Deploying and managing ML services using Titan

Titan Tutorial #3: Getting started with Titan’s CLI

Titan’s CLI (Command Line Interface) is the main tool to manage models and services in our product.

$titan help 
Usage:titan [<flags>] <command> [<args> …]
Flags:-h, — help Output usage information.-C, — chdir=”.” Change working directory.-v, — verbose Enable verbose log output.— version Show application version.
Commands:help               Show help for a command.config             Show current config manifest.deploy             Deploy a Jupyter Notebook in the               Open documentation website in a browser.login              Authorize user.logs               Inspect service               Open service endpoint URL in a list      List deployed start     Start stop      Stop restart   Restart delete    Delete service.upgrade            Install the latest or specified version of Titan.version            Show version.
$titan login
$titan config
$titan docs
$titan deploy
  • Require the user to select which is the desired environment to run the model
Deploying a model with titan deploy
$titan open
$titan services
Checking which services are running
  • $titan services start Start a service.
  • $titan services stop Stop a service.
  • $titan services restart Restart a service.
  • $titan services delete Delete a service.
Stopping a service
Starting a service
Restarting a service
Deleting a service
$titan version
$titan upgrade


In this third post of our series of tutorials we have seen how to use Titan’s CLI to deploy models and to manage services with an expressive and simple command set.

Next Tutorial

Be sure to check our next tutorial, where we create and deploy an Image Detection model from scratch!


Titan can help you to radically reduce and simplify the effort required to put AI/ML models into production, enabling Data Science teams to be agile, more productive and closer to the business impact of their developments.

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