Docling inference server

This project provides a FastAPI wrapper around the docling document parser to make it easier to use in distributed production environments.

Running

The easiest way to run this project is using docker. There are two image families, one for cuda machines and one for cpu:

• Cuda: ghcr.io/aidotse/docling-inference:rev
• CPU: ghcr.io/aidotse/docling-inference:cpu-rev

# Create volumes to not have to download models every time
docker volume create hf_cache
docker volume create ocr_cache

# Run the container
docker run -d \
  --gpus all \
  -p 8080:8080 \
  -e NUM_WORKERS=8 \
  -v hf_cache:/root/.cache/huggingface \
  -v ocr_cache:/root/.EasyOCR \
  ghcr.io/aidotse/docling-inference:latest

Docker compose

services:
  docling-inference:
    image: ghcr.io/aidotse/docling-inference:latest
    ports:
      - 8080:8080
    environment:
      - NUM_WORKERS=8
    volumes:
      - hf_cache:/root/.cache/huggingface
      - ocr_cache:/root/.EasyOCR
    restart: always
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

volumes:
  hf_cache:
  ocr_cache:

Local python

Dependencies are handled with pypoetry in this project. Follow their installation instructions if you do not have it.

# Install the dependencies
poetry install
# OR
poetry install --with cuda -E cuda



# Activate the shell
poetry shell

# Start the server
python src/main.py

Building

Build the project docker image with one of the following commands

• Cuda: docker build -t ghcr.io/aidotse/docling-inference:dev .
• CPU: docker build -f Dockerfile.cpu -t ghcr.io/aidotse/docling-inference:dev .

Configuration

Configuration is handled through environment variables. Here is a list of the available configuration variables. They are defined in src/config.py

• NUM_WORKERS: The number of processes to run
• LOG_LEVEL: The lowest level of logs to display. One of DEBUG, INFO, WARNING, CRITICAL, ERROR
• DEV_MODE: Sets automatic reload of the service. Useful during development
• PORT: The port to run the server on
• AUTH_TOKEN: Token to use for authentication. Token is expected in the Authorization: Bearer: <token> format in the request header. The service is unprotected if this option is omitted