I am doing some work that requires Pose Tracking for my thesis work (as a tool, not a goal). The pose tracking algorithim that I am currently most excited about is the Part Affinity Field based method. I’m looking forward to playing with the new Facbook method too.

The kind folks at the CMU Perceptual Computing Lab have been kind enough to package their code into a clean library with nice wrappers called OpenPose. For now, I wanted to get the demo running in AWS. So I dockerized it and put it up on docker hub.

This was my first time using Docker or AWS, so it was a learning experience. Lets go through how you can run their code on AWS using docker to make it easier:

Setup EC2 Instance

Start of by provisioning an AWS ubuntu deep learning on a p2 instance

Then SSH into that machine when it is ready and do this:

  • Install docker
  • Install NVidia Drivers
    • wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
    • sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
    • nvidia-docker run --rm nvidia/cuda nvidia-smi
  • Make a place to store our data: mkdir data

Run

Docker

sudo nvidia-docker run -v /home/ubuntu/data:/data -it mjsobrep/openpose:x1 bash

Some helpful hints while you are running in Docker

To leave the docker:

  • Ctrl+p, Ctrl+q
    • Now we have detached from the docker, but it is still running
  • Let’s see where the docker is running by running: sudo docker ps
    • Now we have the container ID
  • Let’s stop it by running: sudo docker stop <container id>

OpenPose

You should now be in bash inside of the docker container. Lets run some examples:

  • Check out what options we have:
    • ./build/examples/openpose/openpose.bin --help
  • Lets make a place for us to put the output:
    • mkdir ../data/output
    • mkdir ../data/output/poses
  • ./build/examples/openpose/openpose.bin --video examples/media/video.avi --write_video ../data/output/result.avi --write_pose_json ../data/output/poses --no_display
    • You could pass the --num_gpu <#> flag to use more than 1 gpu
    • This will take a while to run (maybe around 1.5 minutes on a p2.xlarge)

Hopefully, you will get a printout along the lines of:

Real-time pose estimation demo successfully finished. Total time: 81.456782 seconds.

Using your own video

To use your own video, simply put the video in the data folder (/home/ubuntu/data) on the EC2 machine and change the --video argument to OpenPose

Getting data off AWS

All of your output will be in the data folder… Now we need to get it back so we can take a look: Just follow this guide

Stop your EC2

Don’t forget to stop the EC2 instance when you are done. Don’t want to spend too much money…

Future Things to Try:

I’m curious to try deployment using some nvidia tools, per this guide

I could come from a basic ubuntu AMI instead of the learning one and do all of this

I’m also curious whether I could use the EC2 container service with an elastic gpu