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

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

• OSX
• Ubuntu

We currently maintain two 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.

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 method we have used. But it is not recommended. If you find a bug in nvidia-docker, please report it there and try using 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

See all available tags for additional containers, such as release candidates or nightly builds.

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

$ docker build --pull -t $USER/tensorflow-suffix -f Dockerfile.suffix .