robotrahul123/tensorflow repository overview

⁠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 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.

⁠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 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

⁠More containers

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