MinerU provides a convenient Docker deployment method, which helps quickly set up the environment and solve some tricky environment compatibility issues.
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/global/Dockerfile
docker build -t mineru-vllm:latest -f Dockerfile .
[!TIP] The Dockerfile uses
vllm/vllm-openai:v0.10.1.1as the base image by default. This version of vLLM v1 engine has limited support for GPU models. If you cannot use vLLM accelerated inference on Turing and earlier architecture GPUs, you can resolve this issue by changing the base image tovllm/vllm-openai:v0.10.2.
MinerU's Docker uses vllm/vllm-openai as the base image, so it includes the vllm inference acceleration framework and necessary dependencies by default. Therefore, on compatible devices, you can directly use vllm to accelerate VLM model inference.
[!NOTE] Requirements for using
vllmto accelerate VLM model inference:
- Device must have Turing architecture or later graphics cards with 8GB+ available VRAM.
- The host machine's graphics driver should support CUDA 12.8 or higher; You can check the driver version using the
nvidia-smicommand.- Docker container must have access to the host machine's graphics devices.
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 -p 7860:7860 -p 8000:8000 \
--ipc=host \
-it mineru-vllm:latest \
/bin/bash
After executing this command, you will enter the Docker container's interactive terminal with some ports mapped for potential services. You can directly run MinerU-related commands within the container to use MinerU's features.
You can also directly start MinerU services by replacing /bin/bash with service startup commands. For detailed instructions, please refer to the Start the service via command.
We provide a compose.yaml file that you can use to quickly start MinerU services.
# Download compose.yaml file
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
[!NOTE]
- The
compose.yamlfile contains configurations for multiple services of MinerU, you can choose to start specific services as needed.- Different services might have additional parameter configurations, which you can view and edit in the
compose.yamlfile.- Due to the pre-allocation of GPU memory by the
vllminference acceleration framework, you may not be able to run multiplevllmservices simultaneously on the same machine. Therefore, ensure that other services that might use GPU memory have been stopped before starting thevlm-vllm-serverservice or using thevlm-vllm-enginebackend.
connect to vllm-server via vlm-http-client backend
docker compose -f compose.yaml --profile vllm-server up -d
[!TIP] In another terminal, connect to vllm server via http client (only requires CPU and network, no vllm environment needed)
> mineru -p <input_path> -o <output_path> -b vlm-http-client -u http://<server_ip>:30000 > ``` --- ### Start Web API servicebash docker compose -f compose.yaml --profile api up -d
>[!TIP] >Access `http://<server_ip>:8000/docs` in your browser to view the API documentation. --- ### Start Gradio WebUI servicebash docker compose -f compose.yaml --profile gradio up -d ``` [!TIP]
- Access
http://<server_ip>:7860in your browser to use the Gradio WebUI.- Access
http://<server_ip>:7860/?view=apito use the Gradio API.