Docker Deployment

Docker image preparation

There are two ways to prepare a Docker image. 1. Pull from the official image 2. Build locally. You can choose any one during actual use.

1.Pulled from the official image repository, Eosphoros AI Docker Hub

docker pull eosphorosai/dbgpt:latest

2.local build(optional)

bash docker/build_all_images.sh

Check the Docker image

# command
docker images | grep "eosphorosai/dbgpt"

# result
--------------------------------------------------------------------------------------
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 project dependencies and the sqlite database. The eosphorosai/dbgpt-allinone image is built from eosphorosai/dbgpt, which contains a MySQL database. Of course, in addition to pulling the Docker image, the project also provides Dockerfile files, which can be built directly through scripts in DB-GPT. Here are the build commands:

bash docker/build_all_images.sh

When using it, you need to specify specific parameters. The following is an example of specifying parameter construction:

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 view the specific usage through the command bash docker/build_all_images.sh --help

Run Docker container

Run through Sqlite database

docker run --ipc host --gpus all -d \
-p 5670:5670 \
-e LOCAL_DB_TYPE=sqlite \
-e LOCAL_DB_PATH=data/default_sqlite.db \
-e LLM_MODEL=glm-4-9b-chat \
-e LANGUAGE=zh \
-v /data/models:/app/models \
--name dbgpt \
eosphorosai/dbgpt

Open the browser and visit http://localhost:5670

• -e LLM_MODEL=glm-4-9b-chat, which means the base model uses glm-4-9b-chat. For more model usage, you can view the configuration in /pilot/configs/model_config.LLM_MODEL_CONFIG.
• -v /data/models:/app/models, specifies the model file to be mounted. The directory /data/models is mounted in /app/models of the container. Of course, it can be replaced with other paths.

After the container is started, you can view the logs through the following command

docker logs dbgpt -f

Run through MySQL database

docker run --ipc host --gpus all -d -p 3306:3306 \
-p 5670:5670 \
-e LOCAL_DB_HOST=127.0.0.1 \
-e LOCAL_DB_PASSWORD=aa123456 \
-e MYSQL_ROOT_PASSWORD=aa123456 \
-e LLM_MODEL=glm-4-9b-chat \
-e LANGUAGE=zh \
-v /data/models:/app/models \
--name db-gpt-allinone \
db-gpt-allinone

Open the browser and visit http://localhost:5670

• -e LLM_MODEL=glm-4-9b-chat, which means the base model uses glm-4-9b-chat. For more model usage, you can view the configuration in /pilot/configs/model_config.LLM_MODEL_CONFIG.
• -v /data/models:/app/models, specifies the model file to be mounted. The directory /data/models is mounted in /app/models of the container. Of course, it can be replaced with other paths.

After the container is started, you can view the logs through the following command

docker logs db-gpt-allinone -f

Run through the OpenAI proxy model

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 5670:5670 \
-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, set the model to serve the third-party model service API, which can be openai or fastchat interface.
• -v /data/models/text2vec-large-chinese:/app/models/text2vec-large-chinese, sets the knowledge base embedding model to text2vec

Open the browser and visit http://localhost:5670