diff --git a/README.md b/README.md
index c135c99..991ba1f 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ Model Hub V2是提供一站式的模型微调，部署，调试的无代码可
 - ⚠️注意，stack显示部署完成之后，启动的EC2还需要8-10分钟自动运行一些脚本，如果不行，请等待8-10分钟，然后刷新页面
 ![alt text](./assets/image-cf4.png)
 
-# 2.手动部署
+# 2.手动部署（中国区）
 ## 1.环境安装
 - 硬件需求：一台ec2 Instance, m5.xlarge, 200GB EBS storage
 - os需求：ubuntu 22.04
@@ -38,7 +38,6 @@ Model Hub V2是提供一站式的模型微调，部署，调试的无代码可
 - 找到刚才的role，创建一个inline policy
 - ![alt text](./assets/image-3.png)
 - ![alt text](./assets/image-4.png)
-- 注意，如果是中国区，需要把 "arn:aws:s3:::*"改成 "arn:aws-cn:s3:::sagemaker*"
 ```json
 {
     "Version": "2012-10-17",
@@ -53,69 +52,54 @@ Model Hub V2是提供一站式的模型微调，部署，调试的无代码可
                 "s3:CreateBucket"
             ],
             "Resource": [
-                "arn:aws:s3:::*"
+                "*"
+            ]
+        },
+        {
+            "Effect": "Allow",
+            "Action": [
+                "ssmmessages:CreateControlChannel"
+            ],
+            "Resource": [
+                "*"
             ]
         }
     ]
 }
 ```
 - ssh 到ec2 instance
-
 - 如果是中国区需要手动下载代码并打包传到ec2中
-- 请先在能访问github的环境中执行以下命令下载代码，然后把代码打包成zip文件，上传到ec2服务器。
+- 请先在能访问github的环境中执行以下命令下载代码，然后把代码打包成zip文件，上传到ec2服务器的/home/ubuntu/下。
 - 使用--recurse-submodule下载代码  
 ```bash
 git clone --recurse-submodule https://github.com/aws-samples/llm_model_hub.git
 ```
-
-## 2.部署前端
-1. 安装nodejs 18
-```bash
-curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
-```
-2. 如果是中国区安装，则用设置中国代理
-```bash
-npm config set registry https://registry.npm.taobao.org
-```
-3. 安装yarn
-```bash
-sudo apt install -y nodejs
-sudo npm install --global yarn
-```
-2. 配置环境变量
-- copy llm_model_hub/env.sample 到 .env 文件,修改ip改成对应的ec2的ip，随机给一个api key，这个key需要与下一部分后端配置backend/.env中的apikey保持一致
+## 2.ssh登陆到ec2服务器，解压到/home/ubuntu/目录
+```sh
+unzip llm_model_hub.zip
 ```
-REACT_APP_API_ENDPOINT=http://{ip}:8000/v1
-REACT_APP_API_KEY=随机给一个key
-```
-
-
 
-3. 启动web page
-- 安装yarn
-```bash
-yarn install
+## 3.设置环境变量
+```sh
+export SageMakerRoleArn=<上面步骤创建的sagemaker_exection_role的完整arn,如 arn:aws-cn:iam:1234567890:role/sagemaker_exection_role>
 ```
-
-```bash
-#install pm2
-sudo yarn global add pm2
-pm2 start pm2run.config.js 
+- （可选）如需要设置Swanlab或者wandb作为metrics监控看板,也可以后期在backend/.env中添加，添加之后运行pm2 restart all重启服务
+```sh
+export SWANLAB_API_KEY=<SWANLAB_API_KEY>
+export WANDB_API_KEY=<WANDB_API_KEY>
+export WANDB_BASE_URL=<WANDB_BASE_URL>
 ```
-- 以下是其他的管理命令(作为参考，不用执行):
+
+## 4.执行脚本
 ```bash
-pm2 list
-pm2 stop modelhub
-pm2 restart modelhub
-pm2 delete modelhub
+cd /home/ubuntu/llm_model_hub
+bash cn-region-deploy.sh
 ```
+大约40~60分钟（取决于docker镜像网站速度）之后执行完成，可以在/home/ubuntu/setup.log中查看安装日志。
 
-## 3.后端配置
-请见[后端配置](./backend/README.md)
-
-## 4.启动前端
-- 以上都部署完成后，前端启动之后，可以通过浏览器访问http://{ip}:3000访问前端
-- 如果需要做端口转发，则参考后端配置中的nginx配置部分
+## 5.访问
+- 以上都部署完成后，前端启动之后，可以通过浏览器访问http://{ip}:3000访问前端，/home/ubuntu/setup.log中查看用户名和随机密码
+- 如果需要做端口转发，则参考[后端配置](./backend/README.md)中的nginx配置部分
 
 
 # 如何升级？
diff --git a/backend/0.setup-cn.sh b/backend/0.setup-cn.sh
index 966ecee..727066f 100644
--- a/backend/0.setup-cn.sh
+++ b/backend/0.setup-cn.sh
@@ -1,37 +1,11 @@
 
 # 定义要添加的内容
-MIRROR_LINE="-i https://pypi.tuna.tsinghua.edu.cn/simple"
+PIP_INDEX="http://mirrors.aliyun.com/pypi/simple/"
 
-# 处理 backend/requirements.txt
-BACKEND_REQ="/home/ubuntu/llm_model_hub/backend/requirements.txt"
-if [ -f "$BACKEND_REQ" ]; then
-    sed -i "1i$MIRROR_LINE" "$BACKEND_REQ"
-    echo "Added mirror line to $BACKEND_REQ"
-else
-    echo "File $BACKEND_REQ not found"
-fi
+pip config set global.index-url "$PIP_INDEX" &&     pip config set global.extra-index-url "$PIP_INDEX" 
 
-# 处理 backend/byoc/requirements.txt
-BACKEND2_REQ="/home/ubuntu/llm_model_hub/backend/byoc/requirements.txt"
-if [ -f "$BACKEND2_REQ" ]; then
-    sed -i "1i$MIRROR_LINE" "$BACKEND2_REQ"
-    echo "Added mirror line to $BACKEND2_REQ"
-    sed -i 's|https://github.com/|https://gitclone.com/github.com/|' "$BACKEND2_REQ"
-else
-    echo "File $BACKEND2_REQ not found"
-fi
-
-
-
-# 处理 backend/LLaMA-Factory/requirements.txt
-LLAMA_REQ="/home/ubuntu/llm_model_hub/backend/LLaMA-Factory/requirements.txt"
-if [ -f "$LLAMA_REQ" ]; then
-    sed -i "1i$MIRROR_LINE" "$LLAMA_REQ"
-    sed -i 's|https://github.com/|https://gitclone.com/github.com/|' "$LLAMA_REQ"
-    echo "Modified $LLAMA_REQ"
-else
-    echo "File $LLAMA_REQ not found"
-fi
+# 删除flash-attn，中国区安装超时
+sed -i '/^flash_attn==/d' /home/ubuntu/llm_model_hub/backend/docker/requirements_deps.txt
 
 ##设置默认aws region
 sudo apt install awscli
diff --git a/backend/02.start_backend.sh b/backend/02.start_backend.sh
index 0451465..6101259 100644
--- a/backend/02.start_backend.sh
+++ b/backend/02.start_backend.sh
@@ -1,5 +1,6 @@
 #!/bin/bash
 source ../miniconda3/bin/activate py311
 conda activate py311
+cd /home/ubuntu/llm_model_hub/backend/
 pm2 start server.py --name "modelhub-server" --interpreter ../miniconda3/envs/py311/bin/python3 -- --host 0.0.0.0 --port 8000
 pm2 start processing_engine/main.py --name "modelhub-engine" --interpreter ../miniconda3/envs/py311/bin/python3
diff --git a/backend/README.md b/backend/README.md
index f08082e..1510b98 100644
--- a/backend/README.md
+++ b/backend/README.md
@@ -132,6 +132,11 @@ http {
 }
 ```
 
+- 修改llm_modelhub/.env 文件中的域名和端口
+```
+REACT_APP_API_ENDPOINT==http://xxxx.compute-1.amazonaws.com:443/v1
+```
+
 - 生效配置:
 ```bash
 sudo ln -s /etc/nginx/sites-available/modelhub /etc/nginx/sites-enabled/ 
diff --git a/backend/byoc/build_and_push.sh b/backend/byoc/build_and_push.sh
index f0fc1c7..b048d15 100644
--- a/backend/byoc/build_and_push.sh
+++ b/backend/byoc/build_and_push.sh
@@ -14,7 +14,7 @@ region=$(curl -H "X-aws-ec2-metadata-token: $TOKEN" -s http://169.254.169.254/la
 # region=$(aws configure get region)
 suffix="com"
 
-if [[ "$region" == cn*  ]]; then
+if [[ $region =~ ^cn ]]; then
     suffix="com.cn"
 fi
 
diff --git a/backend/docker/build_and_push.sh b/backend/docker/build_and_push.sh
index bea6261..fe5331a 100644
--- a/backend/docker/build_and_push.sh
+++ b/backend/docker/build_and_push.sh
@@ -14,7 +14,7 @@ region=$(curl -H "X-aws-ec2-metadata-token: $TOKEN" -s http://169.254.169.254/la
 # region=$(aws configure get region)
 suffix="com"
 
-if [ "$region" == cn*  ]; then
+if [[ $region =~ ^cn ]]; then
     suffix="com.cn"
 fi
 
@@ -39,7 +39,7 @@ aws  ecr get-login-password --region $region | docker login --username AWS --pas
 # First, authenticate with AWS ECR
 # Run these commands in your terminal before building:
 
-if [[ "$region" == cn*  ]]; then
+if [[ $region =~ ^cn ]]; then
     aws ecr get-login-password --region $region | docker login --username AWS --password-stdin 727897471807.dkr.ecr.$region.amazonaws.${suffix}
 else
     aws ecr get-login-password --region $region | docker login --username AWS --password-stdin 763104351884.dkr.ecr.$region.amazonaws.${suffix}
@@ -55,7 +55,7 @@ aws ecr set-repository-policy \
 
 # Add variables for build arguments pytorch-training:2.5.1-gpu-py311-cu124-ubuntu22.04-sagemaker
 # https://github.com/aws/deep-learning-containers/blob/master/available_images.md
-if [[ "$region" == cn*  ]]; then
+if [[ $region =~ ^cn ]]; then
     BASE_IMAGE="727897471807.dkr.ecr.${region}.amazonaws.${suffix}/pytorch-training:2.4.0-gpu-py311"
     PIP_INDEX="https://mirrors.aliyun.com/pypi/simple"
 else
diff --git a/backend/docker/requirements_deps.txt b/backend/docker/requirements_deps.txt
index a32c469..774cd31 100644
--- a/backend/docker/requirements_deps.txt
+++ b/backend/docker/requirements_deps.txt
@@ -1,6 +1,7 @@
 deepspeed<=0.15.4
 # intel-extension-for-pytorch==2.4.0 
-autoawq @git+https://github.com/casper-hansen/AutoAWQ.git@79547665bdb27768a9b392ef375776b020acbf0c
+# autoawq @git+https://github.com/casper-hansen/AutoAWQ.git@79547665bdb27768a9b392ef375776b020acbf0c
+autoawq<=0.2.7
 metrics
 bitsandbytes>=0.39.0
 rouge-chinese
@@ -10,4 +11,5 @@ pandas
 modelscope
 wandb
 nltk
+swanlab
 flash_attn==2.6.3
\ No newline at end of file
diff --git a/backend/docker/train.py b/backend/docker/train.py
index d310271..2e81732 100644
--- a/backend/docker/train.py
+++ b/backend/docker/train.py
@@ -11,8 +11,8 @@
 from multiprocessing import Process
 
 
-def load_s3_json(s3_path):
-    s3_client = boto3.client('s3')
+def load_s3_json(s3_path,region_name):
+    s3_client = boto3.client('s3',region_name)
     parsed = urlparse(s3_path)
     bucket = parsed.netloc
     key = parsed.path.lstrip('/')
@@ -23,6 +23,17 @@ def dict_to_cmd_args(doc: dict) -> str:
     cmd_parts = [f"--{key} {value}" for key, value in doc.items()]
     return " ".join(cmd_parts)
 
+# delete arg from cmd args
+def delete_arg(args_string, arg_name):
+    parts = args_string.split()
+    for i,part in enumerate(parts):
+        if part.startswith(f"--{arg_name}"):
+            next_part = parts[i+1]
+            parts.remove(part)
+            parts.remove(next_part)
+            break
+    return " ".join(parts)
+
 def update_arg_value(args_string, arg_name, new_value):
     parts = args_string.split()
     for i, part in enumerate(parts):
@@ -94,9 +105,10 @@ def stop_monitoring():
 
 if __name__ == "__main__":
 
-    train_args_json = load_s3_json(os.environ['train_args_path'])
-    merge_args_json = load_s3_json(os.environ['merge_args_path'])
-    datainfo = load_s3_json(os.environ['dataset_info_path'])
+    regin_name = os.environ['REGION'] 
+    train_args_json = load_s3_json(os.environ['train_args_path'],regin_name)
+    merge_args_json = load_s3_json(os.environ['merge_args_path'],regin_name)
+    datainfo = load_s3_json(os.environ['dataset_info_path'],regin_name)
 
     #save to data folder
     with open('/opt/ml/code/data/dataset_info.json', 'w') as f:
@@ -141,14 +153,6 @@ def stop_monitoring():
     GPUS_PER_NODE = int(os.environ["SM_NUM_GPUS"])
     DEVICES = ','.join([str(i) for i in range(GPUS_PER_NODE)])
 
-    # index_path = os.environ.get('PIP_INDEX')
-    # if index_path:
-    #     os.system(f"pip config set global.index-url {index_path}")
-    #     os.system(f"pip config set global.extra-index-url  {index_path}")
-    #     os.system(f"pip install -r requirements_deps.txt")
-    # else:
-    #     os.system(f"pip install -r requirements_deps.txt")
-
     
     os.system("chmod +x ./s5cmd")
 
@@ -166,6 +170,9 @@ def stop_monitoring():
         s3_checkpoint = s3_checkpoint[:-1] if s3_checkpoint.endswith('/') else s3_checkpoint
         # download to local
         run_command(f'./s5cmd sync --exclude "checkpoint-*" {s3_checkpoint}/* /tmp/checkpoint/')
+        # change the model path to local
+        train_args = update_arg_value(train_args,"model_name_or_path","/tmp/checkpoint/")
+        #add resume_from_checkpoint arg
         train_args += " --resume_from_checkpoint /tmp/checkpoint/"
         print(f"resume_from_checkpoint {s3_checkpoint}")
     
@@ -180,9 +187,9 @@ def stop_monitoring():
         train_args = update_arg_value(train_args,"model_name_or_path","/tmp/model_path/")
         print(f"s3 model_name_or_path {s3_model_path}")
 
-    if host_rank == 0:
-        # 启动checkpoint监控进程
-        start_monitoring()
+    # if host_rank == 0:
+    # 启动checkpoint监控进程，ckpt分布在各个节点保存
+    start_monitoring()
 
     print(f'------envs------\nnum_machines:{num_machines}\nnum_processes:{num_processes}\nnode_rank:{host_rank}\n')
     if num_machines > 1: 
@@ -198,9 +205,9 @@ def stop_monitoring():
             stop_monitoring()
         sys.exit(1)
 
-    if host_rank == 0:
-        # 停止checkpoint监控
-        stop_monitoring()
+    # if host_rank == 0:
+    # 停止checkpoint监控
+    stop_monitoring()
         
     if os.environ.get("merge_lora") == '1' and host_rank == 0:
         ## update model path as local folder as s3 provided
diff --git a/backend/env.sample b/backend/env.sample
index 1dac791..58b6797 100644
--- a/backend/env.sample
+++ b/backend/env.sample
@@ -11,6 +11,7 @@ api_keys=123456
 HUGGING_FACE_HUB_TOKEN=
 WANDB_API_KEY=
 WANDB_BASE_URL=
+SWANLAB_API_KEY=
 vllm_image=.dkr.ecr.us-east-1.amazonaws.com/sagemaker_endpoint/vllm:v0.6.4
 model_artifact=s3://sagemaker-us-east-1-/sagemaker_endpoint/vllm/model.tar.gz
 training_image=.dkr.ecr.us-east-1.amazonaws.com/llamafactory/llamafactory:0.9.2.dev0
\ No newline at end of file
diff --git a/backend/training/training_job.py b/backend/training/training_job.py
index 9323db6..baff637 100644
--- a/backend/training/training_job.py
+++ b/backend/training/training_job.py
@@ -17,7 +17,7 @@
 import dotenv
 import os
 from utils.config import boto_sess,role,default_bucket,sagemaker_session, is_efa, \
-LORA_BASE_CONFIG,DEEPSPEED_BASE_CONFIG_MAP,FULL_BASE_CONFIG,DEFAULT_REGION,WANDB_API_KEY, WANDB_BASE_URL
+LORA_BASE_CONFIG,DEEPSPEED_BASE_CONFIG_MAP,FULL_BASE_CONFIG,DEFAULT_REGION,WANDB_API_KEY, WANDB_BASE_URL, SWANLAB_API_KEY
 
 dotenv.load_dotenv()
 
@@ -180,6 +180,13 @@ def create_training_args(self,
             doc['report_to'] = "wandb"
             timestp = to_datetime_string(time.time()).replace(' ', '_')
             doc['run_name'] = f"modelhub_run_{timestp}"
+
+        if SWANLAB_API_KEY:
+            doc['use_swanlab'] = True
+            timestp = to_datetime_string(time.time()).replace(' ', '_')
+            doc['swanlab_run_name'] = f"sagemaker_modelhub_run_{timestp}"
+            doc['swanlab_project'] = f"sagemaker_modelhub"
+            doc['swanlab_api_key'] = SWANLAB_API_KEY
             
         #训练精度
         if job_payload['training_precision'] == 'bf16':
@@ -271,7 +278,8 @@ def create_training(self,
             "merge_args_path":sg_lora_merge_config,
             "train_args_path":sg_config,
             'OUTPUT_MODEL_S3_PATH': output_s3_path, # destination 
-            "PIP_INDEX":'https://mirrors.aliyun.com/pypi/simple' if DEFAULT_REGION.startswith('cn') else '',
+            "REGION": DEFAULT_REGION,
+            # "PIP_INDEX":'https://mirrors.aliyun.com/pypi/simple' if DEFAULT_REGION.startswith('cn') else '',
             "USE_MODELSCOPE_HUB": "1" if DEFAULT_REGION.startswith('cn') else '0'
             
         }
diff --git a/backend/users/add_user.py b/backend/users/add_user.py
index 75866de..525b18d 100644
--- a/backend/users/add_user.py
+++ b/backend/users/add_user.py
@@ -1,5 +1,6 @@
 import sys
 sys.path.append('./')
+
 from db_management.database import DatabaseWrapper
 from logger_config import setup_logger
 import argparse
diff --git a/backend/utils/config.py b/backend/utils/config.py
index c317723..de7270e 100644
--- a/backend/utils/config.py
+++ b/backend/utils/config.py
@@ -52,6 +52,7 @@
                              "stage_3":'examples/deepspeed/ds_z3_config.json'}
 WANDB_API_KEY  = os.environ.get('WANDB_API_KEY','')
 WANDB_BASE_URL = os.environ.get('WANDB_BASE_URL','')
+SWANLAB_API_KEY = os.environ.get('SWANLAB_API_KEY','')
 
 # 加载持久化之后的模型列表，在endpoingt_management.py中支持修改
 try:
@@ -87,4 +88,4 @@
 }
 
 def is_efa(instance_type):
-    return 'ml.p4' in instance_type or 'ml.p5' in instance_type
\ No newline at end of file
+    return 'ml.p4' in instance_type or 'ml.p5' in instance_type or 'ml.g6e' in instance_type or 'ml.g5' in instance_type
\ No newline at end of file
diff --git a/cloudformation-template.yaml b/cloudformation-template.yaml
index d28e5aa..46c81f7 100644
--- a/cloudformation-template.yaml
+++ b/cloudformation-template.yaml
@@ -32,6 +32,12 @@ Parameters:
     Description: Optional WANDB Base URL for view W&B own Wandb portal
     Default: ""
 
+  SwanlabApiKey:
+    Type: String
+    Description: Optional SWANLAB for view Metrics on https://swanlab.cn/
+    Default: ""
+
+
 Resources:
   EC2Instance:
     Type: AWS::EC2::Instance
@@ -135,6 +141,7 @@ Resources:
               HUGGING_FACE_HUB_TOKEN=${HuggingFaceHubToken}
               WANDB_API_KEY=${WandbApiKey}
               WANDB_BASE_URL=${WandbBaseUrl}
+              SWANLAB_API_KEY=${SwanlabApiKey}
               EOF
 
               # 设置权限
@@ -200,6 +207,7 @@ Resources:
               HuggingFaceHubToken: !Ref HuggingFaceHubToken
               WandbApiKey: !Ref WandbApiKey
               WandbBaseUrl: !Ref WandbBaseUrl
+              SwanlabApiKey: !Ref SwanlabApiKey
 
   EC2SecurityGroup:
     Type: AWS::EC2::SecurityGroup
diff --git a/cn-region-deploy.sh b/cn-region-deploy.sh
new file mode 100644
index 0000000..9a8a6f2
--- /dev/null
+++ b/cn-region-deploy.sh
@@ -0,0 +1,191 @@
+#!/bin/bash
+echo "########################注意#####################"
+echo "请确认下载代码时用了--recurse-submodule，检查下backend/docker/LLaMA-Factory/文件下是否不为空"
+#中国区事先手动下载
+#git clone --recurse-submodule https://github.com/aws-samples/llm_model_hub.git
+# 设置日志文件
+LOG_FILE="/home/ubuntu/setup.log"
+
+touch "$LOG_FILE"
+# 函数：记录日志
+log() {
+echo "$(date '+%Y-%m-%d %H:%M:%S') - $1" >> "$LOG_FILE"
+}
+
+log "Starting UserData script execution"
+sudo apt update
+sudo apt install -y git
+
+if ! command -v aws &> /dev/null; then
+    #安装awscli
+    curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
+    unzip awscliv2.zip
+    sudo ./aws/install
+fi
+
+# echo "##create sagemaker execution role"
+# # Create trust policy
+# echo '{
+#   "Version": "2012-10-17",
+#   "Statement": [
+#     {
+#       "Effect": "Allow",
+#       "Principal": {
+#         "Service": "sagemaker.amazonaws.com"
+#       },
+#       "Action": "sts:AssumeRole"
+#     }
+#   ]
+# }' > trust-policy.json
+
+# # Create S3 policy
+# echo '{
+#     "Version": "2012-10-17",
+#     "Statement": [
+#         {
+#             "Effect": "Allow",
+#             "Action": [
+#                 "s3:GetObject",
+#                 "s3:PutObject",
+#                 "s3:DeleteObject",
+#                 "s3:ListBucket",
+#                 "s3:CreateBucket"
+#             ],
+#             "Resource": [
+#                 "arn:aws-cn:s3:::*"
+#             ]
+#         }
+#     ]
+# }' > s3-policy.json
+
+# # Generate random suffix
+# RANDOM_SUFFIX=$(date +%s | sha256sum | base64 | head -c 8)
+# ROLE_NAME="sagemaker_execution_role_${RANDOM_SUFFIX}"
+# POLICY_NAME="sagemaker_s3_policy_${RANDOM_SUFFIX}"
+
+# # Create role and capture the ARN
+# SageMakerRoleArn=$(aws iam create-role \
+#     --role-name ${ROLE_NAME} \
+#     --assume-role-policy-document file://trust-policy.json \
+#     --query 'Role.Arn' --output text)
+
+# # Create policy
+# POLICY_ARN=$(aws iam create-policy \
+#     --policy-name ${POLICY_NAME} \
+#     --policy-document file://s3-policy.json \
+#     --query 'Policy.Arn' --output text)
+
+# # Attach policies
+# aws iam attach-role-policy \
+#     --role-name ${ROLE_NAME} \
+#     --policy-arn ${POLICY_ARN}
+
+# aws iam attach-role-policy \
+#     --role-name ${ROLE_NAME} \
+#     --policy-arn arn:aws-cn:iam::aws:policy/AmazonSageMakerFullAccess
+
+# # Clean up temporary files
+# rm trust-policy.json s3-policy.json
+
+# echo "Created role: ${ROLE_NAME}" >>  "$LOG_FILE"
+# echo "Role ARN: ${SageMakerRoleArn}" >>  "$LOG_FILE"
+
+
+#install nodejs 
+log "Installing nodejs"
+curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
+sudo apt install -y nodejs
+sudo npm config set registry http://mirrors.cloud.tencent.com/npm/
+sudo npm install --global yarn
+# download file
+cd /home/ubuntu/
+#中国区事先手动下载
+#git clone --recurse-submodule https://github.com/aws-samples/llm_model_hub.git
+cd /home/ubuntu/llm_model_hub
+yarn install
+#install pm2
+sudo yarn global add pm2
+
+# 等待一段时间以确保实例已完全启动
+sleep 30
+
+log "Run cn setup script"
+#如果是中国区则执行
+cd /home/ubuntu/llm_model_hub/backend/
+bash 0.setup-cn.sh
+
+# 尝试使用 IMDSv2 获取 token
+TOKEN=$(curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600")
+
+# Get the EC2 instance's public IP
+EC2_IP=$(curl -H "X-aws-ec2-metadata-token: $TOKEN" -s http://169.254.169.254/latest/meta-data/public-ipv4)
+# Get the current region and write it to the backend .env file
+REGION=$(curl -H "X-aws-ec2-metadata-token: $TOKEN" -s http://169.254.169.254/latest/meta-data/placement/region)
+
+echo "Get IP:$EC2_IP and Region:$REGION " >> "$LOG_FILE"
+# Generate a random string key
+RANDOM_KEY=$(openssl rand -base64 32 | tr -dc 'a-zA-Z0-9' | fold -w 32 | head -n 1)
+# Write the EC2_IP to frontend .env file
+rm /home/ubuntu/llm_model_hub/.env
+rm /home/ubuntu/llm_model_hub/backend/.env
+echo "REACT_APP_API_ENDPOINT=http://$EC2_IP:8000/v1" > /home/ubuntu/llm_model_hub/.env
+echo "REACT_APP_API_KEY=$RANDOM_KEY" >> /home/ubuntu/llm_model_hub/.env
+echo "REACT_APP_CALCULATOR=https://aws-gpu-memory-caculator.streamlit.app/" >> /home/ubuntu/llm_model_hub/.env
+
+## write sagemaker role
+echo "AK=" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "SK=" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "role=${SageMakerRoleArn}" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "region=$REGION" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "db_host=127.0.0.1" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "db_name=llm" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "db_user=llmdata" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "db_password=llmdata" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "api_keys=$RANDOM_KEY" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "HUGGING_FACE_HUB_TOKEN=${HuggingFaceHubToken}" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "WANDB_API_KEY=${WANDB_API_KEY}" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "WANDB_BASE_URL=${WANDB_BASE_URL}" >> /home/ubuntu/llm_model_hub/backend/.env
+echo "SWANLAB_API_KEY=${SWANLAB_API_KEY}" >> /home/ubuntu/llm_model_hub/backend/.env
+# Set proper permissions 
+sudo chown -R ubuntu:ubuntu /home/ubuntu/
+RANDOM_PASSWORD=$(openssl rand -base64 12 | tr -dc 'a-zA-Z0-9' | fold -w 8 | head -n 1) 
+aws ssm put-parameter --name "/modelhub/RandomPassword" --value "$RANDOM_PASSWORD" --type "SecureString" --overwrite --region "$REGION"
+cd /home/ubuntu/llm_model_hub/backend
+bash 01.setup.sh
+sleep 30
+#add user in db
+source ../miniconda3/bin/activate py311
+conda activate py311
+python3 users/add_user.py demo_user $RANDOM_PASSWORD default
+
+#build vllm image
+cd /home/ubuntu/llm_model_hub/backend/byoc
+bash build_and_push.sh
+sleep 5
+
+# 构建llamafactory镜像
+log "Building and pushing llamafactory image"
+cd /home/ubuntu/llm_model_hub/backend/docker
+bash build_and_push.sh || { log "Failed to build and push llamafactory image"; exit 1; }
+sleep 5
+
+#upload dummy tar.gz
+cd /home/ubuntu/llm_model_hub/backend/byoc
+../../miniconda3/envs/py311/bin/python startup.py 
+
+#start backend
+cd /home/ubuntu/llm_model_hub/backend/
+bash 02.start_backend.sh
+sleep 15
+
+#start frontend
+cd /home/ubuntu/llm_model_hub/
+pm2 start pm2run.config.js 
+echo "Webui=http://$EC2_IP:3000" 
+echo "username=demo_user"
+echo "RandomPassword=$RANDOM_PASSWORD" 
+echo "Run User Data Script Done! "
+echo "Webui=http://$EC2_IP:3000" >> "$LOG_FILE"
+echo "username=demo_user" >> "$LOG_FILE"
+echo "RandomPassword=$RANDOM_PASSWORD" >> "$LOG_FILE"
+echo "Run User Data Script Done! " >>  "$LOG_FILE"
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