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AI Evaluation & Observability

Aim

Aim is an open-source, self-hosted experiment tracker that logs machine-learning training runs and AI metadata, provides a UI to explore and compare them, and offers an API and SDK for programmatic queries.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Aim — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Aim during model development when you want run data recorded alongside training and need a UI or programmatic access to compare and inspect experiments.

Who it is for

  • Machine-learning practitioners who need to compare training runs and inspect their logged metadata.
  • Developers who want to query experiment records programmatically or analyze them in notebooks.

Documented uses

  • Log training-run parameters and metrics from Python code.
  • Explore and compare recorded runs in a UI.
  • Query tracked metadata for automation or notebook analysis.
  • Bring existing TensorBoard, MLflow or Weights & Biases logs into Aim using its converters.
Before you choose

Requirements & limits

The project describes Aim as open source under the Apache-2.0 license and self-hosted. Its README lists Linux and macOS support and Python 3.7 or later.

  • The documented setup instruments training code using Aim's Python SDK; its quick start does not replace the training framework.
  • The README lists built-in integrations and migration converters, but says custom and complex migration scenarios may require an SDK-based conversion script.
  • The repository describes enterprise support beyond core Aim; the cited material does not specify its terms.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Add run and metric tracking to a Python training workflow

Install Aim, log parameters and metrics from a training script, then start its UI.

Before you start

  • Python 3.7 or later, as listed by the repository README.
  • A training environment and Python code to instrument.

Steps

  1. Install Aim in the training environment with pip3 install aim.

  2. Import Run from aim and initialize a run with Run().

  3. Log parameters on the run, then record metrics with run.track(), including a metric name and step; the quick-start example also supplies context.

  4. Run the training as usual and start the Aim UI with aim up.

Expected outcome

The example records run parameters and metrics for exploration in Aim's UI.

Guide limitations

  • The quick start demonstrates scalar loss and accuracy metrics; other supported object types are listed separately in the documentation.
  • This recipe covers the README's quick-start path, not deployment or a guarantee for a particular environment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
10 October 2026
Original website title
aimhubio/aim · Official homepage title · retained in its source language
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