Would this be useful for people who wanted to leverage GPUs for deep learning, but didn't have the wherewithal or the willingness to set up all the dependencies on the host machine?

Yes. I have made a Docker container containing Keras and the better CNTK backend (+ Jupyter) and it works great: https://github.com/minimaxir/keras-cntk-docker

There's another unexpected bonus to nvidia-docker; CNTK leaks memory if you abort training prematurely, causing the GPU to go OOM. But since the memory is owned by the nvidia-docker process, killing-and-restarting the container fixes the issue without having to restart the entire system.

It is used by this kind of popular pre-built Docker image for Deep Learning: https://github.com/floydhub/dl-docker

They actually created this service using that (a Deep Learning "as a service"): https://www.floydhub.com/

At FloydHub, we use nvidia-docker in production for running DL jobs. It has been very solid for the past 6 months or so. We have also built a collection of open source DL docker images for various frameworks and we actively maintain them.

[1]: https://hub.docker.com/r/floydhub/ [2]: https://github.com/floydhub/dockerfiles

Exactly what we are using this for. However, I found there was little performance benefit versus running on CPU. Can't figure out why.

Yes, this is a great use case. Search around, there are a few folks putting together dockers to get you out of dl dependency hell