[saip]

DevOps is indeed a huge bottleneck in deep learning. Provisioning machines, installing drivers and packages and managing their dependency hell distracts focus from the core deep learning. At FloydHub (I'm a co-founder), we're building a zero-setup deep learning platform.

Spinning up a Jupyter notebook with Pytorch 0.2 is as simple as `floyd run --env pytorch-0.2 --mode jupyter`. All the steps you mention in your comment are automated.

DevOps hassles is, of course, just the first of many hurdles to doing effective deep learning. Experiment management, version control, reproducibility, sharing & collaboration, etc. are also other important problems.