DB-GPT/docs/getting_started/install/docker/docker.md

Docker Install

Docker (Experimental)

1. Preparing docker images

Pull docker image from the Eosphoros AI Docker Hub

docker pull eosphorosai/dbgpt:latest

(Optional) Building Docker image

bash docker/build_all_images.sh

Review images by listing them:

docker images|grep "eosphorosai/dbgpt"

Output should look something like the following:

eosphorosai/dbgpt-allinone       latest    349d49726588   27 seconds ago       15.1GB
eosphorosai/dbgpt                latest    eb3cdc5b4ead   About a minute ago   14.5GB

eosphorosai/dbgpt is the base image, which contains the project's base dependencies and a sqlite database. eosphorosai/dbgpt-allinone build from eosphorosai/dbgpt, which contains a mysql database.

You can pass some parameters to docker/build_all_images.sh.

bash docker/build_all_images.sh \
--base-image nvidia/cuda:11.8.0-runtime-ubuntu22.04 \
--pip-index-url https://pypi.tuna.tsinghua.edu.cn/simple \
--language zh

You can execute the command bash docker/build_all_images.sh --help to see more usage.

2. Run docker container

Run with local model and SQLite database

docker run --ipc host --gpus all -d \
    -p 5000:5000 \
    -e LOCAL_DB_TYPE=sqlite \
    -e LOCAL_DB_PATH=data/default_sqlite.db \
    -e LLM_MODEL=vicuna-13b-v1.5 \
    -e LANGUAGE=zh \
    -v /data/models:/app/models \
    --name dbgpt \
    eosphorosai/dbgpt

Open http://localhost:5000 with your browser to see the product.

• -e LLM_MODEL=vicuna-13b-v1.5, means we use vicuna-13b-v1.5 as llm model, see /pilot/configs/model_config.LLM_MODEL_CONFIG
• -v /data/models:/app/models, means we mount the local model file directory /data/models to the docker container directory /app/models, please replace it with your model file directory.

You can see log with command:

docker logs dbgpt -f

Run with local model and MySQL database

docker run --ipc host --gpus all -d -p 3306:3306 \
    -p 5000:5000 \
    -e LOCAL_DB_HOST=127.0.0.1 \
    -e LOCAL_DB_PASSWORD=aa123456 \
    -e MYSQL_ROOT_PASSWORD=aa123456 \
    -e LLM_MODEL=vicuna-13b-v1.5 \
    -e LANGUAGE=zh \
    -v /data/models:/app/models \
    --name db-gpt-allinone \
    db-gpt-allinone

Open http://localhost:5000 with your browser to see the product.

• -e LLM_MODEL=vicuna-13b-v1.5, means we use vicuna-13b-v1.5 as llm model, see /pilot/configs/model_config.LLM_MODEL_CONFIG
• -v /data/models:/app/models, means we mount the local model file directory /data/models to the docker container directory /app/models, please replace it with your model file directory.

You can see log with command:

docker logs db-gpt-allinone -f

Run with openai interface

PROXY_API_KEY="You api key"
PROXY_SERVER_URL="https://api.openai.com/v1/chat/completions"
docker run --gpus all -d -p 3306:3306 \
    -p 5000:5000 \
    -e LOCAL_DB_HOST=127.0.0.1 \
    -e LOCAL_DB_PASSWORD=aa123456 \
    -e MYSQL_ROOT_PASSWORD=aa123456 \
    -e LLM_MODEL=proxyllm \
    -e PROXY_API_KEY=$PROXY_API_KEY \
    -e PROXY_SERVER_URL=$PROXY_SERVER_URL \
    -e LANGUAGE=zh \
    -v /data/models/text2vec-large-chinese:/app/models/text2vec-large-chinese \
    --name db-gpt-allinone \
    db-gpt-allinone

• -e LLM_MODEL=proxyllm, means we use proxy llm(openai interface, fastchat interface...)
• -v /data/models/text2vec-large-chinese:/app/models/text2vec-large-chinese, means we mount the local text2vec model to the docker container.

Open http://localhost:5000 with your browser to see the product.