This section explains what data sources are and how to create and use them.
Data sources are a type of workload assets and represent a location where data is actually stored. They may represent a remote data location, such as NFS, Git, or S3, or a Kubernetes local resource, such as PVC, ConfigMap, HostPath, or Secret.
This configuration simplifies the mapping of the data into the workload’s file system and handles the mounting process during workload creation for reading and writing. These data sources are reusable and can be easily integrated and used by AI practitioners while submitting workloads across various scopes.
Data Sources Table
The data sources table can be found under Workload manager in the NVIDIA Run:ai platform.
The data sources table provides a list of all the data sources defined in the platform and allows you to manage them.
Note
Data & storage - with Data sources and Data volumes - is visible only if your Administrator has enabled Data volumes.
The data sources table comprises the following columns:
Column
Description
Data source
The name of the data source
Description
A description of the data source
Type
The type of data source connected – e.g., S3 bucket, PVC, or others
Status
The different lifecycle phases and representation of the data source condition
Scope
The scope of the data source within the organizational tree. Click the scope name to view the organizational tree diagram
Kubernetes name
The unique name of the data sources Kubernetes name as it appears in the cluster
Workload(s)
The list of existing workloads that use the data source
Template(s)
The list of workload templates that use the data source
Created by
The user who created the data source
Creation time
The timestamp for when the data source was created
Cluster
The cluster that the data source is associated with
Data Sources Status
The following table describes the data sources' condition and whether they were created successfully for the selected scope.
Status
Description
No issues found
No issues were found while creating the data source
Issues found
Issues were found while propagating the data source credentials
Issues found
The data source couldn’t be created at the cluster
Creating…
The data source is being created
No status / “-”
When the data source’s scope is an account, the current version of the cluster is not up to date, or the asset is not a cluster-syncing entity, the status can’t 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 a New Data Source
To create a new data source:
Click +NEW DATA SOURCE
Select the data source type from the list. Follow the step-by-step guide for each data source type:
NFS
A Network File System (NFS) is a Kubernetes concept used for sharing storage in the cluster among different pods. Like a PVC, the NFS volume’s content remains preserved, even outside the lifecycle of a single pod. However, unlike PVCs, which abstract storage management, NFS provides a method for network-based file sharing. The NFS volume can be pre-populated with data and can be mounted by multiple pod writers simultaneously. At NVIDIA Run:ai, an NFS-type data source is an abstraction that is mapped directly to a Kubernetes NFS volume. This integration allows multiple workloads under various scopes to mount and present the NFS data source.
Select the cluster under which to create this data source
Enter a name for the data source. The name must be unique.
Optional: Provide a description of the data source
Set the data origin
Enter the NFS server (host name or host IP)
Enter the NFS path
Set the data target location
Container path
Optional: Restrictions
Prevent data modification - When enabled, the data will be mounted with read-only permissions
Click CREATE DATA SOURCE
PVC
A Persistent Volume Claim (PVC) is a Kubernetes concept used for managing storage in the cluster, which can be provisioned by an administrator or dynamically by Kubernetes using a StorageClass. PVCs allow users to request specific sizes and access modes (read/write once, read-only many).
NVIDIA Run:ai ensures that data remains consistent and accessible across various scopes and workloads, beyond the lifecycle of individual pods, which is efficient while working with large datasets typically associated with AI projects.
Select the cluster under which to create this data source
Enter a name for the data source. The name must be unique.
Optional: Provide a description of the data source
Select PVC:
Existing PVC
This option is relevant when the purpose is to create a PVC-type data source based on an existing PVC in the cluster
Select a PVC from the list - (The list is empty if no existing PVCs were created in advance)
New PVC - creates a new PVC in the cluster. New PVCs are not added to the Existing PVCs list.
When creating a PVC-type data source and selecting the ‘New PVC’ option, the PVC is immediately created in the cluster (even if no workload has requested this PVC).
Select the storage class
None - Proceed without defining a storage class
Custom storage class - This option applies when selecting a storage class based on existing storage classes.
To add new storage classes to the storage class list, and for additional information, check Kubernetes storage classes
Select the access mode(s) (multiple modes can be selected)
Read-write by one node - The volume can be mounted as read-write by a single node.
Read-only by many nodes - The volume can be mounted as read-only by many nodes.
Read-write by many nodes - The volume can be mounted as read-write by many nodes.
Set the claim size and its units
Select the volume mode
File system (default) - allows the volume to be mounted as a filesystem, enabling the usage of directories and files.
Block - exposes the volume as a block storage, which can be formatted or used by applications directly without a filesystem.
Set the data target location
container path
Optional: Prevent data modification - When enabled, the data will be mounted with read-only permission.
Click CREATE DATA SOURCE
After the data source is created, check its status to monitor its proper creation across the selected scope.
S3 Bucket
The S3 bucket data source enables the mapping of a remote S3 bucket into the workload’s file system. Similar to a PVC, this mapping remains accessible across different workload executions, extending beyond the lifecycle of individual pods. However, unlike PVCs, data stored in an S3 bucket resides remotely, which may lead to decreased performance during the execution of heavy machine learning workloads. As part of the NVIDIA Run:ai connection to the S3 bucket, you can create credentials in order to access and map private buckets.
Note
S3 data sources are not supported for custom inference workloads.
Select the cluster under which to create this data source
Enter a name for the data source. The name must be unique.
Optional: Provide a description of the data source
Set the data origin
Set the S3 service URL
Select the credential
None - for public buckets
Credential names - This option is relevant for private buckets based on existing credentials that were created for the scope.
To add new credentials to the credentials list, and for additional information, check the Credentials article.
Enter the bucket name
Set the data target location
container path
Click CREATE DATA SOURCE
After a private data source is created, check its status to monitor its proper creation across the selected scope.
Git
A Git-type data source is a NVIDIA Run:ai integration, that enables code to be copied from a Git branch into a dedicated folder in the container. It is mainly used to provide the workload with the latest code repository. As part of the integration with Git, in order to access private repositories, you can add predefined credentials to the data source mapping.
Select the cluster under which to create this data source
Enter a name for the data source. The name must be unique.
Optional: Provide a description of the data source
Set the data origin
Set the Repository URL
Set the Revision (branch, tag, or hash)- If left empty, it will use the 'HEAD' (latest)
Select the credential
None - for public repositories
Credential names - This option applies to private repositories based on existing credentials that were created for the scope.
To add new credentials to the credentials list, and for additional information, check the Credentials article.
Set the data target location
container path
Click CREATE DATA SOURCE
After a private data source is created, check its status to monitor its proper creation across the selected scope.
Host path
A Host path volume is a Kubernetes concept that enables mounting a host path file or a directory on the workload’s file system. Like a PVC, the host path volume’s data persists across workloads under various scopes. It also enables data serving from the hosting node.
Select the cluster under which to create this data source
Enter a name for the data source. The name must be unique.
Optional: Provide a description of the data source
Set the data origin
host path
Set the data target location
container path
Optional: Prevent data modification - When enabled, the data will be mounted with read-only permissions.
Click CREATE DATA SOURCE
ConfigMap
A ConfigMap data source is a NVIDIA Run:ai abstraction for the Kubernetes ConfigMap concept. The ConfigMap is used mainly for storage that can be mounted on the workload container for non-confidential data. It is usually represented in key-value pairs (e.g., environment variables, command-line arguments etc.). It allows you to decouple environment-specific system configurations from your container images, so that your applications are easily portable. ConfigMaps must be created on the cluster prior to being used within the NVIDIA Run:ai system.\
Select the cluster under which to create this data source
Enter a name for the data source. The name must be unique.
Optional: Provide a description of the data source
Set the data origin
Select the ConfigMap name (The list is empty if no existing ConfigMaps were created in advance).
Set the data target location
container path
Click CREATE DATA SOURCE
Secret
A secret-type data source enables the mapping of a credential into the workload’s file system. Credentials are a workload asset that simplify the complexities of Kubernetes Secrets. The credentials mask sensitive access information, such as passwords, tokens, and access keys, which are necessary for gaining access to various resources.
Select the cluster under which to create this data source
Enter a name for the data source. The name must be unique.
Optional: Provide a description of the data source
Set the data origin
Select the credential
To add new credentials, and for additional information, check the Credentials article.
Set the data target location
container path
Click CREATE DATA SOURCE
After the data source is created, check its status to monitor its proper creation across the selected scope.
Note
It is also possible to add data sources directly when creating a specific workspace, training or inference workload.
Copying a Data Source
To copy an existing data source:
Select the data source you want to copy
Click MAKE A COPY
Enter a name for the data source. The name must be unique.
Update the data source and click CREATE DATA SOURCE
Renaming a Data Source
To rename an existing data source:
Select the data source you want to rename
Click Rename and edit the name/description
Deleting a Data Source
To delete a data source:
Select the data source you want to delete
Click DELETE
Confirm you want to delete the data source
Note
It is not possible to delete a data source being used by an existing workload or template.
Creating PVCs in Advance
Add PVCs in advance to be used when creating a PVC-type data source via the NVIDIA Run:ai UI.
The actions taken by the admin are based on the scope (cluster, department or project) that the admin wants for data source of type PVC. Follow the steps below for each required scope:
Cluster Scope
Locate the PVC in the NVIDIA Run:ai namespace (runai)
Provide NVIDIA Run:ai with visibility and authorization to share the PVC to your selected scope by implementing the following label: run.ai/cluster-wide: "true”
The PVC is now displayed for that scope in the list of existing PVCs.
Note
This step is also relevant for creating the data source of type PVC via API
Department Scope
Locate the PVC in the NVIDIA Run:ai namespace (runai)
To authorize NVIDIA Run:ai to use the PVC, label it: run.ai/department: "id"
The PVC is now displayed for that scope in the list of existing PVCs.
Project Scope
Locate the PVC in the project’s namespace.
The PVC is now displayed for that scope in the list of existing PVCs.
Creating ConfigMaps in Advance
Add ConfigMaps in advance to be used when creating a ConfigMap-type data source via the NVIDIA Run:ai UI.
Cluster Scope
Locate the ConfigMap in the NVIDIA Run:ai namespace (runai)
To authorize NVIDIA Run:ai to use the ConfigMap, label it: run.ai/cluster-wide: "true”
The ConfigMap must have a label of run.ai/resource: <resource-name>
The ConfigMap is now displayed for that scope in the list of existing ConfigMaps.
Department Scope
Locate the ConfigMap in the NVIDIA Run:ai namespace (runai)
To authorize NVIDIA Run:ai to use the ConfigMap, label it: run.ai/department: "<department-id>"
The ConfigMap must have a label of run.ai/resource: <resource-name>
The ConfigMap is now displayed for that scope in the list of existing ConfigMaps.
Project Scope
Locate the ConfigMap in the project’s namespace
The ConfigMap must have a label of run.ai/resource: <resource-name>
The ConfigMap is now displayed for that scope in the list of existing ConfigMaps.
Using API
To view the available actions, go to the Data sources API reference.
