This directory contains Dockerfiles 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 two Docker container images:
tensorflow/tensorflow - TensorFlow with all dependencies - CPU only!
tensorflow/tensorflow:latest-gpu - TensorFlow with all dependencies and support for NVidia CUDA
Note: We store all our containers on Docker Hub.
Run non-GPU container using
$ docker run -it -p 8888:8888 tensorflow/tensorflow
For GPU support install NVidia drivers (ideally latest) and nvidia-docker. Run using
$ nvidia-docker run -it -p 8888:8888 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.
$ # The old, not recommended way to run docker with gpu support:
$ 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 $DEVICES tensorflow/tensorflow:latest-gpu
See all available tags for additional containers, such as release candidates or nightly builds.
Building TensorFlow Docker containers should be done through the parameterized_docker_build.sh script. The raw Dockerfiles should not be used directly as they contain strings to be replaced by the script during the build.
Attempting to run parameterized_docker_build.sh from a binary docker image such as for example tensorflow/tensorflow:latest will not work. One needs to execute the script from a developer docker image since by contrast with a binary docker image it contains not only the compiled solution but also the tensorflow source code. Please select the appropriate developer docker image of tensorflow at tensorflow/tensorflow:[.](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
The smallest command line to generate a docker image will then be: docker run -it tensorflow/tensorflow:"right_tag"
If you would like to start a jupyter notebook on your docker container, make sure to map the port 8888 of your docker container by adding -p 8888:8888 to the above command.
To use the script, specify the container type (CPU vs. GPU), the desired Python version (PYTHON2 vs. PYTHON3) and whether the developer Docker image is to be built (NO vs. YES). In addition, you need to specify the central location from where the pip package of TensorFlow will be downloaded.
For example, to build a CPU-only non-developer Docker image for Python 2, using TensorFlow's nightly pip package:
export TF_DOCKER_BUILD_IS_DEVEL=NO export TF_DOCKER_BUILD_TYPE=CPU export TF_DOCKER_BUILD_PYTHON_VERSION=PYTHON2 export NIGHTLY_VERSION="1.head" export TF_DOCKER_BUILD_CENTRAL_PIP=$(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.