{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Aim logs training runs and AI metadata, then provides a web UI and Python API to compare, visualize, and query experiments programmatically.","skillfed_tags":["experiment-tracking","ml-monitoring","self-hosted"],"use_cases":["Log and compare hyperparameter sweeps across training runs to identify the best configuration","Track metrics, images, and system resource usage during model training to spot training issues in real time","Query experiment metadata programmatically in a Jupyter notebook to analyze trends across runs","Centralize experiment history for a team so all members can browse and filter runs by tags and parameters","Migrate existing experiment logs from another tracker into Aim's queryable format for unified analysis"],"what_it_does":"Aim is an open-source ML experiment tracker designed to log training runs and arbitrary AI metadata, then make that data queryable and explorable. It provides both a web-based UI for visual comparison and filtering of runs, and a Python SDK for programmatic access to tracked metadata\u2014useful for automation, analysis in Jupyter notebooks, or building custom dashboards. The package bundles a full web application stack (FastAPI, SQLAlchemy, Alembic) and specialized storage layers (aimrocks, aimrecords) to handle tracking of experiments.\n\nThe core workflow is straightforward: create a Run object, log metrics and parameters as you train, then browse or query results through the UI or API. Aim handles system monitoring, real-time alerting, and run organization via tags and experiments. It integrates with popular ML frameworks and supports migration from other trackers. However, the large dependency footprint and aging maintenance status mean you should verify compatibility with your Python version and check whether the known security vulnerabilities affect your use case.","worth_installing":"Yes, with conditions. Aim is suitable if you need a self-hosted, open-source experiment tracker with a polished UI and Python query API for ML teams. Install it if you can tolerate the medium dependency footprint and are willing to verify the four known security vulnerabilities do not affect your threat model. Avoid if you require active maintenance and rapid security patches\u2014the 463-day gap since the last release signals aging support."},"id":"aim","links":{"html":"https://skillfed.io/packages/aim","md":"https://skillfed.io/packages/aim.md","pypi":"https://pypi.org/project/aim/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-05-08","license_spdx":null,"license_treatment":"permissive","name":"aim","python_support":"supports_current","summary":"A super-easy way to record, search and compare AI experiments."},"popularity":{"monthly_downloads":192207,"position":9866,"tier":"top_15000"},"security":{"n_vulnerabilities":4},"version":"3.29.1"}