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?
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
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.
Inviting you to the Aim [slack channel](https://slack.aimstack.io/).
We would love to learn more about such use cases and why they are important.
It's focused on comparing 1000s of experiments really effectively in minutes. MLFlow, Tensorboard don't have these capabilities which has motivated us to work on Aim.
Especially valuable when running hyperparam sensitive tasks such as RL.
What is the new paradigm and how does it differ from the existing paradigms?
And what do you mean by "no way to do effective comparison of runs by hyperparams or other metadata on the tensorboard or MLFlow"? If you mean "you can't compare or sort a list of runs by hyperparameter or minimum loss or whatever" then MLFlow can certainly do that, so I think I'm misunderstanding.
Any comments on Losswise or W&B?
And do you have a plan for monetization or governance?
Sorry for all the questions! I have complaints about all the existing solutions, so I'm excited to see a new effort.
re comparison: we have always wanted to use a free open-source self-hosted tool that would let us group metrics/runs by hyperparams, experiment context(train, val, test ...) and any other adjacent info about the training runs. Be able to aggregate groups of metrics, be able to give them different styles, divide them into subplots, search through the runs easily (without regexps on super-long names) etc. As far as I checked last times no such features aren't built for those tools. This is huge motivation behind Aim.
Probably the closest to this is W&B but it's not open-source and doesn't allow to see full context of the runs while comparing them (separate module).
Haven't used Losswise tbh.
We are trying to build a way that would allow to compare 1000s of ML training runs at the same time while still making the full info (context) of the runs available.
This is what I meant by "new paradigm".
(It turns out this is a fun problem :) ).
We have been working on Aim just a few months only (3 of us) and it's in very early stages. Most of the ideas we have aren't really shipped yet.
But it's already very useful for many RL researchers who run lots of experiments and those experiments are sensitive to hyperparameters. Aim seems to be able to handle them.
Have you checked out the live demo from the README?
Check out my blogpost on TowardsDataScience for more info on Aim (https://towardsdatascience.com/3-ways-aim-can-accelerate-you...).
Hope this info is useful and makes sense. Would be awesome to connect.
I would love to learn more about your use-cases and needs in these tools. My twitter is @gevorg_s.
Aim does that and also aggregates groups of runs to reduce the dimension and make it easy to compare.
It has a proper search by hyperparsams (and everything else tracked/collected really). All in one panel where you can compare 100s of experiments at a time.
Loading many runs on TB, with very long names makes it super slow to analyze the runs really. Tbh that has been a motivation for building this open source tool - to have something efficient and beautiful :)