Public Repository

Last pushed: a year ago
Short Description
Machine Learning (GPU) Python3.4 (0.9), with frills like keras, opencv, pandas, seaborn, and bokeh
Full Description

It's a jupyter style docker image, so it should start a server automatically, meaning you should be able to go to localhost:8888 immediately in a web browser and start doing stuff.

To be clear, this is just the official tensorflow:gpu jupyter image upgraded to python3 with a couple of the data science libraries I like stuck on. Now that I know a bit more about tensorflow, I've added keras and openCV for easier prototyping and computer vision stuff. However, note that in particular the addition of openCV adds 6GB for a total of 11GB, but at least it's all CUDA optimized.

With the recent upgrade to 0.9, or somewhere along the way (I'm pulling/building master), running cuda with docker has been abstracted away to some extent with Follow the instructions there to run it. Also, I suggest linking a folder so you don't have to copy data back and forth from containers if you are working locally. ie -v "~/notebooks:/notebooks"

sample code I use to start the notebook:

nvidia-docker run -it --net host -p 8888:8888 -v "/home/thomas/notebooks:/notebooks" thomasekeller/tensorflow-py3-frills

Changed the build to use bazel so it should work fine now, tensorflow imports without error in the notebook on my laptop now.
Dockerfile heavily inspired by grahama's

Docker Pull Command