eynollah/docs/docker.md

Inference with Docker

docker pull ghcr.io/qurator-spk/eynollah:latest

1. ocrd resource manager

(just once, to get the models and install them into a named volume for later re-use)

vol_models=ocrd-resources:/usr/local/share/ocrd-resources
docker run --rm -v $vol_models ocrd/eynollah ocrd resmgr download ocrd-eynollah-segment default

Now, each time you want to use Eynollah, pass the same resources volume again. Also, bind-mount some data directory, e.g. current working directory $PWD (/data is default working directory in the container).

Either use standalone CLI (2) or OCR-D CLI (3):

2. standalone CLI

(follow self-help, cf. readme)

docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah binarization --help
docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah layout --help
docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah ocr --help

3. OCR-D CLI

(follow self-help, cf. readme and https://ocr-d.de/en/spec/cli)

docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-eynollah-segment -h
docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-sbb-binarize -h

Alternatively, just "log in" to the container once and use the commands there:

docker run --rm -v $vol_models -v $PWD:/data -it ocrd/eynollah bash

Training with Docker

Build the Docker training image

cd train
docker build -t model-training .

Run the Docker training image

cd train
docker run --gpus all -v $PWD:/entry_point_dir model-training