es-dev-stack

An on-premises, bare-metal solution for deploying GPU-powered applications in containers

Blog Post with deployment details:

http://www.emergingstack.com/2016/01/10/Nvidia-GPU-plus-CoreOS-plus-Docker-plus-TensorFlow.html

(pinmarch added: Docker Hub https://hub.docker.com/r/pinmarch/nvidia-install/ )

Prerequisites

CoreOS-compatible dedicated machine with vanilla CoreOS installed
Current-generation Nvidia GPU (tested with TitanX)
Current-generation Nvidia GPU (tested with GTX970 by pinmarch)
NVIDIA driver installer file (*.run)

To Build

Nvidia Drivers Installation Image

$ cd es-dev-stack/corenvidiadrivers

$ docker build -t nvidia-install .

GPU-enabled TensorFlow Image

$ cd es-dev-stack/tflowgpu

$ docker build -t tflowgpu .

To Run

Stage 1 - Install Nvidia Drivers & Register GPU Devices (One-Time)

# docker run -it --rm -v /dev/:/dev/ --privileged nvidia-install

(./mkdevs.sh is executed in the previous command.)

Stage 2 - TensorFlow Docker Container with mapped GPU devices

$ docker run -v /dev/nvidia0:/dev/nvidia0 -v /dev/nvidia1:/dev/nvidia1 -v /dev/nvidiactl:/dev/nvidiactl -v /dev/nvidia-uvm:/dev/nvidia-uvm -it -p 8888:8888 tflowgpu

To Test

Open your web browser to http://{host IP}:8888 and launch the CNN.ipynb notebook
Execute all steps to confirm
To validate GPU is utilized, watch the statistics produced from the Nvidia-SMI tool;

$ docker exec -it {container ID} /bin/bash

From within the running container:

$ watch nvidia-smi

Credits:

This solution takes inspiration from a few community sources. Thanks to;

Nvidia driver setup via Docker - Joshua Kolden joshua@studiopyxis.com