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LMQL in Docker

Use LMQL in a Docker container.

Hereafter are the instructions to setup a docker container running the latest stable version of LMQL. The Dockerfile for the image building can be found in the scripts/ folder.


Important note: the current version of LMQL requires you to map the 3000 and 3004 ports of the docker container to your host machine in order to access the playground IDE and the LMQL backend.

Building the image

The following command lets you create an image with this Dockerfile:

docker build -t lmql-docker:latest .

Using GPU and local models

In order to use the local models, you first need to ensure that you have CUDA installed on your host machine and a supported Nvidia GPU. Be aware that this image has been tested with CUDA 12.1 on the host machine and will install PyTorch stable with CUDA 11.8 support. Before that, make sure that you have install docker gpu support. Then, finally, build the image using the following line:

docker build --build-arg GPU_ENABLED=true -t lmql-docker:cuda11.8 .

Note that the cuda11.8 tag is indicative and can be changed as you like.

Starting a container

To start a container using the image that you have built:

docker run -d -p 3000:3000 -p 3004:3004 lmql-docker:latest

Using environment variables

To override the default environment variables, you can do it through the docker run command like this example:

docker run -d -e OPENAI_API_KEY=<your openai api key> -p 3000:3000 -p 3004:3004 lmql-docker:latest

Otherwise, if you want to use the api.env file you can also mount the file as follow:

docker run -d -v $(PWD)/api.env:/lmql/.lmql/api.env -p 3000:3000 -p 3004:3004 lmql-docker:latest

Starting a container with GPU and local models

Make sure you have followed the image building step from the section Using GPU and local models. To start the docker container with access to the GPUs consider using the following command:

docker run --gpus all -d -p 3000:3000 -p 3004:3004 lmql-docker:cuda11.8

Where all means that you allocate all the GPUs to the docker container.

Note that here we expose the port 3000 and 3004 from the container to the port 3000 and 3004 from your machine. And we reuse the name lmql-docker:cuda11.8 as it is the value we previously used to build the image.