Merge pull request #897 from run-ai/v2.18

Existing PVC

Author: JamieWeider72

.github/workflows/automated-publish-docs.yaml

@@ -25,7 +25,7 @@ jobs:
       - name: Get all v*.* branches
         id: calculate-env
         run: |
-          BRANCHES=$(git branch --list --all | grep -v master | grep 'origin/v*.*' | sed -n -E 's:.*/(v[0-9]+\.[0-9]+).*:\1:p' | sort -Vu)
+          BRANCHES=$(git branch -r | grep -E '^ *origin/v[0-9]{1,2}\.[0-9]{1,2}$' | sort -Vu | sed 's/origin\///g' | sed 's/ //g')
           NEWEST_VERSION=$(printf '%s\n' "${BRANCHES[@]}" | sort -V | tail -n 1)
           CURRENT_BRANCH=${GITHUB_REF#refs/heads/}
           ALIAS=$CURRENT_BRANCH-alias
@@ -50,7 +50,6 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: ${{ needs.env.outputs.CURRENT_BRANCH }}
-          fetch-depth: 0
 
       - name: setup python
         uses: actions/setup-python@v5
@@ -99,4 +98,5 @@ jobs:
           SLACK_MESSAGE_ON_SUCCESS: "Docs were updated successfully for version ${{ needs.env.outputs.TITLE }}. PR Link: ${{ github.event.pull_request.html_url }}"
           SLACK_MESSAGE_ON_FAILURE: "Docs update FAILED for version ${{ needs.env.outputs.TITLE }}. PR Link: ${{ github.event.pull_request.html_url }}"
           MSG_MINIMAL: true
-          SLACK_FOOTER: ""
+          SLACK_FOOTER: ""
+          

docs/Researcher/user-interface/workspaces/blocks/Existing PVC.md

@@ -13,7 +13,7 @@ Run the helm command below:
     ``` bash
     helm repo add runai-backend https://runai.jfrog.io/artifactory/cp-charts-prod
     helm repo update
-    helm upgrade -i runai-backend -n runai-backend runai-backend/control-plane --version "~2.17.0" \
+    helm upgrade -i runai-backend -n runai-backend runai-backend/control-plane --version "~2.18.0" \
         --set global.domain=<DOMAIN>  # (1)
     ```
 

docs/admin/runai-setup/self-hosted/k8s/backend.md

@@ -12,7 +12,7 @@ Run the helm command below:
     ``` bash
     helm repo add runai-backend https://runai.jfrog.io/artifactory/cp-charts-prod
     helm repo update
-    helm upgrade -i runai-backend -n runai-backend runai-backend/control-plane --version "~2.17.0" \
+    helm upgrade -i runai-backend -n runai-backend runai-backend/control-plane --version "~2.18.0" \
         --set global.domain=runai.apps.<OPENSHIFT-CLUSTER-DOMAIN> \ # (1)
         --set global.config.kubernetesDistribution=openshift
     ```

docs/admin/runai-setup/self-hosted/ocp/backend.md

@@ -124,9 +124,9 @@ To get the full experience of Run:ai’s environment and platform use the follow
 * [Distributed training](../../Researcher/user-interface/trainings.md#trainings)
 * Deployments.
 
-## Supported integrations
+## Workload-related Integrations
 
-To assist you with other platforms, and other types of workloads use the integrations listed below.
+To assist you with other platforms, and other types of workloads use the integrations listed below. These integrations are not regularly tested by Run:ai and are hence provided on an as-is basis. The link below point to the Run:ai customer portal. 
 
 1. [Airflow](https://runai.my.site.com/community/s/article/How-to-integrate-Run-ai-with-Apache-Airflow){target=_blank}
 2. [MLflow](https://runai.my.site.com/community/s/article/How-to-integrate-Run-ai-with-MLflow){target=_blank}

docs/admin/workloads/README.md

@@ -8,16 +8,16 @@ date: 2023-Dec-12
 
 ## Introduction
 
-*Policies* allow administrators to impose restrictions and set default values for researcher workloads. Restrictions and default values can be placed on CPUs, GPUs, and other resources or entities. Enabling the *New Policy Manager* provides information about resources that are non-compliant to applied policies. Resources that are non-compliant will appear greyed out. To see how a resource is not compliant, press on the clipboard icon in the upper right hand corner of the resource.
+*Policies* allow administrators to impose restrictions and set default values for researcher workloads. Restrictions and default values can be placed on CPUs, GPUs, and other resources or entities. Enabling the *New Policy Manager* provides information about resources that are non-compliant to applied policies. Resources that are non-compliant will appear greyed out. To see how a resource is not compliant, press on the clipboard icon in the upper right-hand corner of the resource.
 
 !!! Note
-    Policies from Run:ai versions 2.15 or lower will still work after enabling the *New Policy Manager*. However, showing non-compliant policy rules will not be available. For more information about policies for version 2.15 or lower, see [What are Policies](policies.md#what-are-policies).
+    Policies from Run:ai versions 2.17 or lower will still work after enabling the New Policy Manager. For more information about policies for version 2.17 or lower, see [What are Policies](policies.md#what-are-policies).
 
 For example, an administrator can create and apply a policy that will restrict researchers from requesting more than 2 GPUs, or less than 1GB of memory per type of workload.
 
 Another example is an administrator who wants to set different amounts of CPU, GPUs and memory for different kinds of workloads. A training workload can have a default of 1 GB of memory, or an interactive workload can have a default amount of GPUs.
 
-Policies are created for each Run:ai project (Kubernetes namespace). When a policy is created in the `runai` namespace, it will take effect when there is no project-specific policy for the workloads of the same kind.
+Policies are created for each Run:ai project (Kubernetes namespace). When a policy is created in the `runai` namespace, it will take effect when there is no project-specific policy for workloads of the same kind.
 
 In interactive workloads or workspaces, applied policies will only allow researchers access to resources that are permitted in the policy. This can include compute resources as well as node pools and node pool priority.
 
@@ -47,7 +47,7 @@ A policy configured to a specific scope, is applied to all elements in that scop
 
 ### Policy Editor UI
 
-Policies are added to the system using the policy editor and are written in YAML format. YAML™ is a human-friendly, cross language, Unicode based data serialization language designed around the common native data types of dynamic programming languages. It is useful for programming needs ranging from configuration files to internet messaging to object persistence to data auditing and visualization. For more information, see [YAML.org](https://yaml.org/){target=_blank}.
+Policies are added to the system using the policy editor and are written in YAML format. YAML™ is a human-friendly, cross-language, Unicode-based data serialization language designed around the common native data types of dynamic programming languages. It is useful for programming needs ranging from configuration files to internet messaging to object persistence to data auditing and visualization. For more information, see [YAML.org](https://yaml.org/){target=_blank}.
 
 ### Policy API
 
@@ -59,50 +59,47 @@ The following is an example of a workspace policy you can apply in your platform
 
 ```YAML
 defaults:
-    environment:
-      allowPrivilegeEscalation: false
-      createHomeDir: true
-      environmentVariables:
-        - name: MY_ENV
-          value: my_value
-    workspace:
-      allowOverQuota: true
+  createHomeDir: true
+  environmentVariables:
+    instances:
+      - name: MY_ENV
+        value: my_value
+  security:
+    allowPrivilegeEscalation: false
 rules:
-    compute:
-      cpuCoreLimit:
-        min: 0
-        max: 9
-        required: true
-      gpuPortionRequest:
-        min: 0
-        max: 10
+  imagePullPolicy:
+    required: true
+    options:
+      - value: Always
+        displayed: Always
+      - value: Never
+        displayed: Never
+  createHomeDir:
+    canEdit: false
+  security:
+    runAsUid:
+      min: 1
+      max: 32700
+    allowPrivilegeEscalation:
+      canEdit: false
+  compute:
+    cpuCoreLimit:
+      required: true
+      min: 0
+      max: 9
+    gpuPortionRequest:
+      min: 0
+      max: 10
+  storage:
+    nfs:
+      instances:
+        canAdd: false
     s3:
-      url:
-        options:
-          - displayed: "https://www.google.com"
-            value: "https://www.google.com"
-          - displayed: "https://www.yahoo.com"
-            value: "https://www.yahoo.com"
-    environment:
-      imagePullPolicy:
-        options:
-          - displayed: "Always"
-            value: "Always"
-          - displayed: "Never"
-            value: "Never"
-        required: true
-      runAsUid:
-        min: 1
-        max: 32700
-      createHomeDir:
-        canEdit: false
-      allowPrivilegeEscalation:
-        canEdit: false
-    workspace:
-      allowOverQuota:
-        canEdit: false
-    imposedAssets:
-      dataSources:
-        nfs:
-          canAdd: false
+      attributes:
+        url:
+          options:
+            - value: https://www.google.com
+              displayed: https://www.google.com
+            - value: https://www.yahoo.com
+              displayed: https://www.yahoo.com
 ```

docs/admin/workloads/policies/README.md

@@ -22,11 +22,11 @@ date: 2024-June-14
 
 * <!-- RUN-16917/RUN-19363 move to top Expose secrets in workload submission -->Added new *Data sources* of type *Secret* to workload form. *Data sources* of type *Secret* are used to hide 3rd party access credentials when submitting workloads. For more information, see [Submitting Workloads](../admin/workloads/submitting-workloads.md#how-to-submit-a-workload).
 
-* <!-- RUN-16830/RUN-16831 -  Graphs & special metrics for inference -->Added new graphs for *Inference* workloads. The new graphs provide more information for *Inference* workloads to help analyze performance of the workloads. New graphs include Latency, Throughput, and number of replicas. For more information, see [Workloads View](../admin/workloads/README.md#workloads-view) (Requires minimum cluster version v2.18).
+* <!-- RUN-16830/RUN-16831 -  Graphs & special metrics for inference -->Added new graphs for *Inference* workloads. The new graphs provide more information for *Inference* workloads to help analyze performance of the workloads. New graphs include Latency, Throughput, and number of replicas. For more information, see [Workloads View](../admin/workloads/README.md#workloads-view). (Requires minimum cluster version v2.18).
 
 * <!-- TODO add link to doc when ready - get approval for text RUN-16805/RUN-17416 - Provide latency-based metric for autoscaling for requests -->Added latency metric for autoscaling. This feature allows automatic scale-up/down the number of replicas of a Run:ai inference workload based on the threshold set by the ML Engineer. This ensures that response time is kept under the target SLA. (Requires minimum cluster version v2.18).
 
-* <!-- TODO Add to inference doc models explanation after autoscaling.  RUN-16872/RUN-18526 Separating ChatUi from model in favor of coherent autoscaling -->Improved autoscaling for inference models by taking out ChatBot UI from models images. By moving ChatBot UI to predefined *Environments*, autoscaling is more accurate by taking into account all types of requests (API, and ChatBot UI). Adding a ChatBot UI environment preset by Run:ai allows AI practitioners to easily connect them to workloads.
+* <!-- Add to inference doc models explanation after autoscaling.  RUN-16872/RUN-18526 Separating ChatUi from model in favor of coherent autoscaling -->Improved autoscaling for inference models by taking out ChatBot UI from models images. By moving ChatBot UI to predefined *Environments*, autoscaling is more accurate by taking into account all types of requests (API, and ChatBot UI). Adding a ChatBot UI environment preset by Run:ai allows AI practitioners to easily connect them to workloads.
 
 * <!-- RUN-16832/ RUN-16833 - Custom value for auto-scale to zero-->Added more precision to trigger auto-scaling to zero. Now users can configure a precise consecutive idle threshold custom setting to trigger Run:ai inference workloads to scale-to-zero. (Requires minimum cluster version v2.18).
 
@@ -101,7 +101,7 @@ date: 2024-June-14
 
 #### Policy for distributed and inference workloads in the API
 
-Added a new API for creating distributed training workload policies and inference workload policies. These new policies in the API allow to set defaults, enforce rules and impose setup on distributed training and inference workloads. For distributed policies, worker and master may require different rules due to their different specifications. The new capability is currently available via API only. Documentation on submitting policies to follow shortly.
+* Added a new API for creating distributed training workload policies and inference workload policies. These new policies in the API allow to set defaults, enforce rules and impose setup on distributed training and inference workloads. For distributed policies, worker and master may require different rules due to their different specifications. The new capability is currently available via API only. Documentation on submitting policies to follow shortly.
 
 #### Policy for distributed and inference workloads in the API