The standard way to run Grobid is to use Docker for starting a Grobid server.
For installing Docker on your system, please visit the official Docker documentation here.
For convenience, we provide two docker images:
- the full image provides the best accuracy, because it includes all the required python and TensorFlow libraries, GPU support and all Deep Learning model resources. However it requires more resources, ideally a GPU (it will be automatically detected on Linux). If you have a limited amount of PDF, a good machine, and prioritize accuracy, use this Grobid flavor. To run this version of Grobid, the command is:
docker run --rm --gpus all --init --ulimit core=0 -p 8070:8070 grobid/grobid:0.8.0
- the lightweight image offers best runtime performance, memory usage and Docker image size. However, it does not use some of the best performing models in term of accuracy. If you have a lot of PDF to process, a low resource system, and accuracy is not so important, use this flavor:
docker run --rm --init --ulimit core=0 -p 8070:8070 lfoppiano/grobid:0.8.0
More documentation on the Docker images can be found here.
From there, you can check on your browser if the service works fine by accessing the welcome page of the service console, available at the URL http://localhost:8070. The GROBID server can be used via the web service.