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README.md

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 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

Building TensorFlow Docker containers should be done through the parameterized_docker_build.sh script. The raw Dockerfiles should not be used directly as they contain strings to be replaced by the script during the build.

To use the script, specify the container type (CPU vs. GPU), the desired Python version (PYTHON2 vs. PYTHON3) and whether the developer Docker image is to be built (NO vs. YES). In addition, you need to specify the central location from where the pip package of TensorFlow will be downloaded.

For example, to build a CPU-only non-developer Docker image for Python 2, using TensorFlow's nightly pip package:

export TF_DOCKER_BUILD_IS_DEVEL=NO
export TF_DOCKER_BUILD_TYPE=CPU
export TF_DOCKER_BUILD_PYTHON_VERSION=PYTHON2

export NIGHTLY_VERSION="1.head"
export TF_DOCKER_BUILD_CENTRAL_PIP=$(echo ${TF_DOCKER_BUILD_PYTHON_VERSION} | sed s^PYTHON2^http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=${TF_DOCKER_BUILD_PYTHON_VERSION},label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp27-cp27mu-manylinux1_x86_64.whl^ | sed s^PYTHON3^http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-${NIGHTLY_VERSION}-cp35-cp35m-manylinux1_x86_64.whl^)

tensorflow/tools/docker/parameterized_docker_build.sh

If successful, the image will be tagged as ${USER}/tensorflow:latest by default.

Rebuilding GPU images requires nvidia-docker.