Run Detectron2 Inside Docker Container

We can make a docker container for Detectron 2, this container-based approach is much better than just creating Virtual Environments. The docker ensures whatever service is running on my machine can run on the machine all over the world without breaking.

We will create a Dockerfile which will first set up CUDA and then install detection 2. One main benefit of creating Dockerfile is that we can run it on a different kind of Linux distribution be it Ubuntu or Redhat. This is useful for deployment.

If you don’t have docker on the machine, set it up by using

On Ubuntu

sudo snap install docker

On Redhat

sudo yum install docker

Create Dockerfile

Step 1: Select a Base Image and install basic packages.

Here we select an image with Nvidia Cuda

FROM nvidia/cuda:10.1-cudnn7-devel

This next line prevents the installer from opening dialogue boxes during installation which stops the errors.

ENV DEBIAN_FRONTEND noninteractive

Install opencv, python3-dev and some other essentital packages

RUN apt-get update && apt-get install -y \
    python3-opencv ca-certificates python3-dev git wget sudo  \
    cmake ninja-build && \
 rm -rf /var/lib/apt/lists/*

Create a symbolic link for python3 to python, i.e now running python will by default invoke python3.

RUN ln -sv /usr/bin/python3 /usr/bin/python

Step 2: Create a non-root user

Docker by default gives us root privileges, but we don’t want to work in the root environment all the time so we will create a non-root user,

here, ARG is a build-time variable. They are only available from the moment they are ‘announced’ in the Dockerfile with an ARG instruction up to the moment when the image is built.

ARG USER_ID=1000
RUN useradd -m --no-log-init --system  --uid ${USER_ID} appuser -g sudo
RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
USER appuser
WORKDIR /home/appuser

Add local user path to the path

ENV PATH="/home/appuser/.local/bin:${PATH}"

Install Pip

RUN wget https://bootstrap.pypa.io/get-pip.py && \
   python3 get-pip.py --user && \
   rm get-pip.py

Step 3: Install Dependencies

See https://pytorch.org/ for other options if you use a different version of CUDA

Install dependencies for installing detectron-2

RUN pip install --user tensorboard
RUN pip install --user torch==1.6 torchvision==0.7 -f https://download.pytorch.org/whl/cu101/torch_stable.html
RUN pip install --user 'git+https://github.com/facebookresearch/fvcore'

Step 4: Install Detectron 2

Clone the detectron 2 repo from GitHub

RUN git clone https://github.com/facebookresearch/detectron2 detectron2_repo

Set FORCE_CUDA because during `docker build` Cuda is not accessible

ENV FORCE_CUDA="1"

This will by default build detectron2 for all common Cuda architectures and take a lot more time because inside docker build, there is no way to tell which architecture will be used.

ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing"
ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"

Install detectron 2 via pip

RUN pip install --user -e detectron2_repo

Change Working Directory to /home/appuser/detectron2_repo

WORKDIR /home/appuser/detectron2_repo

You can see the full Dockerfile here. Download it using

wget https://gist.githubusercontent.com/shashank2806/303e3ae90688c133816668179008dd1b/raw/36cc372857d6d12f3028814bb4732d111cfe6e25/Dockerfle

Build docker image

docker build --build-arg USER_ID=$UID -t detectron2:v0 .

Run it

docker run --gpus all -it \
	--shm-size=8gb --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
	--name=detectron2 detectron2:v0

Sometimes you me encounter an error like this while running the container

AssertionError:
Found no NVIDIA driver on your system. Please check that you
have an NVIDIA GPU and installed a driver from

In this case, we can use the nvidia-docker. Install nvidia-docker with help of this article on ubuntu

How to setup Docker and Nvidia-Docker 2.0 on Ubuntu 18.04

and then run this to build

sudo nvidia-docker build -t detectron2:v1

and then this command to run the container

sudo nvidia-docker run -it --name detectron2 detectron2

And that’s it. Happy Dockering!!