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@ -33,24 +33,18 @@ Extract model archive:
tar -xzf models.tar.gz tar -xzf models.tar.gz
``` ```
Copy [config file](qurator/sbb_ner/webapp/config.json) into working directory.
Set USE_CUDA environment variable to True/False depending on GPU availability.
Run webapp directly: Run webapp directly:
``` ```
env FLASK_APP=qurator/sbb_ner/webapp/app.py env FLASK_ENV=development env USE_CUDA=True flask run --host=0.0.0.0 env CONFIG=config.json env FLASK_APP=qurator/sbb_ner/webapp/app.py env FLASK_ENV=development env USE_CUDA=True/False flask run --host=0.0.0.0
``` ```
Set USE_CUDA=False, if you do not have a GPU available/installed.
For production purposes rather use For production purposes rather use
``` ```
env USE_CUDA=True/False gunicorn --bind 0.0.0.0:5000 qurator.sbb_ner.webapp.wsgi:app env CONFIG=config.json env USE_CUDA=True/False gunicorn --bind 0.0.0.0:5000 qurator.sbb_ner.webapp.wsgi:app
```
If you want to use a different model configuration file:
```
env USE_CUDA=True/False env CONFIG=`realpath ./my-config.json` gunicorn --bind 0.0.0.0:5000 qurator.sbb_ner.webapp.wsgi:app
``` ```
# Docker # Docker
@ -69,7 +63,6 @@ docker run -ti --rm=true --mount type=bind,source=data/konvens2019,target=/usr/s
Make sure that your GPU is correctly set up and that nvidia-docker has been installed. Make sure that your GPU is correctly set up and that nvidia-docker has been installed.
``` ```
docker build --build-arg http_proxy=$http_proxy -t qurator/webapp-ner-gpu -f Dockerfile . docker build --build-arg http_proxy=$http_proxy -t qurator/webapp-ner-gpu -f Dockerfile .
``` ```