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Given the number of tools around, and as this book promotes Drake, has anyone got "comparative experience" with some of the following tools: Cookiecutter: http
by fnl 9y ago
Given the number of tools around, and as this book promotes Drake, has anyone got "comparative experience" with some of the following tools:
Cookiecutter: https://drivendata.github.io/cookiecutter-data-science https://drivendata.github.io/cookiecutter-data-science
DataVersionControl: https://dataversioncontrol.com/ https://dataversioncontrol.com/
Drake: https://github.com/Factual/drake https://github.com/Factual/drake
Luigi: https://github.com/spotify/luigi https://github.com/spotify/luigi
Pachyderm: http://www.pachyderm.io/ http://www.pachyderm.io/
Sacred: https://github.com/IDSIA/sacred https://github.com/IDSIA/sacred
They all focus in slightly different ways on the issue of managing data science/machine learning workflows, so I wonder if someone has a clear preference for one of those over any another.
EDIT: added Luigi
- fnl 9y agoTo amend, why I'm even bringing this up, what worries me about Drake is this: https://github.com/Factual/drake/pulse/monthly https://github.com/Factual/drake/pulse/monthly Its GitHub pulse is - dead; For two years now. Makes me think one of the other projects listed might be better choices.
- bringtheaction 9y agoI was about to say maybe it's finished but it has 70 open issues so maybe it was just abandoned? It is at version 1.0.3 though so it could be that it's considered finished. Seems strange to leave the issues open if it was though.
- fnl 9y agoMaybe so. Yet, I can't find any features in Drake that I don't get with Make, too - in fact, it looks to be rather the opposite. Indeed, for some of the tools I listed, they barely have any more functionality than I'd get out of Make & Git alone. And for Make, I'm pretty sure development & support will stick around for a few more years... To me, only Luigi (Hadoop integration), Pachyderm (containers, production deployments in the Enterprise version) and Sacred (Python & TensorFlow integration) really stick out as differentiating themselves. But maybe I'm overlooking something?