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This looks neat! We have been doing a lot of deep learning for NLP at our startup recently. Several “engineering bottlenecks” in the process (1) managing multiple jobs is definitely worth solving. git for deep learning would be neat (2) collaborating is a pain when the team is remote. I guess this ties to (1) too.

And oh, about the time I forgot to turn off our GPU instance for a couple of weeks… racked up a nice bill...

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We’ve definitely felt the bane of forgetting to turn off some really expensive GPU instances. Efficient scheduling and spinning instances up/down as required is one of the first things we built to cut down on the costs.

Git is an apt analogy. The search space of hyperparameters is usually fairly large for most DL algorithms, so a good amount of experimentation is required to tune them. Things can start to get haywire without end-to-end version control of code, data, parameters, results, environments, etc. Definitely one of the core problems we solve.

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