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Last pushed: 2 years ago
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tensorflow on python3.4
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What is this?


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:

Which containers exist?

We currently maintain three Docker container images:

  •, which is a minimal VM with TensorFlow and
    all dependencies.

  •, which contains a full source
    distribution and all required libraries to build and run TensorFlow from

  •, which is the same as the previous
    container, but built with GPU support.

Running the container

Each of the containers is published to a Docker registry; for the non-GPU
containers, running is as simple as

$ docker run -it -p 8888:8888

For the container with GPU support, we require the user to make the appropriate
NVidia libraries available on their system, as well as providing mappings so
that the container can see the host's GPU. For most purposes, this can be
accomplished via

$ 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

Alternately, you can use the script in this directory.

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 .
Docker Pull Command
Source Repository