Introducing a New Model and Project Management Tool for Machine and Deep Learning

September 10, 2019

Today we are happy to introduce to you our new fully open-sourced, “zero integration” model and project management tool for machine and deep learning. We call it TRAINS.

At allegro.ai we build an end-to-end AI platform & solution for enterprise companies. Since R&D is a central part of these pipelines, TRAINS is one of our technological cornerstones we created for our solution.

TRAINS was born from engaging with hundreds of companies and realizing that a significant number of them, both big and small, were not at the phase where they were worried about robust and scalable continuous deployment. Rather, they were still in the phase of rapid experimentation and prototyping (AKA, “find me a model that works”).

In that preliminary stage, efforts are concentrated on frequently trying out new models and repositories. However, without the benefit of a unifying experimentation management tool, ongoing research operations in organizations are usually left to the discretion of each researcher.

While this allows for zero-overhead prototyping, the side effects of such non-uniformity are detrimental in the long run: Reduced collaboration, loss of work, irreproducible training, and a negative effect on overall morale. One of the CTOs we interviewed had the following story:

“…The demo presentation required tuning some of the models we developed, using onsite footage we all annotated together as a company-wide effort. There were still training sessions that had to be set up, but they would finish before the deadline. Just then, the unthinkable happened: Our deep learning research wizard had a family emergency and all but disappeared. Nobody else knew where the ‘good’ data was, how to run his scripts, and which version of the code was the one that actually worked. We were going to have to use the base model, and the demo was going to be horrible…”

Our full-blown platform fits with and really shines for organizations with more deep-learning mileage than the current average, but we still wanted to help companies that aren’t there yet to push ahead with their machine learning experiments and projects.

Naturally, such companies are really interested in tools that would boost their productivity and solve the obstacles they encounter on a daily basis. At the same time, these are small teams in prototyping stages with few resources, so minimal integration costs are a must. The ideal product to chaperone them from prototype to an alpha version would be one that allows them to work with it as if it were not there, which would mean absolutely no workflow restrictions or introduction of new APIs.

Our solution for such a need is TRAINS— An “automagical” experiment integration and model management tool. A research team in the prototyping stage can set up and store insightful entries on their on-premises TRAINS-server in a matter of minutes by adding only two lines of code (including the line “from trains import Task”).

But why did we make it open-source?

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