Credentials
This section explains what credentials are and how to create and use them.
Credentials are workload assets that simplify the complexities of Kubernetes secrets. They consist of and mask sensitive access information, such as passwords, tokens, and access keys, which are necessary for gaining access to various resources.
Credentials are crucial for the security of AI workloads and the resources they require, as they restrict access to authorized users, verify identities, and ensure secure interactions. By enforcing the protection of sensitive data, credentials help organizations comply with industry regulations, fostering a secure environment overall.
Essentially, credentials enable AI practitioners to access relevant protected resources, such as private data sources and Docker images, thereby streamlining the workload submission process.
Credentials table
The Credentials table can be found under Workload manager in the NVIDIA Run:ai User interface.
The Credentials table provides a list of all the credentials defined in the platform and allows you to manage them.

The Credentials table comprises the following columns:
Credential
The name of the credential
Description
A description of the credential
Type
The type of credential, e.g., Docker registry
Status
The different lifecycle phases and representation of the credential's condition
Scope
The scope of this compute resource within the organizational tree. Click the name of the scope to view the organizational tree diagram
Kubernetes name
The unique name of the credential's Kubernetes name as it appears in the cluster
Environment(s)
The environment(s) that are associated with the credential
Data source(s)
The private data source(s) that are accessed using the credential
Created by
The user who created the credential
Creation time
The timestamp of when the credential were created
Cluster
The cluster with which the credential are associated
Credentials status
The following table describes the credentials’ condition and whether they were created successfully for the selected scope.
No issues found
No issues were found while creating the credential (this status may change while propagating the credential to the selected scope)
Issues found
Issues found while propagating the credential
Issues found
Failed to access the cluster
Creating…
Credential is being created
Deleting…
Credential is being deleted
No status
When the credential's scope is an account, or the current version of the cluster is not up to date, the status cannot be displayed
Customizing the table view
Filter - Click ADD FILTER, select the column to filter by, and enter the filter values
Search - Click SEARCH and type the value to search by
Sort - Click each column header to sort by
Column selection - Click COLUMNS and select the columns to display in the table
Download table - Click MORE and then click ‘Download as CSV’. Export to CSV is limited to 20,000 rows.
Refresh - Click REFRESH to update the table with the latest data
Adding new credentials
Creating credentials is limited to specific roles.
To add a new credential:
Go to the Credentials table
Click +NEW CREDENTIAL
Select the credential type from the list Follow the step-by-step guide for each credential type:
Editing credentials
To rename a credential:
Select the credential from the table
Click Rename to edit its name and description
Deleting credentials
To delete a credential:
Select the credential you want to delete
Click DELETE
In the dialog, click DELETE to confirm
Using credentials
You can use credentials (secrets) in various ways within the system
Access private data sources
To access private data sources, attach credentials to data sources of the following types: Git, S3 Bucket
Use directly within the container
To use the secret directly from within the container, you can choose between the following options
Get the secret mounted to the file system by using the Generic secret data source
Get the secret as an environment variable injected into the container. There are two equivalent ways to inject the environment variable.
a. By adding it to the Environment asset. b. By adding it ad-hoc as part of the workload.
Creating secrets in advance
Add secrets in advance to be used when creating credentials via the NVIDIA Run:ai UI. Follow the steps below for each required scope:
Cluster scope:
Create the secret in the NVIDIA Run:ai namespace (
runai
)To authorize NVIDIA Run:ai to use the secret, label it:
run.ai/cluster-wide: "true"
Label the secret with the correct credential type:
Docker registry -
run.ai/resource: "docker-registry"
Access key -
run.ai/resource: "access-key"
Username and password -
run.ai/resource: "password"
Generic secret -
run.ai/resource: "generic"
The secret is now displayed for that scope in the list of existing secrets.
Department scope:
Create the secret in the NVIDIA Run:ai namespace (
runai
)To authorize NVIDIA Run:ai to use the secret, label it:
run.ai/department: "<department_id>"
Label the secret with the correct credential type:
Docker registry -
run.ai/resource: "docker-registry"
Access key -
run.ai/resource: "access-key"
Username and password -
run.ai/resource: "password"
Generic secret -
run.ai/resource: "generic"
The secret is now displayed for that scope in the list of existing secrets.
Project scope:
Create the secret in the project’s namespace
Label the secret with the correct credential type:
Docker registry -
run.ai/resource: "docker-registry"
Access key -
run.ai/resource: "access-key"
Username and password -
run.ai/resource: "password"
Generic secret -
run.ai/resource: "generic"
Using API
To view the available actions, go to the Credentials API reference
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