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43 lines
1.5 KiB
Markdown
43 lines
1.5 KiB
Markdown
## Inference with Docker
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docker pull ghcr.io/qurator-spk/eynollah:latest
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### 1. ocrd resource manager
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(just once, to get the models and install them into a named volume for later re-use)
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vol_models=ocrd-resources:/usr/local/share/ocrd-resources
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docker run --rm -v $vol_models ocrd/eynollah ocrd resmgr download ocrd-eynollah-segment default
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Now, each time you want to use Eynollah, pass the same resources volume again.
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Also, bind-mount some data directory, e.g. current working directory $PWD (/data is default working directory in the container).
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Either use standalone CLI (2) or OCR-D CLI (3):
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### 2. standalone CLI
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(follow self-help, cf. readme)
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docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah binarization --help
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docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah layout --help
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docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah ocr --help
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### 3. OCR-D CLI
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(follow self-help, cf. readme and https://ocr-d.de/en/spec/cli)
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docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-eynollah-segment -h
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docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-sbb-binarize -h
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Alternatively, just "log in" to the container once and use the commands there:
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docker run --rm -v $vol_models -v $PWD:/data -it ocrd/eynollah bash
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## Training with Docker
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Build the Docker image
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cd train
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docker build -t model-training .
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Run the Docker image
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cd train
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docker run --gpus all -v $PWD:/entry_point_dir model-training
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