Running SoftGym inside a docker

We provide both Dockerfile and pre-built Docker container for compiling SoftGym. Part of the docker solutions are borrowed from PyFlex

Prerequisite

Install docker-ce
Install nvidia-docker
Install Anaconda
Install Pybind11 using conda install pybind11

Running pre-built Dockerfile

Pull the pre-built docker file

sudo docker pull xingyu/softgym

Assuming you are using conda, using the following command to run docker, which will mount the python environment and SoftGym into the docker container. Make sure you have replaced PATH_TO_SoftGym and PATH_TO_CONDA with the corresponding paths (make sure to use absolute path!).

nvidia-docker run \
  -v PATH_TO_SoftGym:/workspace/softgym \
  -v PATH_TO_CONDA:PATH_TO_CONDA \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  --gpus all \
  -e DISPLAY=$DISPLAY \
  -e QT_X11_NO_MITSHM=1 \
  -it xingyu/softgym:latest bash

As an example:

nvidia-docker run \
  -v ~/softgym:~/softgym \
  -v ~/software/miniconda3/:~/software/miniconda3/ \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  --gpus all \
  -e DISPLAY=$DISPLAY \
  -e QT_X11_NO_MITSHM=1 \
  -it xingyu/softgym:latest bash

This solution follows this tutorial for running GL and CUDA application inside the docker. It is important to mount the conda path inside the docker container to be exactly the same as in your home machine.

Now you are in the Docker environment. Go to the softgym directory and compile PyFlex

export PATH="PATH_TO_CONDA/bin:$PATH"
. ./prepare_1.0.sh && ./compile_1.0.sh

Running with Dockerfile

We also posted the Dockerfile. To generate the pre-built file, download the Dockerfile in this directory and run

docker build -t softgym .

in the directory that contains the Dockerfile.