Aim: Record, search and compare ML training runs

Re:Aim: Record, search and compare ML training runs by marcinzm on Wednesday January 13, 2021 @04:16PM (#25768084)

Honestly, it's really hard for me to tell from the documentations if this is something that is self-hosted, is run locally or is a SaaS product. The phrase "open source" shows up nowhere I can see for example.

As best I can tell it's an open source version of comet or wandb that you run locally (or I guess host somewhere). Is that right?

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Re:Aim: Record, search and compare ML training runs by ellisv on Wednesday January 13, 2021 @05:23PM (#25768782)

From what I can glean from the docs, you run the UI locally which reads from experiments from `.aim/`

The experiments results could be populated by running the training job remotely and committing the `.aim/` directory afterwards.

It's kinda like a UI for DVC

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Re:Aim: Record, search and compare ML training runs by gevorg_s on Thursday January 14, 2021 @12:57AM (#25772661)

Yea, spot on. Right now UI is ran locally and it reads the data from `.aim`. We have seen users deploy it for the team and have seen standalone in the local environment too. Once training is ran, the logs are saved in the `.aim`. We are using a format that is ~50% more memory efficient than the tb logs - and searchable (see the aimrecords repo).

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Re:Aim: Record, search and compare ML training runs by jszymborski on Wednesday January 13, 2021 @06:01PM (#25769196)

yes, it looks like it's meant to be run in a similar way to Tensorboard

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Re:Aim: Record, search and compare ML training runs by gevorg_s on Thursday January 14, 2021 @12:58AM (#25772664)

yep.

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Re:Aim: Record, search and compare ML training runs by gevorg_s on Thursday January 14, 2021 @12:52AM (#25772625)

Hi, Aim is fully open source and self-hosted (at the moment - just like tensorboard). Open source version of wandb would be one way to put it.

But we are building a new way of interacting with the ML training runs that lets the researchers compare lots of them (1000s) in really short period of time while having full access to the context of the experiments. This is a super early version. And lots more work needs to be done.

We have implemented a pythonic search to search through the experiments that is easy to use. Hopefully this sheds more light to the work we are doing.

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