Aim is built around several concepts allowing to make sure that it meets the following criteria:

  • Run data isolation. Each training run process isolated in terms of data and do not require additional services to run.

  • Scalability. Aim web app is able to handle 1000s of training runs. Starting from v3.4.0 Aim provides a Remote Tracking server allowing to run multiple parallel experiments in a distributed multi-host environment.

  • Flexibility. Aim UI and query language allow users to select, group and filter the tracked data any way they want.

Aim Components

In order to understand how Aim works, lets take a quick look on a different components it has.

  • Aim Storage. At its core Aim uses a custom-built storage, based on rocksdb. More details in ‘Where is data collected?’. Data tracked by different training runs collected and indexed in an aim repository (.aim). The storage itself is generic; it allows accessing the data as collection of dictionaries and arrays.

  • Aim SDK. On top of the storage Aim SDK provides functionality to track/select/query data. Additionally, SDK is a layer used by Web APIs and CLI.

  • Aim UI. Web app allowing to browse run metadata, metrics, images and other tracked data.

  • Aim CLI. A collection of command line utilities for running Aim web server, managing aim repositories, runs, etc.

  • Remote Tracking server. A gRPC-based service accepting incoming traffic and storing data on a centralized server.

The next sections will describe various concepts Aim introduces and provide more detailed look on individual components introduced above.