PrimeHub
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    • Configuration
      • How to configure PrimeHub
      • Multiple Jupyter Notebook Kernels
      • Configure SSH Server
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      • Configure Custom Image Build
      • Configure Model Deployment
      • Setup Self-Signed Certificate for PrimeHub
      • Chart Configuration
      • Configure PrimeHub Store
    • Environment Variables
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  • Info
  • Members
  • Jobs
  • Deployments
  • MLflow
  1. User Guide
  2. Group Admin

Settings

PreviousImagesNextGenerate an PrimeHub API Token

Last updated 2 years ago

Group Admin can view the current settings of the managed group which are configured by Platform Administrator. All of settings are viewable-only to Group Admin, except Default Timeout Setting of Jobs is configurable.

In order to change viewable-only settings, please request Platform Administrator.

Info

It displays the current settings, Name, Display Name, Shared Volume, User Quota and Group Quota of the working group.

Members

It displays group members and group administrators.

Jobs

  • Default Timeout Setting: Set Minutes / Hours / Days.

Group Admin can apply a group-wise Job timeout setting on every jobs submitted from the group. A running job will be cancelled when it exceeds the setting. This setting is able to be overwritten by each job submission for the customization. By default it is 7 days.

Deployments

It displays if Model Deployment is enabled to the group, i.e., if the group can use Deployments feature.

MLflow

PrimeHub provides Models feature by integrating with MLflow app instance. We can easily set up the MLflow app in the following steps:

  1. Click Create MLflow App link to create the MLflow app.

  2. After the MLflow app is successfully created, we can choose it from the Configure with Installed Apps selector. Both the required information MLflow Tracking URI and MLflow UI URI will be automatically filled.

  3. Click Save button to keep the setting for binding Models to the MLflow instance.

Furthermore, if we have another installed MLflow app instance, then we can learn App URL and Service Endpoint from the installed App detail.

  • Fill in MLflow Tracking URI with http://+Service Endpoint.

  • Fill in MLflow UI URI with App URL.

By integrating externally-hosted MLflow server, see for the detail.

Configuration