angersson/tensorflow repository overview

⁠TensorFlow Docker Images

TensorFlow's many tags are defined on GitHub⁠, where you can also find extra Dockerfiles. See the full list of tags⁠ for the available images.

These tags are accurate for all releases after TensorFlow 1.12. Older releases are still tagged using the older format and images.

⁠Base Image Tags

All of TensorFlow's images are based on Ubuntu 16.04.

• 1.xx-, latest-, and nightly- tags come with TensorFlow pre-numbered and latest- images contain the latest release, and the nightly images come with the latest TensorFlow nightly Python package.
• devel images come with Bazel⁠ and are ideal for developing changes to TensorFlow (they don't have TensorFlow installed, however). /tensorflow_src includes the TensorFlow source tree at a recent known-to-compile commit.
• custom-op is a special experimental image for developing TF custom ops.

⁠Optional Features

• -py3 images come with Python 3.5 instead of Python 2.7.
• -gpu tags are based on NVidia CUDA⁠. You need nvidia-docker⁠ to run them.
• -jupyter tags include Jupyter and some TensorFlow tutorial notebooks.. They start a Jupyter notebook server on boot. Mount a volume to /tf/notebooks to work on your own notebooks.

⁠Running Containers

$ docker run -it --rm tensorflow/tensorflow bash

Start a CPU-only container with Python 2

$ docker run -it --rm --runtime=nvidia tensorflow/tensorflow:latest-gpu-py3 python

Start a GPU container with Python 3, using the Python interpreter.

 docker run -it --rm -v $(realpath ~/notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-py3-jupyter

Run a Jupyter notebook server with your own notebook directory (assumed here to be ~/notebooks). To use it, navigate to localhost:8888 in your browser.