Using TensorFlow via Docker

This directory contains Dockerfiles to make it easy to get up and running with TensorFlow via Docker.

Installing Docker

General installation instructions are on the Docker site, but we give some quick links here:

OSX
ubuntu

Which containers exist?

We currently maintain three Docker container images:

gcr.io/tensorflow/tensorflow - TensorFlow with all dependencies - CPU only!
gcr.io/tensorflow/tensorflow:latest-gpu - TensorFlow with all dependencies and support for Nvidia Cuda

Note: We also publish the same containers into Docker Hub.

Running the container

Run non-GPU container using

$ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow

For GPU support install NVidia drivers (ideally latest) and nvidia-docker. Run using

$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu

Note: If you would have a problem running nvidia-docker you may try the old way we have used. But it is not recomended. If you find a bug in nvidia-docker report it there please and try using the nvidia-docker as described above.

$ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda.* | xargs -I{} echo '-v {}:{}')
$ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
$ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow:latest-gpu

More containers

See all available tags for additional containers like release candidates or nighlty builds.

Rebuilding the containers

Just pick the dockerfile corresponding to the container you want to build, and run