🐋 Docker -

NVIDIA Image Building

Building a NVIDIA CUDA enabled Docker image. I'll be building an image to run darknet machine learning library in this example.

1. Connect to a build machine with working nvidia-docker if you don't have it locally.

   ssh user@xxx.xxx.xxx.xxx
   

2. Try out your hypothesis how to build the image; base image, commands, etc.

   nvidia-docker pull nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
   nvidia-docker history nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
   nvidia-docker rm `nvidia-docker ps -aq`
   nvidia-docker run -it --rm --name cccc nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04 /bin/bash
   # try out what you plan on doing...
   

3. Create a Dockerfile after you get it in the state you want.

   FROM nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
   MAINTAINER Ruksi <me@ruk.si>
   
   ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 CUDA_ROOT=/usr/local/cuda/bin
   
   RUN apt-get update --fix-missing && apt-get install -y \
     libopencv-dev \
     python-opencv \
     git
   
   RUN git clone https://github.com/pjreddie/darknet.git /darknet
   WORKDIR /darknet
   RUN sed -i'' -- 's/GPU=0/GPU=1/g' Makefile
   RUN sed -i'' -- 's/CUDNN=0/CUDNN=1/g' Makefile
   RUN sed -i'' -- 's/OPENCV=0/OPENCV=1/g' Makefile
   RUN make
   
   ENTRYPOINT []
   CMD ["/bin/bash"]
   

4. Build the image

   # Check latest commit id at https://github.com/pjreddie/darknet
   b61bcf5
   
   # Build the Dockerfile to an image.
   nvidia-docker build -t ruksi/darknet:gpu-b61bcf5 .
   
   # Check that it works.
   nvidia-docker run --rm ruksi/darknet:gpu-b61bcf5 /darknet/darknet
   # => usage: /darknet/darknet <function>
   
   # Push to Docker repository.
   `aws ecr get-login --region eu-west-1` # if required for access
   nvidia-docker push ruksi/darknet:gpu-b61bcf5