thyroxine / deeplytough-compose Goto Github PK

How to run with DeeplyTough with docker-compose

1. Install Docker Engine 19 or later
2. Install Docker Compose
3. Install NVIDIA Container toolkit, including essential package nvidia-docker2
4. Add your user to docker group

usermod -aG docker $USER

Use pacman and AUR if installing for Arch linux.

Test your installation by

docker run --rm --gpus all nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04 nvidia-smi

You should see all your GPUs in nvidia-smi.

git clone https://github.com/BenevolentAI/DeeplyTough

docker-compose build

mkdir -p datasets
export STRUCTURE_DATA_DIR=$(pwd)/datasets
./DeeplyTough/datasets_downloader.sh 

Wait some time for downloading....

Create results folder

mkdir -p results

If you need any of results from repository, just copy them to this folder. If you need custom network models, create networks folder and uncomment the corresponding volume in YAML file.

Execute any commands interactively.

docker-compose run --rm deeplytough
source activate deeplytough
python <your command>

docker-compose -f docker-compose-batch.yml run --rm deeplytough

Edit command in YAML file for you needs.

You can run program in background, and it will restart automatically if failed.

docker-compose -f docker-compose-batch.yml up -d

docker-compose logs

docker-compose -f docker-compose-batch.yml down

If you do not want to build container by yourself, you can use container from GitHub Docker Registry.

1. (Optional) Install docker-credential-secretservice to store your passwords securely.
2. Obtain a GitHub token https://github.com/settings/tokens/new with read:packages permission
3. Save your token to file token.txt
4. Login to registry by command:

cat token.txt | docker login https://docker.pkg.github.com -u <your username> --password-stdin

5. Run batch version with container from repository:

docker-compose -f docker-compose-repo.yml run --rm deeplytough