Leonurus-free

chatgpt-next-web

docker run -d -p 3000:3000 \
   -e CUSTOM_MODELS=-all,+Qwen1.5-110B-Chat-AWQ \
   -e BASE_URL=http://192.168.100.142:8200 \
   yidadaa/chatgpt-next-web
   
docker run -d -p 3001:3000 -e CUSTOM_MODELS=-all,+qwen2.5-72b-instruct-awq -e BASE_URL=http://192.168.100.160:8200 --name chatgpt-next-web --restart=always yidadaa/chatgpt-next-web

docker run -d -p 3002:3000 --name chatgpt-next-web-custom --restart=always yidadaa/chatgpt-next-web

stirling-pdf

docker run -d \
  -p 8080:8080 \
  -v /home/free/python_project/Stirling-PDF/trainingData:/usr/share/tessdata \
  -v /home/free/python_project/Stirling-PDF/extraConfigs:/configs \
  -v /home/free/python_project/Stirling-PDF/logs:/logs \
  -v /home/free/python_project/Stirling-PDF/customFiles:/customFiles \
  -e DOCKER_ENABLE_SECURITY=true \
  -e INSTALL_BOOK_AND_ADVANCED_HTML_OPS=true \
  -e LANGS=zh_CN \
  -e http_proxy=socks5://192.168.20.21:1080 \
  -e https_proxy=socks5://192.168.20.21:1080 \
  --name stirling-pdf-nologin \
  frooodle/s-pdf:latest-fat
  
docker run -d \
  -p 8080:8080 \
  -v /home/free/python_project/Stirling-PDF/trainingData:/usr/share/tessdata \
  -v /home/free/python_project/Stirling-PDF/extraConfigs:/configs \
  -v /home/free/python_project/Stirling-PDF/logs:/logs \
  -v /home/free/python_project/Stirling-PDF/customFiles:/customFiles \
  -e DOCKER_ENABLE_SECURITY=true \
  -e INSTALL_BOOK_AND_ADVANCED_HTML_OPS=true \
  -e SECURITY_ENABLE_LOGIN=true \
  -e SECURITY_INITIALLOGIN_USERNAME=admin \
  -e SECURITY_INITIALLOGIN_PASSWORD=123 \
  -e LANGS=zh_CN \
  --name stirling-pdf-login \
  frooodle/s-pdf:latest-fat
  

docker run -d -p 3002:8080 -v E:/temp/Stirling-PDF/trainingData:/usr/share/tessdata -v E:/temp/Stirling-PDF/extraConfigs:/configs -v E:/temp/Stirling-PDF/logs:/logs -v E:/temp/Stirling-PDF/customFiles:/customFiles -e DOCKER_ENABLE_SECURITY=true -e INSTALL_BOOK_AND_ADVANCED_HTML_OPS=true -e LANGS=zh_CN --name stirling-pdf-nologin --restart=always frooodle/s-pdf:0.27.0-fat

xinference

docker run -d -v F:/.xinference:/root/.xinference \
  -v F:/.xinference/.cache/huggingface:/root/.cache/huggingface \
  -v F:/.xinference/.cache/modelscope:/root/.cache/modelscope \
  -v F:/.xinference/logs:/workspace/xinference/logs \
  -e XINFERENCE_MODEL_SRC=modelscope \

  -p 9997:9997 \
  --gpus all \
  --name xinference \
  --restart=always \
  xprobe/xinference:v0.15.2 \
  xinference-local -H 0.0.0.0 \
  --log-level debug
  
docker run -d -v F:/.xinference:/root/.xinference -v F:/.xinference/.cache/huggingface:/root/.cache/huggingface -v F:/.xinference/.cache/modelscope:/root/.cache/modelscope -v F:/.xinference/logs:/workspace/xinference/logs -p 9997:9997 --gpus all --name xinference --restart=always xprobe/xinference:v0.15.2 xinference-local -H 0.0.0.0 --log-level debug

lobe-chat

docker run -d -p 3210:3210 \
  -e OPENAI_API_KEY=sk-123 \
  -e OPENAI_PROXY_URL=http://192.168.100.143:8090/v1 \
  --name lobe-chat \
  lobehub/lobe-chat:v1.19.33
  
docker run -d -p 3210:3210 -e OPENAI_API_KEY=sk-123 -e OPENAI_PROXY_URL=http://192.168.100.143:8090/v1 --name lobe-chat lobehub/lobe-chat:v1.19.33

vllm

docker pull vllm/vllm-openai:latest  
docker run -d
	--runtime nvidia \
	--gpus all \
	--name vllm_Qwen2.5-7B-Instruct \
    -v F:/.vllm/model:/root/model \
    -v F:/.vllm/vllm:/root/.cache/vllm \
    -v F:/.vllm/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
    -p 8888:8000 \
    --ipc=host \
    vllm/vllm-openai:v0.6.3.post1 \   # 以上是docker配置，以下是vllm配置
    --model /root/model/Qwen2.5-7B-Instruct \
    --gpu-memory-utilization 0.8 \
    --tensor-parallel-size 2 \
    --max-model-len 8129 \
    --served-model-name Qwen2.5-7B-Instruct 
    
    
docker run -d --runtime nvidia --gpus "device=1" --name vllm_Qwen2.5-7B-Instruct -v F:/.vllm/model:/root/model -v F:/.vllm/vllm:/root/.cache/vllm -v F:/.vllm/huggingface:/root/.cache/huggingface --env "HUGGING_FACE_HUB_TOKEN=<secret>" -p 8888:8000 --ipc=host vllm/vllm-openai:v0.6.6.post1 --model /root/model/Qwen2.5-7B-Instruct --gpu-memory-utilization 0.8 --tensor-parallel-size 1 --max-model-len 16384 --served-model-name Qwen2.5-7B-Instruct 

docker run -d --runtime nvidia --gpus all --name vllm_Qwen2.5-7B-Instruct-lora -v F:/.vllm/model:/root/model -v F:/.vllm/vllm:/root/.cache/vllm -v F:/.vllm/huggingface:/root/.cache/huggingface --env "HUGGING_FACE_HUB_TOKEN=<secret>" -p 8888:8000 --ipc=host vllm/vllm-openai:v0.6.3.post1 --model /root/model/outputs-Qwen2.5-7B-Instruct-lora --gpu-memory-utilization 0.8 --tensor-parallel-size 2 --max-model-len 8129 --served-model-name Qwen2.5-7B-Instruct-lora 
    
 
 # 采用vllm.entrypoints.openai.api_server启动
 
CUDA_VISIBLE_DEVICES=2 python -m vllm.entrypoints.openai.api_server \
    --model /home/ubuntu/luo/Corrective_fintune_v1/models/Qwen/outputs-Qwen2.5-7B-Instruct-lora \
    --tokenizer /home/ubuntu/luo/Corrective_fintune_v1/models/Qwen/outputs-Qwen2.5-7B-Instruct-lora \
    --max-model-len 2048 \
    --gpu-memory-utilization 1 \
    --enforce-eager \
    --dtype half \
    --port 8888
    


# DeepSeek-R1-Distill-Qwen-7B

docker run -d --runtime nvidia --gpus "device=1" --name vllm-DeepSeek-R1-Distill-Qwen-7B -v F:/.vllm/model:/root/model -v F:/.vllm/vllm:/root/.cache/vllm -v F:/.vllm/huggingface:/root/.cache/huggingface --env "HUGGING_FACE_HUB_TOKEN=<secret>" -p 8888:8000 --ipc=host vllm/vllm-openai:v0.6.6.post1 --model /root/model/DeepSeek-R1-Distill-Qwen-7B --gpu-memory-utilization 0.8 --tensor-parallel-size 1 --max-model-len 16384 --served-model-name DeepSeek-R1-Distill-Qwen-7B

postman

curl http://192.168.100.50:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
    "model": "/home/data/models/Qwen1___5-7B-Chat",
    "messages": [
    {"role": "system", "content": "你是一个有用的助手."},
    {"role": "user", "content": "告诉我关于尼米兹号航母的的一些知识."}
    ]
}'

omniparse

# 构建镜像
docker build -t omniparse .

# 启动镜像
docker run -d --runtime nvidia --gpus "device=1" -p 8001:8001 -v F:/.omniparse:/tmp --name omniparse omniparse:latest

mysql

docker run \
	-d \
   	--name mysql_9.1.0 \
   	-e MYSQL_ROOT_PASSWORD=1qaz2wsx \
   	-p 33306:3306 \
   	-v F:/.mysql:/etc/mysql/conf.d \
   	-v F:/.mysql:/var/lib/mysql \
   	--restart=always \
   	mysql:9.1.0
   	
# 注意mysql9.1.0中删除了以下服务器选项和变量：
–mysql本机密码服务器选项
–mysql本机密码代理用户服务器选项
–default_authentication_plugin服务器系统变量
9版本推荐使用caching_sha2_password远程密码连接
   	
docker run -d --name mysql_8.0 -e MYSQL_ROOT_PASSWORD=1qaz2wsx -p 33306:3306 -v F:/.mysql:/etc/mysql/conf.d -v F:/.mysql:/var/lib/mysql --restart=always mysql:8.0

docker exec -it mysql_8.0 bin/bash

dev_env

docker run -d \
  --privileged
  --runtime nvidia --gpus all \
  -p 98080:8080 \ 
  -p 90022:22 \
  -p 97000:7000 \
  -p 98000:8000 \
  -v F:/pythonProject:/home/pythonProject \
  --restart=always \
  --name dev_env \
  dev_env_cuda12.5:latest

docker run -d --privileged --runtime nvidia --gpus "device=1" -p 18080:8080 -p 10022:22 -p 17000:7000 -p 18000:8000 -v F:/pythonProject:/home/pythonProject --restart=always --name dev_env dev_env_conda_ssh:12.6.3-cudnn-devel-ubuntu22.04

自己在容器中安装ssh

DataTransfer

docker build -t datatransfer:v1 .

docker run -d --restart=always --name DataTransfer datatransfer:v1

NotesSynchronization

docker build -t notes_synchronization:v1 .

docker run -d -v D:/MyNotes:/app/MyNotes --restart=always --name notes_synchronization notes_synchronization:v1

onlyoffice

sudo docker run -i -t -d -p 80:80 --restart=always \
    -v F:/.onlyoffice/DocumentServer/logs:/var/log/onlyoffice  \
    -v F:/.onlyoffice/DocumentServer/data:/var/www/onlyoffice/Data  \
    -v F:/.onlyoffice/DocumentServer/lib:/var/lib/onlyoffice \
    -v F:/.onlyoffice/DocumentServer/db:/var/lib/postgresql -e JWT_SECRET=false onlyoffice/documentserver:8.3.0
    

docker run -i -t -d -p 80:80 --restart=always -v F:/.onlyoffice/DocumentServer/logs:/var/log/onlyoffice  -v F:/.onlyoffice/DocumentServer/data:/var/www/onlyoffice/Data  -v F:/.onlyoffice/DocumentServer/lib:/var/lib/onlyoffice -v F:/.onlyoffice/DocumentServer/db:/var/lib/postgresql -e JWT_SECRET=false onlyoffice/documentserver:8.3.0