Docker uses containers to create virtual environments that isolate a TensorFlow installation from the rest of the system. TensorFlow programs are run within this virtual environment that can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The TensorFlow Docker images are tested for each release.

Docker is the easiest way to enable TensorFlow GPU support on Linux since only the NVIDIA® GPU driver is required on the host machine (the NVIDIA® CUDA® Toolkit does not need to be installed).

TensorFlow Docker requirements

  1. Install Docker on your local host machine.
  2. For GPU support on Linux, install nvidia-docker.

Download a TensorFlow Docker image

The official TensorFlow Docker images are located in the tensorflow/tensorflow Docker Hub repository. Image releases are tagged using the following format:

Tag Description
latest The latest release of TensorFlow CPU binary image. Default.
nightly Nightly builds of the TensorFlow image. (unstable)
version Specify the version of the TensorFlow binary image, for example: 1.12.0
devel Nightly builds of a TensorFlow master development environment. Includes TensorFlow source code.

Each base tag has variants that add or change functionality:

Tag Variants Description
tag-gpu The specified tag release with GPU support. (See below)
tag-py3 The specified tag release with Python 3 support.
tag-jupyter The specified tag release with Jupyter (includes TensorFlow tutorial notebooks)

You can use multiple variants at once. For example, the following downloads TensorFlow release images to your machine:

docker pull tensorflow/tensorflow                     # latest stable release
docker pull tensorflow/tensorflow:devel-gpu           # nightly dev release w/ GPU support
docker pull tensorflow/tensorflow:latest-gpu-jupyter  # latest release w/ GPU support and Jupyter

Start a TensorFlow Docker container

To start a TensorFlow-configured container, use the following command form:

docker run [-it] [--rm] [-p hostPort:containerPort] tensorflow/tensorflow[:tag] [command]

For details, see the docker run reference.

Examples using CPU-only images

Let's verify the TensorFlow installation using the latest tagged image. Docker downloads a new TensorFlow image the first time it is run:

docker run -it --rm tensorflow/tensorflow \
   python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))"

Let's demonstrate some more TensorFlow Docker recipes. Start a bash shell session within a TensorFlow-configured container:

docker run -it tensorflow/tensorflow bash

Within the container, you can start a python session and import TensorFlow.

To run a TensorFlow program developed on the host machine within a container, mount the host directory and change the container's working directory (-v hostDir:containerDir -w workDir):

docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/tensorflow python ./

Permission issues can arise when files created within a container are exposed to the host. It's usually best to edit files on the host system.

Start a Jupyter Notebook server using TensorFlow's nightly build with Python 3 support:

docker run -it -p 8888:8888 tensorflow/tensorflow:nightly-py3-jupyter

Follow the instructions and open the URL in your host web browser:

GPU support

Docker is the easiest way to run TensorFlow on a GPU since the host machine only requires the NVIDIA® driver (the NVIDIA® CUDA® Toolkit is not required).

Install nvidia-docker to launch a Docker container with NVIDIA® GPU support. nvidia-docker is only available for Linux, see their platform support FAQ for details.

Check if a GPU is available:

lspci | grep -i nvidia

Verify your nvidia-docker installation:

docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

Examples using GPU-enabled images

Download and run a GPU-enabled TensorFlow image (may take a few minutes):

docker run --runtime=nvidia -it --rm tensorflow/tensorflow:latest-gpu \
   python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))"

It can take a while to set up the GPU-enabled image. If repeatably running GPU-based scripts, you can use docker exec to reuse a container.

Use the latest TensorFlow GPU image to start a bash shell session in the container:

docker run --runtime=nvidia -it tensorflow/tensorflow:latest-gpu bash