This directory contains Dockerfile
s 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:
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.
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
See all available tags for additional containers like release candidates or nighlty 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 .