Skip to content

Latest commit

 

History

History
44 lines (33 loc) · 1.94 KB

docker_build_instructions.md

File metadata and controls

44 lines (33 loc) · 1.94 KB
Raw

Pytorch Dense Correspondence inside Docker

Introduction

We highly recommend using a docker environment to work with this repo. All development for this project has used this setup.

The docker image essentially packages all dependencies in a safe environment. The scripts we provide will externally mount our source code, and our data, into the docker environment.

Most source code for this project is in Python and so once the docker image is built we won't need any compiling.

Quickstart

The following is all of the steps to build a docker image for pytorch-dense-correspondence from a fresh Ubuntu installation:

  1. Install Docker for Ubuntu
  • Make sure to sudo usermod -aG docker your-user and then not run below docker scripts as sudo
  1. Install nvidia-docker2. You can test that your nvidia-docker installation is working by running
nvidia-docker run --rm nvidia/cuda nvidia-smi

If you get errors about nvidia-modprobe not being installed, install it by running

sudo apt-get install nvidia-modprobe

and then restart your machine.

Note: It's possible that the latest nvidia-docker doesn't include nvidia-smi. Try testing your docker installation by running

nvidia-docker run --rm nvidia/cuda:10.0-base nvidia-smi.

instead.

  1. Clone, setup, and build docker image for pytorch-dense-correspondence. If using clone via ssh, you need to have ssh keys setup to clone the submodules. Make sure that these ssh keys don't have a password, otherwise it will not work. Cloning via https should be OK.
git clone git@github.com:RobotLocomotion/pytorch-dense-correspondence.git
cd pytorch-dense-correspondence
git submodule update --init --recursive
cd docker
./docker_build.py

You're done with setup!

Now there should be a docker image called <username>-pytorch-dense-correspondence on your machine.