Deploying Trains Overview

This page describes the Trains open source suite deployment options, which include:

  • The demo Trains Server, which works with Trains by default, is open to anyone, and resets periodically deleting data.
  • A self-hosted Trains Server
  • Trains Agent (our DevOps solution for experiment execution and automation)

The Trains Python Client Package

To install and optionally set configuration options:

  1. Install the Trains package.

    pip install trains
    

    Now, just add two lines of code to your Python experiment script.

    from trains import Task
    task = Task.init(project_name="my project", task_name="my task")
    

    When you run your script, Trains automagically monitors and logs its results which you can track and compare in the demo Trains Server (https://demoapp.trains.allegro.ai/dashboard) Trains Web-App (UI).

  2. Optionally, configure Trains options (for example, cache, metrics, and log settings), see the Trains Configuration Reference.

Trains Server and Trains Agent deployment options

Option 1. The demo Trains Server, only.

Automagically track, analyze, and compare experiments using the demo Trains Web-App (UI).

Our demo Trains Server is periodically refreshed and data is deleted.

The Trains Python Client package automatically works with the demo Trains Server.

Optionally, configure Trains options (for example, cache, metrics, and log settings), see the Trains Configuration Reference.

Option 2. The demo Trains Server and Trains Agent

Manage your remote experiment execution using Trains Agent (see workers and queues), but not your own backend infrastructure.

To install and optionally set configuration options:

  1. Install and Configure Trains Agent.
  2. Optionally, configure Trains options (for example, cache, metrics, and log settings), see the Trains Configuration Reference.

Option 3. A self-hosted Trains Server

Manage your own Trains backend infrastructure, including your own Trains Web-App (UI).

Overview of deployment:

  1. Deploy your own Trains Server, using one of the available formats which include:

  2. Optionally, configure Trains Server for additional features, including sub-domains and load balancers, web login authentication, and the non-responsive task watchdog.

  3. Configure Trains for Trains Server.

Option 4. A self-hosted Trains Server and Trains Agent.

Manage your own Trains backend infrastructure, and manage your remote experiment execution using Trains Agent.

Overview of deployment:

  1. Perform the steps in Option 3.
  2. Install and Configure Trains Agent.