ViCoS toolbox is a collection of tools packaged as a docker image, alongside the machinery to manage installation and inference/training scripts for models.
Running the image is pretty straightforward as far as docker is concerned. You can try running the toolbox with the following command, but anything you do will not be saved:
docker run --rm -it --publish 443:443 --device nvidia.com/gpu=all vicoslab/toolbox
The toolbox should then be accessible in your browser at localhost.
Additionally, you may also:
- publish the public inference endpoint (container port 444 by default)
- run as your own user, to prevent permissions in your mounted folders from getting messed up
- mount your datasets to
/data - mount a cache folder to
/cache - mount data which should be persisted to
/persist - one thing which may be needed during training is larger shm sizes (i.e. running the toolbox with something like
--shm-size=2gbor larger)
docker run --rm -it \
--publish 443:443 --publish 444:444 \
--device nvidia.com/gpu=all \
--user $(id -u):$(id -g) \
-v /path/to/datasets:/data \
-v ~/.cache/toolbox:/cache \
--mount type=volume,src=toolbox-persist,dst=/persist \
toolboxWhen deploying, you need to additionally consider the following env variables:
DOMAIN=localhost:443
DOMAIN_PUBLIC=localhost:444
HOST=https://localhost/app/label-studio
PUBLIC_URL=https://localhost/app/label-studio
LABEL_STUDIO_HOST=https://localhost/app/label-studioIf you do not intend to host a public inference endpoint, you may omit DOMAIN_PUBLIC, although in any case the toolbox performs absolutely 0 authentication. The HOST,
PUBLIC_URL and LABEL_STUDIO_HOST from label studio do not seem to play well with ports.
Currently, everything is part of a single image to simplify deployment (since the apps are only single-tenant, its easier to manage as a whole unit). The docker build is multistage and utilises caching where possible, so building shouldn't take too long, except the initial build, which may take up to 15 minutes.