Run 22028 tw display the data sources k8s pvc name in runai UI

docs/platform-admin/workloads/assets/credentials.md

@@ -8,7 +8,7 @@ Essentially, credentials enable AI practitioners to access relevant protected re
 
 ## Credentials table
 
-The Credentials table can be found under __Credentials__ in the Run:ai User interface.
+The Credentials table can be found under __Workload manager__ in the Run:ai User interface.
 
 The Credentials table provides a list of all the credentials defined in the platform and allows you to manage them.
 
@@ -22,9 +22,11 @@ The Credentials table comprises the following columns:
 | Description | A description of the credentials |
 | Type | The type of credentials, e.g., Docker registry |
 | Status | The different lifecycle phases and representation of the credentials’ condition |
+| Scope | The [scope](./overview.md#asset-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 credentials Kubernetes name as it appears in the cluster |
+| Environment(s) | The environment(s) that are associated with the credentials |
 | Data source(s) | The private data source(s) that are accessed using the credentials |
 | Created by | The user who created the credentials |
-| Scope | The [scope](./overview.md#asset-scope) of this compute resource within the organizational treeClick the name of the scope to view the organizational tree diagram |
 | Creation time | The timestamp of when the credentials were created |
 | Cluster | The cluster with which the credentials are associated |
 
@@ -47,6 +49,7 @@ The following table describes the credentials’ condition and whether they were
 *   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
@@ -154,7 +157,7 @@ To delete a credential:
 
 1.  Select the credential you want to delete
 2.  Click __DELETE__
-3.  In the dialog, click __DELETE__ to confirm the deletion
+3.  In the dialog, click __DELETE__ to confirm
 
 !!! Note
       Credentials cannot be deleted if they are being used by a workload and template.
@@ -173,6 +176,7 @@ To use the secret directly from within the container, you can choose between the
 
 1. Get the secret mounted to the file system by using the [Generic secret](./datasources.md#secret) data source
 2. 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.
 

docs/platform-admin/workloads/assets/datasources.md

@@ -7,7 +7,7 @@ This configuration simplifies the mapping of the data into the workload’s file
 
 ## Data sources table
 
-The data sources table can be found under __Data sources__ in the Run:ai platform.
+The data sources table can be found under __Workload manager__ in the 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.
 
@@ -22,6 +22,7 @@ The data sources table comprises the following columns:
 | 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](./overview.md#asset-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 |
@@ -36,7 +37,7 @@ The following table describes the data sources' condition and whether they were
 | --- | --- |
 | 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 cluster could not be accessed |
+| 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 |
 
@@ -75,7 +76,7 @@ A Network File System ([NFS](https://kubernetes.io/docs/concepts/storage/volumes
 
 ### PVC
 
-A Persistent Volume Claim ([PVC](https://kubernetes.io/docs/concepts/storage/persistent-volumes/){target=_blank}) 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). At Run:ai, a PVC-type data source is an abstraction that is mapped directly to a Kubernetes PVC. By leveraging PVCs as data sources, Run:ai enables access to persistent storage for workloads, ensuring that data remains consistent and accessible across various scopes and workloads, beyond the lifecycle of individual pods. This ensures that data generated by AI workloads is not lost when pods are rescheduled or updated, providing a seamless and efficient storage solution that can handle the large datasets typically associated with AI projects.
+A Persistent Volume Claim ([PVC](https://kubernetes.io/docs/concepts/storage/persistent-volumes/){target=_blank}) 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). 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.
 
 1.  Select the __cluster__ under which to create this data source
 2.  Select a [scope](./overview.md#asset-scope)
@@ -97,6 +98,8 @@ A Persistent Volume Claim ([PVC](https://kubernetes.io/docs/concepts/storage/per
     *   __Read-write by many nodes__ - The volume can be mounted as read-write by many nodes.
 8.  Set the __claim size__ and its __units__
 9.  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.
 10.  Set the data target location
     *   __container path__
 11.  Optional: __Prevent data modification__ - When enabled, the data will be mounted with read-only permission.
@@ -214,6 +217,8 @@ To delete a data source:
 !!! Note
     It is not possible to delete an environment being used by an existing workload or template.
 
+
+
 ## Using API
 
 To view the available actions, go to the [Data sources](https://app.run.ai/api/docs#tag/Datasources) API reference.