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

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

_{SEAMLESSLY INTEGRATES WITH:}

_{TRUSTED BY ML TEAMS FROM:}

AimStack offers enterprise support that's beyond core Aim. Contact via hello@aimstack.io e-mail.

About • Demos • Ecosystem • Quick Start • Examples • Documentation • Community • Blog

ℹ️ About

Aim is an open-source, self-hosted ML experiment tracking tool designed to handle 10,000s of training runs.

Aim provides a performant and beautiful UI for exploring and comparing training runs. Additionally, its SDK enables programmatic access to tracked metadata — perfect for automations and Jupyter Notebook analysis.

Aim's mission is to democratize AI dev tools 🎯

🎬 Demos

Check out live Aim demos NOW to see it in action.

Machine translation experiments	lightweight-GAN experiments
Training logs of a neural translation model(from WMT'19 competition).	Training logs of 'lightweight' GAN, proposed in ICLR 2021.

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

🌍 Ecosystem

Aim is not just an experiment tracker. It's a groundwork for an ecosystem. Check out the two most famous Aim-based tools.

aimlflow	Aim-spaCy
Exploring MLflow experiments with a powerful UI	an Aim-based spaCy experiment tracker

🏁 Quick start

Follow the steps below to get started with Aim.

1. Install Aim on your training environment

pip3 install aim

2. Integrate Aim with your code

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" })

_See the full list of supported trackable objects(e.g. images, text, etc) here._

3. Run the training as usual and start Aim UI

aim up

Learn more

Read the full documentation on aimstack.readthedocs.io 📖

🆚 Comparisons to familiar tools

🛣️ Roadmap

Detailed milestones

The Aim product roadmap :sparkle:

• The Backlog contains the issues we are going to choose from and prioritize weekly
• The issues are mainly prioritized by the highly-requested features

High-level roadmap

The high-level features we are going to work on the next few months:

In progress

• [ ] Aim SDK low-level interface
• [ ] Dashboards – customizable layouts with embedded explorers
• [ ] Ergonomic UI kit
• [ ] Text Explorer

👥 Community

Aim README badge

Add Aim badge to your README, if you've enjoyed using Aim in your work:

[![Aim](https://img.shields.io/badge/powered%20by-Aim-%231473E6)](https://github.com/aimhubio/aim)

Cite Aim in your papers

In case you've found Aim helpful in your research journey, we'd be thrilled if you could acknowledge Aim's contribution:

@software{Arakelyan_Aim_2020,
  author = {Arakelyan, Gor and Soghomonyan, Gevorg and {The Aim team}},
  doi = {10.5281/zenodo.6536395},
  license = {Apache-2.0},
  month = {6},
  title = {{Aim}},
  url = {https://github.com/aimhubio/aim},
  version = {3.9.3},
  year = {2020}
}

Contributing to Aim

Considering contibuting to Aim? To get started, please take a moment to read the CONTRIBUTING.md guide.

Join Aim contributors by submitting your first pull request. Happy coding! 😊

Made with contrib.rocks.

More questions?

1. Read the docs
2. Open a feature request or report a bug
3. Join Discord community server