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combine Docker docs

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cneud 2025-10-20 22:16:56 +02:00
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25
docs/docker.md
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@ -1,4 +1,8 @@
# 1. ocrd resource manager
## 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
@ -6,15 +10,18 @@
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)
### 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)
### 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
@ -22,3 +29,15 @@ Either use standalone CLI (2) or OCR-D CLI (3):
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 image
cd train
docker build -t model-training .
Run the Docker image
cd train
docker run --gpus all -v $PWD:/entry_point_dir model-training

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train/README.md
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@ -41,19 +41,3 @@ each class will be defined with a RGB value and beside images, a text file of cl
> Convert COCO GT or results for a single image to a segmentation map and write it to disk.
* [`ocrd-segment-extract-pages`](https://github.com/OCR-D/ocrd_segment/blob/master/ocrd_segment/extract_pages.py)
> Extract region classes and their colours in mask (pseg) images. Allows the color map as free dict parameter, and comes with a default that mimics PageViewer's coloring for quick debugging; it also warns when regions do overlap.
### Train using Docker
Build the Docker image:
```bash
cd train
docker build -t model-training .
```
Run Docker image
```bash
cd train
docker run --gpus all -v $PWD:/entry_point_dir model-training
```