Deployments Overview

Within a project, users can access the Deployments section to manage their trained or exported models. This section allows the creation of new deployments and provides a comprehensive overview of current deployments.

Upon entering the page, users will see their account and credit details, along with a chart visualizing the status of their deployments. Deployments can fall under three categories: successful, error, or hibernated. The chart provides a clear breakdown of these statuses.

Deployemnt main page

Below the chart, a table offers detailed information for each deployment, including:

  • Deployment Name

  • Status (Deployed / Error / Hibernated)

  • Optimized (Yes / No)

  • Runtime

  • Number of Instances

  • Auto Shutdown (Enabled / Disabled)

  • Autoscaling (Enabled / Disabled)

  • GPU Machine (Yes / No)

  • Hibernation Threshold

  • Last Updated

By selecting a deployment from the table, users can either delete or rename the deployment. Once a deployment is selected, a three-dot menu will appear, providing options to perform these actions. Additionally, the table can be sorted, filtered, and exported using the buttons on the top left.

Note

Work with deployments using our Python SDK for automation or through the platform interface - choose what works best for you!