Using TensorFlow via Docker
This directory contains Dockerfile
s 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:
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
$ docker build --pull -t $USER/tensorflow-suffix -f Dockerfile.suffix .