Data Science Deployments With Docker
With the recent release of NVIDIA’s nvidia-docker tool, accessing GPUs from within Docker is a breeze. In this tutorial we’ll walk you through setting up nvidia-docker so you too can deploy machine learning models with ease.
By Madison May, indico .
Deploying machine learning models has always been a struggle. Most of the software industry has adopted the use of container engines like Docker for deploying code to production, but since accessing hardware resources like GPUs from Docker was difficult and required hacky, driver specific workarounds, the machine learning community has shied away from this option. With the recent release of NVIDIA’s nvidia-docker tool, however, accessing GPUs from within Docker is a breeze, and we’re already reaping the benefits here at indico. In this tutorial we’ll walk you through setting up nvidia-docker so you too can deploy machine learning models with ease.
Before we get into the details however, let’s talk briefly about why using Docker for your next data science project may be a good choice. There is certainly a learning curve for the tools in the Docker ecosystem, but the benefits are worth the effort.
No inconsistencies between team environment configurations:Software configuration is always a pain. Docker’s configure once, run anywhere model means your teammates will have to worry less about environment setup and can focus more on writing code and building machine learning models.
Reliable deployments:Fewer bugs crop up in production when you can be assured that your development environment is identical to your production environment.
Git-like tool for environment configuration:If something does go wrong in production, reverting to a previous Docker image ensures you can quickly get back to a functional state.
Why is a special solution needed for using GPUs within Docker?
Docker is designed to be hardware and platform agnostic. GPUs are specialized hardware that is not necessarily available on every host. Because of this, the Docker binary does not include GPU support out of the box, and requires a fair amount of configuration to get things working properly. When we first started using Docker in production and needed to enable access to GPU devices from within the container, we had to roll our own solution. It was educational to have to understand the mechanisms by which hardware like GPUs are exposed to an operating system (primarily the /dev block), but we ended up with a solution that was not portable and required that the host’s NVIDIA driver was identical to a second copy of the driver installed within the container. Whenever we updated our NVIDIA drivers to support newer CUDA versions, we had to make a breaking change to our Docker image in order to ensure drivers matched exactly.
Thankfully, the nice folks at NVIDIA have rectified this problem by releasing nvidia-docker, a tool for configuring docker to allow GPU access from within containers.
How does nvidia-docker work?
nvidia-docker takes the following steps to get CUDA working within your container:
It attaches the GPU device blocks to your container as Docker volumes (/dev/nvidia0, /dev/nvidiactl, etc.)
It mounts the device drivers on your host within the Docker container
This means that as long as you have a functional NVIDIA driver on your host and a CUDA version recent enough to support your driver is installed within your container, you should be able to execute CUDA code from your running Docker container. Importantly, the Docker container can also be run in another environment with different driver versions, making it easy to build once and then run anywhere.
How do I install nvidia-docker?
NVIDIA GPU with Architecture > Fermi (2.1)
NVIDIA drivers >= 340.29 with binary nvidia-modprobe
If you already meet these requirements, installation of nvidia-docker is as easy as installing a .deb file (on Ubuntu 14.
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