TensorFlow Dockerfiles

This directory houses TensorFlow's Dockerfiles. DO NOT EDIT THE DOCKERFILES MANUALLY! They are maintained by assembler.py, which builds Dockerfiles from the files in partials/ and the rules in spec.yml. See the Maintaining section for more information.

Building

The Dockerfiles in the dockerfiles directory must have their build context set to the directory with this README.md to copy in helper files. For example:

$ docker build -f ./dockerfiles/cpu.Dockerfile -t tf .

Each Dockerfile has its own set of available --build-args which are documented in the Dockerfile itself.

Running

After building the image with the tag tf (for example), use docker run to run the images. Examples are below.

Note for new Docker users: the -v and -u flags share directories between the Docker container and your machine, and very important. Without -v, your work will be wiped once the container quits, and without -u, files created by the container will have the wrong file permissions on your host machine. If you are confused, check out the Docker run documentation.

# Volume mount (-v) is optional but highly recommended, especially for Jupyter.
# User permissions (-u) are required if you use (-v).

# CPU-based images
$ docker run -u $(id -u):$(id -g) -v $(PWD):/my-devel -it tf

# GPU-based images (set up nvidia-docker2 first)
$ docker run --runtime=nvidia -u $(id -u):$(id -g) -v $(PWD):/my-devel -it tf

# Images with Jupyter run on port 8888, and needs a volume for notebooks
$ docker run --user $(id -u):$(id -g) -p 8888:8888 -v $(PWD):/notebooks -it tf

# Ideal run command (Nvidia image, named container, volume mount the easy way)
$ nvidia-docker run --name jupyter -it -p 8888:8888 -v ${PWD}:/notebooks twoandahalfhorsemenofdeeplearning-nvidia-jupyter

These images do not come with the TensorFlow source code -- but the development images have git included, so you can git clone it yourself.

Contributing

To make changes to TensorFlow's Dockerfiles, you'll update spec.yml and the *.partial.Dockerfile files in the partials directory, then run assembler.py to re-generate the full Dockerfiles before creating a pull request.

You can use the Dockerfile in this directory to build an editing environment that has all of the Python dependencies you'll need:

$ docker build -t tf-assembler -f assembler.Dockerfile .

# Set --user to set correct permissions on generated files
$ docker run --user $(id -u):$(id -g) -it -v $(pwd):/tf tf-assembler bash 

# In the container...
/tf $ python3 ./assembler.py -o dockerfiles -s spec.yml