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AimStack offers enterprise support that's beyond core Aim. Contact via hello@aimstack.io e-mail.
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 🎯
| Log Metadata Across Your ML Pipeline 💾 | Visualize & Compare Metadata via UI 📊 |
|---|---|
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| Run ML Trainings Effectively ⚡ | Organize Your Experiments 🗂️ |
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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. |
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 |
Follow the steps below to get started with Aim.
pip3 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" })
See the full list of supported trackable objects(e.g. images, text, etc) here.
aim up
Aim has built-in converters to easily migrate logs from other tools. These migrations cover the most common usage scenarios. In case of custom and complex scenarios you can use Aim SDK to implement your own conversion script.
Aim easily integrates with a wide range of ML frameworks, providing built-in callbacks for most of them.
Aim Python SDK empowers you to query and access any piece of tracked metadata with ease.
from aim import Repo
my_repo = Repo('/path/to/aim/repo')
query = "metric.name == 'loss'" # Example query
# Get collection of metrics
for run_metrics_collection in my_repo.query_metrics(query).iter_runs():
for metric in run_metrics_collection:
# Get run params
params = metric.run[...]
# Get metric values
steps, metric_values = metric.values.sparse_numpy()
Aim remote tracking server allows running experiments in a multi-host environment and collect tracked data in a centralized location.
See the docs on how to set up the remote server.
Read the full documentation on aimstack.readthedocs.io 📖
Training run comparison
Order of magnitude faster training run comparison with Aim
Scalability
Beloved TB visualizations to be added on Aim
MLFlow is an end-to-end ML Lifecycle tool. Aim is focused on training tracking. The main differences of Aim and MLflow are around the UI scalability and run comparison features.
Aim and MLflow are a perfect match - check out the aimlflow - the tool that enables Aim superpowers on Mlflow.
Run comparison
UI Scalability
Hosted vs self-hosted
The Aim product roadmap :sparkle:
Backlog contains the issues we are going to choose from and prioritize weeklyThe high-level features we are going to work on the next few months:
In progress
Aim UI
SDK and Storage
Integrations
Add Aim badge to your README, if you've enjoyed using Aim in your work:
[](https://github.com/aimhubio/aim)
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}
}
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.
FAQs
A super-easy way to record, search and compare AI experiments.
We found that aim demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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