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
Prerequisites
- CoreOS-compatible dedicated machine with vanilla CoreOS installed
- Current-generation Nvidia GPU (tested with TitanX)
To Build
Nvidia Drivers Installation Image
$ cd es-dev-stack/corenvidiadrivers
$ docker build -t cuda .
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 --privileged cuda
# ./mkdevs.sh
Stage 2 - TensorFlow Docker Container with mapped GPU devices
$ docker run --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidia1:/dev/nvidia1 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm -it -p 8888:8888 --privileged 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
ConvNet demo notebook - Edward Banner edward.banner@gmail.com