docker-compose.yml

version: "2.4"
services:
  GNNforPysics:
    container_name: 'GNNforPysics'
    build: .
    image: 'gnnforpysics/tensorflow:22.02-tf1-py3'
    environment:
      - TF_FORCE_GPU_ALLOW_GROWTH=true
    volumes:
      # - $PWD:/mydir
      - ./:/mydir
    working_dir: /mydir
    ports:
      - 8888:8888
      - 6006:6006
    shm_size: '2gb'
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              # device_ids: ['0']
              count: 1
              capabilities: [gpu]
    # command: ls -al
    # command: nvidia-smi
    command: jupyter lab --ip=0.0.0.0 --allow-root --no-browser
    # command: python /home/run.py
    # command: python -m learning_to_simulate.train --data_path=./tmp/datasets/Water-3D --model_path=./tmp/models/Water-3D --num_steps 500000
    # command: python -m learning_to_simulate.train --mode="eval_rollout" --data_path=./tmp/datasets/Water-3D --model_path=./tmp/models/Water-3D --output_path=./tmp/rollouts/Water-3D
    # command: python -m learning_to_simulate.render_rollouts --rollout_path=./tmp/rollouts/Water-3D --test_num=100
    # command: >
    #   bash -c "
    #     python -m learning_to_simulate.train --data_path=./tmp/datasets/Water-3D --model_path=./tmp/models/Water-3D --num_steps 10000 &&
    #     python -m learning_to_simulate.train --mode="eval_rollout" --data_path=./tmp/datasets/Water-3D --model_path=./tmp/models/Water-3D --output_path=./tmp/rollouts/Water-3D $$
    #     "
    # command: >
    #   bash -c "
    #     python -m learning_to_simulate.train --data_path=./tmp/datasets/Water-3D --model_path=./tmp/models/Water-3D --num_steps 10000 &&
    #     python -m learning_to_simulate.train --mode="eval_rollout" --data_path=./tmp/datasets/Water-3D --model_path=./tmp/models/Water-3D --output_path=./tmp/rollouts/Water-3D $$
    #     python -m learning_to_simulate.render_rollouts --rollout_path=./tmp/rollouts/Water-3D --test_num=100
    #     "