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I want robust, packaged setups (docker, AWS, whatever) to be cheap/free, accessible, and work out of the box with no end-user setup. I am getting into DL mysel
by ccvannorman 9y ago
I want robust, packaged setups (docker, AWS, whatever) to be cheap/free, accessible, and work out of the box with no end-user setup.
I am getting into DL myself and very excited about the potential, but I have spent literally 5 hours on setup (python and DL-specific AWS Ubunut instance) and have gotten exactly nowhere. Version conflicts, iPython not working in venv, dependency mismatches where many people built demos I want to use on python 2.7 that don't work with my 3.5 setup, can't get matplotlib to display graphs over X11/remote ssh, etc, etc, etc. So frustrating.
I want programmers to be able to one-button setup a DL machine so they can start tinkering and not wasting time on setup/dependencies/bugs.
- levlandau 9y agoi hear you. check out floydhub [https://www.floydhub.com/ https://www.floydhub.com/]
- saip 9y agoDevOps 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.