GPU Accelerated Application Deployment with NVIDIA-Docker

When it comes to deep learning model development and training, personally for me, the majority of the time is spent on data pre-processing, then for setting up the development environment.  Cloud based development environments such as Azure DLVM, Google CoLab etc. are very good options to go with when you don’t have much time to spend on installing all the required packages for your workstation. But, there are times that we want to do the development on our machines and train/deploy in another place (may be on the client’s environment, for a machine with a better GPU for faster training or to train on a Kubernetes cluster). Docker comes handy in these scenarios.

Docker provides both hardware and software encapsulation by allowing portable deployment. If you are a data scientist/ machine learning guy or a deep learning developer, I strongly recommend you to give it a try with docker and I’m pretty sure that’ll make your life so easy.

Alright! That’s about docker! Let’s assume now you are using docker for deploying your deep learning applications and you want to use docker to ship your deep learning model to a remote computer that is having a powerful GPU, which allows you to use large mini-batch sizes and speedup your training process. Though docker containers solve the problem of framework dependencies and platform dependencies it is also hardware-agnostic. This creates a problem!

Have you ever tried to access the GPU resource on the host computer from a program running inside a docker container? Sadly, Docker does not natively support NVIDIA GPUs within containers.

The early work around was installing the Nvidia drivers inside the docker container. It’s bit of a hassle as the driver version installed in the container should match the driver on the host.

For making docker images that uses GPU resources more portable, Nvidia has introduced nvidia-docker!

nvidia-docker

NVIDIA-Docker plugin enables GPU accelerated application deployment

Nvidia-docker is a wrapper around the docker command that mounts the GPU on the host machine with the docker container. The only thing you should pay your attention is the CUDA version you want to use.

So, in which scenarios you can use this? In my case, nvidia-docker comes handy for me when I’m running my experiment on a cluster which is having a higher GPU power. What I do is just containerize all my code into a docker and run on the remote with nvidia-docker. (Windows guys… nvidia-docker is not still available for windows hosts. Not sure if that is in the development timeline or not 😀 )

Here’s the official GitHub on nvidia-docker. Just install it at make sure to restart your docker engine and make sure nvidia-docker the default docker run-time. Then rest is the same as building and running a typical docker.

Here’s a simple docker file I wrote for containerizing my PyTorch code. I’ve used CUDA 9.1.  You can modify this for your need.

FROM nvidia/cuda:9.1-base-ubuntu16.04

# Install some basic utilities
RUN apt-get update && apt-get install -y \
curl \
ca-certificates \
sudo \
git \
bzip2 \
libx11-6 \
&& rm -rf /var/lib/apt/lists/*

# Create a working directory
RUN mkdir /app
WORKDIR /app

# Create a non-root user and switch to it
RUN adduser --disabled-password --gecos '' --shell /bin/bash user \
&& chown -R user:user /app
RUN echo "user ALL=(ALL) NOPASSWD:ALL" > /etc/sudoers.d/90-user
USER user

# All users can use /home/user as their home directory
ENV HOME=/home/user
RUN chmod 777 /home/user

# Install Miniconda
RUN curl -so ~/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-4.5.1-Linux-x86_64.sh \
&& chmod +x ~/miniconda.sh \
&& ~/miniconda.sh -b -p ~/miniconda \
&& rm ~/miniconda.sh
ENV PATH=/home/user/miniconda/bin:$PATH
ENV CONDA_AUTO_UPDATE_CONDA=false

# Create a Python 3.6 environment
RUN /home/user/miniconda/bin/conda install conda-build \
&& /home/user/miniconda/bin/conda create -y --name py36 python=3.6.5 \
&& /home/user/miniconda/bin/conda clean -ya
ENV CONDA_DEFAULT_ENV=py36
ENV CONDA_PREFIX=/home/user/miniconda/envs/$CONDA_DEFAULT_ENV
ENV PATH=$CONDA_PREFIX/bin:$PATH

# Install PyTorch with Cuda 9.1 support
RUN conda install -y -c pytorch \
cuda91=1.0 \
magma-cuda91=2.3.0 \
pytorch=0.4.0 \
torchvision=0.2.1 \
&& conda clean -ya
RUN conda install opencv

# Install other dependencies from pip 
#My requirments.txt file jsut contains the following packages I used for the code. Change this for your need.
#numpy==1.14.3
#torch==0.4.0
#torchvision==0.2.1
#matplotlib==2.2.2
#tqdm==4.28.1
COPY requirements.txt .
RUN pip install -r requirements.txt

# Create /data directory so that a container can be run without volumes mounted
RUN sudo mkdir /data && sudo chown user:user /data

# Copy source code into the image
COPY --chown=user:user . /app

# Set the default command to python3
CMD ["python3"]

Here’s the bash command used for running the docker using the Nvidia run-time.

# 1. Build image
docker build .

# 2. Run the docker image
docker run \
--runtime=nvidia -it -d \
--rm <dockerImage> python3 <yourCode.py>

 

Just try it and see how your deep learning life becomes easy! Happy coding! 🙂

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