An easy-to-use &
supercharged open-source
ML experiment tracker

Aim logs your training runs, enables a beautiful UI to compare them and an API to query them programmatically.

⭐️ Star Aim on Github Out now: Aim 3.9

Get started
in under a minute
and on any environment

Check out our GitHub repo or
documentation to learn more

pip install aim

from aim import Run
# Initialize a new run
run = Run()
# Log run parameters
run["hparams"] = {
    "learning_rate": 0.001,
    "batch_size": 32,
}
# Log metrics
for i in range(10):
    run.track(i, name='loss', step=i, context={ "subset":"train" })
    run.track(i, name='acc', step=i, context={ "subset":"train" })

aim up

Why use Aim?

Compare runs easily to build models faster

Group and aggregate 100s of metrics
Analyze and learn correlations
Query with easy pythonic search

Check out our GitHub

Deep dive into details
of each run for easy debugging

Explore hparams, metrics, images,
distributions, audio, text, ...
Track plotly and matplotlib plots
Analyze system resource usage

Check out our GitHub

Have all relevant information
centralized for easy governance

Centralized dashboard to view all your runs
Use SDK to query/access tracked runs
You own your data - Aim is open source
and self hosted.

Check out our GitHub

Demos

Play with Aim before installing it!
Check out our demos to see the full functionality

Machine translation

Training logs of a neural translation model (from WMT'19 competition).

lightweight-GAN

Training logs of 'lightweight' GAN, proposed in ICLR 2021.

FastSpeech 2

Training logs of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech".

Simple MNIST

Simple MNIST training logs.