removed private data

docker/.env

@@ -1,33 +0,0 @@
-# The name of the docker-compose project
-COMPOSE_PROJECT_NAME=dvats-macu
-# The user ID you are using to run docker-compose
-#G3 1004
-#G4 1004
-USER_ID=1004
-# The group ID you are using to run docker-compose (you can get it with id -g in a terminal)
-GROUP_ID=1004
-# The user name assigned to the user id
-USER_NAME=macu
-# The port from which you want to access Jupyter lab
-JUPYTER_PORT=8866
-#127.0.0.1:8888
-#http://127.0.0.1:8888/?token=c27a0876fb83515d508e140531ecabde308b5f6db9e5a0fa
-# The token used to access (like a password)
-JUPYTER_TOKEN=patataFeliz
-# The path toz your data files to train/test the models
-LOCAL_DATA_PATH=/home/macu/work_dir
-# The W&B entity
-WANDB_ENTITY=mi-santamaria
-# The W&B project
-WANDB_PROJECT=deepvats
-# The W&B personal API key (see https://wandb.ai/authorize)
-WANDB_API_KEY=c071bbe845e7da7f602d784bc088a9596206ba69
-# List of comma separated GPU indices that will be available in the container (by default only 0, the first one)
-CUDA_VISIBLE_DEVICES=1 #0,1,2
-# Github PAT (see https://docs.github.com/en/github/authenticating-to-github/keeping-your-account-and>
-GH_TOKEN=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
-# Port in which you want Rstudio server to be deployed (for developing in the front end)
-RSTUDIO_PORT=8867
-# Password to access the Rstudio server
-RSTUDIO_PASSWD=patataFeliz
-