Manage custom applications¶

The Applications page in the Registry lists all custom applications available to you from the All applications tab. The table below describes the elements and available actions from this page:

Share applications¶

The sharing capability allows you to manage permissions and share an application with users, groups, and organizations, as well as recipients outside of DataRobot. This is useful, for example, for allowing others to use your application without requiring them to have the expertise to create one.

You can access sharing functionality from the actions menu in the Apps workshop. Click the menu icon () next to the app you want to share and select Share ().

This opens the Share dialog, which lists each associated user and their role. Editors can share an application with one or more users or groups, or the entire organization. Additionally, you can share an application externally with a sharing link.

The following actions are also available in the Share dialog:

• To remove a user, click the X button to the right of their role.

• To re-assign a user's role, click the assigned role and assign a new one from the dropdown.

  

Delete an application¶

If you have the appropriate permissions, you can delete an application by opening the menu () and clicking Delete ().

Application sources¶

An application source contains the files, dependencies, and environment from which a custom app can be built. On the Applications page, select the Application sources tab to view all the sources that you can build custom applications from.

Add an application source¶

To create a new application source from the Applications page, click Add > New application source.

The new application source is immediately added to the page. Select the new application source to begin configuring it.

Configure an application source¶

After selecting an application source, you can choose its base environment, upload files to the source, and create runtime parameters.

Whenever you edit any of these components of an application source, you create a new version of the source. You can select any version of a source from the Version dropdown.

Environment¶

Custom apps run inside of environments (Docker containers). Environments include the packages, language, and system libraries used by the custom app. Select a DataRobot-provided environment for the application source from the dropdown under the Environment header. DataRobot offers a predefined base environment named [Experimental] Python 3.9 Streamlit.

Files¶

In the Files section, you can assemble the files that make up the custom application source. Drag files into the box, or use the options in this section to create or upload the files required to assemble a custom job:

If you choose to create a blank text file, enter the information into the file, name it using a full path (including the folder it belongs to and the file extension), then click Save.

Resources¶

After creating an application source, you can configure the resources an application consumes to minimize potential environment errors in production. DataRobot allows you to customize resource limits and the replicas number. To edit the resources bundle:

1. Select an application source. In the Resources section, click () Edit:

   

2. In the Update resources dialog box, configure the following settings:

   

   Setting	Description
   Bundle	Select a resource bundle from the dropdown that determines the maximum amount of memory and CPU that can be allocated for a custom application.
   Replicas	Sets the number of replicas executed in parallel to balance workloads when a custom application is running. The default value is 1, and the maximum value is 4.

3. Once you have configured the resource settings for the application source, click Save.

Runtime parameters¶

Create and define runtime parameters used by the custom app built from the application source.

You can add runtime parameters to a custom app by including them in a metadata.yaml file, making your custom app easier to reuse. A template for this file is available from Files > Create dropdown.

To define runtime parameters, you can add the following runtimeParameterDefinitions in metadata.yaml:

name: runtime-parameter-example
type: inference
targetType: regression

runtimeParameterDefinitions:
- fieldName: my_first_runtime_parameter
  type: string
  description: My first runtime parameter.

- fieldName: runtime_parameter_with_default_value
  type: string
  defaultValue: Default
  description: A string-type runtime parameter with a default value.

- fieldName: runtime_parameter_boolean
  type: boolean
  defaultValue: true
  description: A boolean-type runtime parameter with a default value of true.

- fieldname: runtime_parameter_numeric
  type: numeric
  defaultValue: 0
  minValue: -100
  maxValue: 100
  description: A boolean-type runtime parameter with a default value of 0, a minimum value of -100, and a maximum value of 100.

- fieldName: runtime_parameter_for_credentials
  type: credential
  allowEmpty: false
  description: A runtime parameter containing a dictionary of credentials.

The credential runtime parameter type supports any credentialType value available in the DataRobot REST API. The credential information included depends on the credentialType, as shown in the examples below:

basic:
  credentialType: basic
  description: string
  name: string
  password: string
  user: string
        

azure:
  credentialType: azure
  description: string
  name: string
  azureConnectionString: string
        

gcp:
  credentialType: gcp
  description: string
  name: string
  gcpKey: string
        

s3:
  credentialType: s3
  description: string
  name: string
  awsAccessKeyId: string
  awsSecretAccessKey: string
  awsSessionToken: string
        

api_token:
  credentialType: api_token
  apiToken: string
  name: string