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:

Install the Trains package by executing the following command:
```
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).
Optionally, configure Trains options (for example, cache, metrics, and log settings), see the Trains Configuration Reference.

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

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.

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:

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

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

Overview of deployment:

Deploy your own Trains Server, using one of the available formats which include:
- Pre-built AWS EC2 AMIs.
- Pre-built Google Cloud Platform AMIs.
- Pre-built Docker images for Linux, macOS, and Windows 10.
- Kubernetes and Kubernetes Using Helm.
Optionally, configure Trains Server for additional features, including sub-domains and load balancers, web login authentication, and the non-responsive task watchdog.
Configure Trains for Trains Server.

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

Overview of deployment: