/etcryanfb.xyz

In a previous post I outlined a workflow for using VisualSFM for photogrammetry. In this post, I’d like to highlight some recent work I’ve done for creating an isolated, replicable VisualSFM environment using Docker.

Using a Local Docker Host

First, follow the Docker installation guide for your platform (though on a Mac, you may prefer to follow this Homebrew-based guide).

After you’ve set up docker and your shell’s environment variables (using e.g. $(boot2docker shellinit)), run:

docker run -i -t ryanfb/visualsfm /bin/bash

Which should download my image from Docker Hub and drop you into a shell with a VisualSFM command (as well as youtube-dl, and ffmpeg replacement avconv).¹

Using an AWS GPU-Enabled Docker Host

Because my VisualSFM image builds on work by Traun Leyden to build a CUDA-enabled Ubuntu install with Docker, you can run the cuda tag/branch of it in a GPU-enabled environment to take advantage of SiftGPU during the SIFT feature recognition stage of VisualSFM processing (with no GPU/CUDA support detected, it will fall back to the CPU-based VLFeat SIFT implementation).²

This means you can also use his instructions and AMI for building a CUDA-enabled AWS EC2 instance, and then run my VisualSFM image inside it.

To do this you’ll need to:

• Launch an EC2 instance with instance type g2.2xlarge, community AMI ami-2cbf3e44, and 20+ GB of storage
• Connect to your EC2 instance
• Install docker inside your EC2 instance:
  • sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9
  • sudo sh -c "echo deb https://get.docker.com/ubuntu docker main > /etc/apt/sources.list.d/docker.list"
  • sudo apt-get update
  • sudo apt-get install lxc-docker
• Run a GPU-enabled VisualSFM docker image:
  • Build the CUDA samples and run deviceQuery inside your Docker host (this seems to be necessary to init the nvidia devices in /dev):
    • cd ~/nvidia_installers
    • sudo ./cuda-samples-linux-6.5.14-18745345.run -noprompt -cudaprefix=/usr/local/cuda-6.5/
    • cd /usr/local/cuda/samples/1_Utilities/deviceQuery
    • sudo make
    • ./deviceQuery
  • Find your nvidia devices with: ls -la /dev | grep nvidia
  • Set these as --device arguments in a variable you’ll pass to the docker run command:
    • export DOCKER_NVIDIA_DEVICES="--device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm"
    • sudo docker run -ti $DOCKER_NVIDIA_DEVICES ryanfb/visualsfm:cuda /bin/bash (note the :cuda tag specifier here)
  • Follow the instructions here for more explanation and to verify CUDA access inside the container

You should now be inside a docker container in your EC2 instance, with a VisualSFM command which will use SiftGPU for feature recognition.

Footnotes