Allegro Trains Documentation
Trains is now ClearML
This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.
Trains is Allegro AI’s open source, experimentation and version control solution for data scientists, researchers, and engineers, working individually and collaborating in teams. Trains supports experiment tracking, analysis, reproducibility, comparison, tuning, automation, storage maintenance, and a variety of additional features.
Trains is a suite of open source Python packages and plugins, including:
Our Trains Python Package allows you to integrate Trains into your experiments with just two lines of code (see Quick Start) and get all of Trains robust automagical logging. Optionally, augment your Python experiment scripts with the powerful features and functionality that our Task, Logger, Model, Automation, and Storage classes provide.
Our backend infrastructure is Trains Server. It is available to you as either our demo Trains Server (https://demoapp.trains.allegro.ai/dashboard) or a self-hosted Trains Server. You can deploy your own Trains Server in a variety of formats, including pre-built Docker images for Linux, Windows 10, macOS, pre-built AWS EC2 AMIs, and Kubernetes standard installations or Kubernetes using Helm.
Trains Server includes your own Trains User Interface, a RESTful API, and a file server.
Pre-populated examples ready to enqueue
A self-hosted Trains Server installs with the example experiments located in the
trainsrepository, examples folder.
Trains Agent is our DevOps component for experiment execution, resource control, and automation. You can use Trains Agent with either the demo Trains Server or a self-hosted Trains Server (see Installing and Configuring Trains Agent and Trains Agent Reference).