"
        ) > /var/log/s3-backup.log 2>&1 &
    fi
fi

# 6. 空闲自动结束守护进程
echo "=== 设置并启动空闲自动结束守护进程 ==="
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 "${CHECK_INTERVAL}"
    # 从日志中统计最近5分钟内 vLLM 的推理请求数
    REQ_COUNT=$(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')] 空闲1小时，执行自动结束。"
            shutdown -h now
            exit 0
        fi
    else
        IDLE_COUNT=0
    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 全部处理完成 (自动空闲监控已启动) ==="

#!/bin/bash
set -euo pipefail

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

AWS_REGION="${AWS_REGION:-ap-northeast-1}"
INSTANCE_TYPE="${INSTANCE_TYPE:-g6.xlarge}" # NVIDIA L4 GPU (24GB VRAM)
IAM_ROLE_NAME="${IAM_ROLE_NAME:-EC2-S3-ECR-ReadOnly-Profile}"
EBS_SIZE_GB="${EBS_SIZE_GB:-40}" # DLAMI 快照限制下最小40GB

# 1. 自动查找最新 Deep Learning AMI
echo "正在自动查找 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)

# 2. 自动检查/创建安全组 (8000,22)
DEFAULT_VPC=$(aws ec2 describe-vpcs --region "${AWS_REGION}" --filters "Name=isDefault,Values=true" --query "Vpcs[0].VpcId" --output text)
EXISTING_SG=$(aws ec2 describe-security-groups --region "${AWS_REGION}" --filters "Name=vpc-id,Values=${DEFAULT_VPC}" "Name=group-name,Values=vllm-sg" --query "SecurityGroups[0].GroupId" --output text 2>/dev/null || echo "")
if [ -n "${EXISTING_SG}" ] && [ "${EXISTING_SG}" != "None" ]; then
    SG_ID="${EXISTING_SG}"
else
    SG_ID=$(aws ec2 create-security-group --region "${AWS_REGION}" --group-name "vllm-sg" --description "SG for vLLM API" --vpc-id "${DEFAULT_VPC}" --query "GroupId" --output text)
    aws ec2 authorize-security-group-ingress --region "${AWS_REGION}" --group-id "${SG_ID}" --protocol tcp --port 8000 --cidr "0.0.0.0/0"
    aws ec2 authorize-security-group-ingress --region "${AWS_REGION}" --group-id "${SG_ID}" --protocol tcp --port 22 --cidr "0.0.0.0/0"
fi

# 3. 创建 IAM 实例配置文件 (S3 & ECR 读取)
if ! aws iam get-instance-profile --instance-profile-name "${IAM_ROLE_NAME}" >/dev/null 2>&1; then
    aws iam create-role --role-name "${IAM_ROLE_NAME}-Role" \
        --assume-role-policy-document '{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Principal":{"Service":"ec2.amazonaws.com"},"Action":"sts:AssumeRole"}]}'
    aws iam attach-role-policy --role-name "${IAM_ROLE_NAME}-Role" --policy-arn arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess
    aws iam attach-role-policy --role-name "${IAM_ROLE_NAME}-Role" --policy-arn arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly
    aws iam create-instance-profile --instance-profile-name "${IAM_ROLE_NAME}"
    aws iam add-role-to-instance-profile --instance-profile-name "${IAM_ROLE_NAME}" --role-name "${IAM_ROLE_NAME}-Role"
    sleep 5 # 等待 IAM 权限传播
fi

# 4. Base64 编码 UserData 并启动实例
USERDATA_BASE64=$(base64 -w 0 "${USER_DATA_FILE}" 2>/dev/null || base64 "${USER_DATA_FILE}" | tr -d '\r\n')
INSTANCE_ID=$(aws ec2 run-instances \
    --region "${AWS_REGION}" \
    --image-id "${AMI_ID}" \
    --instance-type "${INSTANCE_TYPE}" \
    --iam-instance-profile "Name=${IAM_ROLE_NAME}" \
    --security-group-ids "${SG_ID}" \
    --user-data "${USERDATA_BASE64}" \
    --block-device-mappings "[{\"DeviceName\":\"/dev/sda1\",\"Ebs\":{\"VolumeSize\":${EBS_SIZE_GB},\"VolumeType\":\"gp3\",\"DeleteOnTermination\":true}}]" \
    --instance-initiated-shutdown-behavior terminate \
    --tag-specifications "ResourceType=instance,Tags=[{Key=Name,Value=vllm-server-temp}]" \
    --query 'Instances[0].InstanceId' \
    --output text)
echo "实例启动开始: ${INSTANCE_ID}"
echo "${INSTANCE_ID}" > "${INSTANCE_STATE_FILE}"

# 5. 等待启动完成 & 获取公网 IP
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 "实例启动完成: IP = ${PUBLIC_IP}"
echo "等待健康检查 (约7~10分)..."
while ! curl -s "http://${PUBLIC_IP}:8000/health" >/dev/null 2>&1; do
    echo -n "."
    sleep 5
done
echo ""
echo "🎉 vLLM 服务器已准备就绪！ (http://${PUBLIC_IP}:8000)"

PUBLIC_IP="<启动的 EC2 公网 IP>"

# 1. 创建请求体 JSON
cat <<'EOF' > req.json
{
  "model": "Qwen/Qwen2.5-Coder-7B-Instruct",
  "messages": [
    {"role": "user", "content": "请用三行说明在 AWS 东京区域临时（一次性）运行 GPU 实例的优势。"}
  ]
}
EOF

# 2. 向 vLLM 的 OpenAI 兼容端点发送请求
curl http://${PUBLIC_IP}:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d @req.json

{
  "id": "chatcmpl-a662010ed226ea08",
  "object": "chat.completion",
  "created": 1789410574,
  "model": "Qwen/Qwen2.5-Coder-7B-Instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "1. 成本效率：临时 GPU 实例适合短时或一次性作业，可利用无费用时段。\n2. 可扩展性：可根据需求轻松增删实例。\n3. 安全性：使用后实例会被立即删除，降低数据泄露等风险。"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 58,
    "total_tokens": 163,
    "completion_tokens": 105
  }
}

#!/bin/bash
set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
INSTANCE_STATE_FILE="${SCRIPT_DIR}/../.current_instance_id"
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：未指定要终止的实例 ID。"
    exit 1
fi

echo "=== 完全终止 EC2 实例 (Terminate) ==="
echo "目标实例 ID: ${INSTANCE_ID}"
echo "※ EBS 卷(DeleteOnTermination=true)也将一并销毁。"

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

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

rm -f "${INSTANCE_STATE_FILE}"

echo "=========================================================="
echo " 实例及 EBS 卷已完全销毁！"
echo " 之后，该实例相关的计算/EBS 计费将不再发生。"
echo "=========================================================="

./scripts/04_ec2_terminate.sh