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Track and compare research experiments with Aim experiment tracker
Passed 22 of 23 test cases with the skill, and 20 of 23 without it.
2 fixed20 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
Where is experiment data stored locally when using Aim, and what CLI command upgrades the repository storage format to version 3.0?
| Checked | Without skill | With skill |
|---|---|---|
| The response identifies .aim as the storage location. | Passed | Passed |
| The upgrade command specifies aim storage --repo . upgrade 3.0. | Missed | Passed |
The instructions your model reads when the skill loads.
Aim is an open-source experiment tracking platform designed for researchers and ML engineers who need to log, compare, and analyze large numbers of experiments. Unlike cloud-based tracking services that require sending data to external servers, Aim runs entirely on your own infrastructure, making it suitable for research environments with data privacy requirements or institutional restrictions on external services.
The core problem Aim solves is experiment management at scale. A typical research project involves hundreds or thousands of training runs with different hyperparameters, data splits, model architectures, and random seeds. Without systematic tracking, researchers lose track of which configurations produced which results, leading to wasted computation and unreproducible findings. Aim provides a high-performance storage backend and a rich web UI for logging, querying, and visualizing experiment metadata and metrics.
With over 6,000 GitHub stars, Aim has established itself as a compelling self-hosted alternative to tools like Weights and Biases and MLflow. Its Python-native API integrates with minimal friction into existing training loops, and the query language enables sophisticated filtering across thousands of runs.
Install Aim via pip:
pip install aim
Initialize an Aim repository in your project directory:
cd /path/to/research-project
aim init
This creates a .aim directory that stores all experiment data locally. Launch the web UI:
aim up
The dashboard becomes available at http://localhost:43800, providing interactive visualizations of all tracked experiments.
For remote server deployment:
aim up --host 0.0.0.0 --port 43800
test cases passed, vs 20 / 23 without
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