Why you should log with Aimstack

I’ve long idolized Home | AimStack. It’s a tool that allows you to track metrics and hyperparameters and a whole bunch of stuff. It lets you compare across experiments.

I first built my own version of this in RShiny back in 2017 so I could compare AUC across experiments.

I was using h2o.ai at the time which had this great flow for monitoring an individual experiment but made it impossible to compare across experiments.

Then my company Capital One built rubicon: GitHub - capitalone/rubicon-ml: Capture all information throughout your model’s development in a reproducible way and tie results directly to the model code!. This was cool and they open sourced it. It tracks parameters.

But it pales in comparison to aim.

Aim can do all these things:

Track hyperparameters
Track learning curves (like a metric over time such as when building DL models and you’re watching how the error decreases with each epoch)
Track any plotly plot, which means you can put any EDA charts nicely organized in one place
it captures all your logging.info calls so you don’t need to use a remote logging thing like cloud watch to monitor long runs
It has its own loggers if you want to differentiate
It lets you take notes on an experiment
If lets you compare across experiments
It lets you use locally or remotely as a remote API
It tracks images, etc.
it connects with ML packages like xgbost to auto log.
It can even convert MLFlow data.

It’s incredible. Use it. It’s free.

_________________________

Bryan lives somewhere at the intersection of faith, fatherhood, and futurism and writes about tech, books, Christianity, gratitude, and whatever’s on his mind. If you liked reading, perhaps you’ll also like subscribing: