ENG-12182: Moves the dw installation info to the installation guide

assemblies/configuring-distributed-workloads.adoc

@@ -7,13 +7,12 @@ ifdef::context[:parent-context: {context}]
 
 [role='_abstract']
 ifdef::self-managed,cloud-service[]
-To configure the distributed workloads feature for your data scientists to use in {productname-short}, you must enable several components in the {productname-long} {install-package}, create the required Kueue resources, and optionally configure the CodeFlare Operator.
+To configure the distributed workloads feature for your data scientists to use in {productname-short}, you must create the required Kueue resources, and optionally configure the CodeFlare Operator.
 endif::[]
 ifdef::upstream[]
-To configure the distributed workloads feature for your data scientists to use in {productname-short}, you must enable several components in the {productname-long} Operator, create the required Kueue resources, and optionally configure the CodeFlare Operator.
+To configure the distributed workloads feature for your data scientists to use in {productname-short}, you must create the required Kueue resources, and optionally configure the CodeFlare Operator.
 endif::[]
 
-include::modules/configuring-the-distributed-workloads-components.adoc[leveloffset=+1]
 include::modules/configuring-quota-management-for-distributed-workloads.adoc[leveloffset=+1]
 include::modules/configuring-the-codeflare-operator.adoc[leveloffset=+1]
 

assemblies/installing-odh-v2.adoc

@@ -38,6 +38,8 @@ include::modules/installing-the-odh-operator-v2.adoc[leveloffset=+1]
 
 include::modules/installing-odh-components.adoc[leveloffset=+1]
 
+include::modules/installing-the-distributed-workloads-components.adoc[leveloffset=+1]
+
 include::modules/accessing-the-odh-dashboard.adoc[leveloffset=+1]
 
 ifdef::parent-context[:context: {parent-context}]

modules/configuring-quota-management-for-distributed-workloads.adoc

@@ -21,13 +21,28 @@ ifdef::cloud-service[]
 * You have downloaded and installed the OpenShift command-line interface (CLI). See link:https://docs.openshift.com/dedicated/cli_reference/openshift_cli/getting-started-cli.html#installing-openshift-cli[Installing the OpenShift CLI] (Red Hat OpenShift Dedicated) or link:https://docs.openshift.com/rosa/cli_reference/openshift_cli/getting-started-cli.html#installing-openshift-cli[Installing the OpenShift CLI] (Red Hat OpenShift Service on AWS).
 endif::[]
 
-ifndef::upstream[]
-* You have enabled the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads#configuring-the-distributed-workloads-components_distributed-workloads[Configuring the distributed workloads components].
-endif::[]
+
 ifdef::upstream[]
-* You have enabled the required distributed workloads components as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-the-distributed-workloads-components_distributed-workloads[Configuring the distributed workloads components].
+* You have installed the required distributed workloads components as described in link:{odhdocshome}/installing-open-data-hub/#installing-distributed-workload-components_installv2[Installing the distributed workloads components].
+endif::[]
+
+ifdef::self-managed[]
+
+ifndef::disconnected[]
+* You have installed the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}/installing-and-deploying-openshift-ai_install#installing-distributed-workload-components_component-install[Installing the distributed workloads components].
 endif::[]
 
+ifdef::disconnected[]
+* You have installed the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}_in_a_disconnected_environment/installing-and-deploying-openshift-ai_install#installing-distributed-workload-components_component-install[Installing the distributed workloads components].
+endif::[]
+
+endif::[]
+
+ifdef::cloud-service[]
+* You have installed the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}/installing-and-deploying-openshift-ai_install#installing-distributed-workload-components_component-install[Installing the distributed workloads components].
+endif::[]
+
+
 ifndef::upstream[]
 * You have created a data science project that contains a workbench, and the workbench is running a default notebook image that contains the CodeFlare SDK, for example, the *Standard Data Science* notebook. For information about how to create a project, see link:{rhoaidocshome}{default-format-url}/working_on_data_science_projects/using-data-science-projects_projects#creating-a-data-science-project_projects[Creating a data science project]. 
 endif::[]

modules/configuring-the-codeflare-operator.adoc

@@ -14,13 +14,28 @@ ifdef::cloud-service[]
 * You have logged in to OpenShift with the `cluster-admin` role.
 endif::[]
 
-ifndef::upstream[]
-* You have enabled the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads#configuring-the-distributed-workloads-components_distributed-workloads[Configuring the distributed workloads components].
-endif::[]
+
 ifdef::upstream[]
-* You have enabled the required distributed workloads components as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-the-distributed-workloads-components_distributed-workloads[Configuring the distributed workloads components].
+* You have installed the required distributed workloads components as described in link:{odhdocshome}/installing-open-data-hub/#installing-distributed-workload-components_installv2[Installing the distributed workloads components].
+endif::[]
+
+ifdef::self-managed[]
+
+ifndef::disconnected[]
+* You have installed the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}/installing-and-deploying-openshift-ai_install#installing-distributed-workload-components_component-install[Installing the distributed workloads components].
+endif::[]
+
+ifdef::disconnected[]
+* You have installed the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}_in_a_disconnected_environment/installing-and-deploying-openshift-ai_install#installing-distributed-workload-components_component-install[Installing the distributed workloads components].
 endif::[]
 
+endif::[]
+
+ifdef::cloud-service[]
+* You have installed the required distributed workloads components as described in link:{rhoaidocshome}{default-format-url}/installing_and_uninstalling_{url-productname-short}/installing-and-deploying-openshift-ai_install#installing-distributed-workload-components_component-install[Installing the distributed workloads components].
+endif::[]
+
+
 
 .Procedure
 ifdef::upstream,self-managed[]

modules/configuring-the-training-job.adoc

@@ -17,10 +17,10 @@ ifdef::cloud-service[]
 endif::[]
 
 ifndef::upstream[]
-* You have access to a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You have access to a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/managing_openshift_ai/managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 ifdef::upstream[]
-* You have access to a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You have access to a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/managing-openshift-ai/#managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 
 ifndef::upstream[]

modules/downloading-the-demo-notebooks-from-the-codeflare-sdk.adoc

@@ -10,10 +10,10 @@ If you do not want to run distributed workloads from notebooks, you can skip thi
 
 .Prerequisites
 ifndef::upstream[]
-* You can access a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/managing_openshift_ai/managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 ifdef::upstream[]
-* You can access a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/managing-openshift-ai/#managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 
 ifndef::upstream[]

modules/configuring-the-distributed-workloads-components.adoc → modules/installing-the-distributed-workloads-components.adoc

@@ -1,20 +1,19 @@
 :_module-type: PROCEDURE
 
-[id="configuring-the-distributed-workloads-components_{context}"]
-= Configuring the distributed workloads components
+[id="installing-the-distributed-workloads-components_{context}"]
+= Installing the distributed workloads components
 
 [role='_abstract']
-To configure the distributed workloads feature for your data scientists to use in {productname-short}, you must enable several components.
+To use the distributed workloads feature in {productname-short}, you must install several components.
 
 .Prerequisites
 ifdef::upstream,self-managed[]
-* You have logged in to {openshift-platform} with the `cluster-admin` role.
+* You have logged in to {openshift-platform} with the `cluster-admin` role and you can access the data science cluster.
 endif::[]
 ifdef::cloud-service[]
-* You have logged in to OpenShift with the `cluster-admin` role.
+* You have logged in to OpenShift with the `cluster-admin` role and you can access the data science cluster.
 endif::[]
 
-* You have access to the data science cluster.
 * You have installed {productname-long}.
 
 ifdef::cloud-service[]
@@ -27,27 +26,6 @@ ifdef::upstream[]
 * You have sufficient resources. In addition to the minimum {productname-short} resources described in link:{odhdocshome}/installing-open-data-hub/#installing-the-odh-operator-v2_installv2[Installing the {productname-short} Operator version 2], you need 1.6 vCPU and 2 GiB memory to deploy the distributed workloads infrastructure.
 endif::[]
 
-* You have access to a Ray cluster image. For information about how to create a Ray cluster, see the link:https://docs.ray.io/en/latest/cluster/getting-started.html[Ray Clusters documentation].
-+
-ifdef::self-managed[]
-[NOTE]
-====
-Mutual Transport Layer Security (mTLS) is enabled by default in the CodeFlare component in {productname-short}.
-{productname-short} {vernum} does not support the `submissionMode=K8sJobMode` setting in the Ray job specification, so the KubeRay Operator cannot create a submitter Kubernetes Job to submit the Ray job.
-Instead, users must configure the Ray job specification to set `submissionMode=HTTPMode` only, so that the KubeRay Operator sends a request to the RayCluster to create a Ray job.
-====
-endif::[]
-ifdef::cloud-service[]
-[NOTE]
-====
-Mutual Transport Layer Security (mTLS) is enabled by default in the CodeFlare component in {productname-short}.
-{productname-short}, does not support the `submissionMode=K8sJobMode` setting in the Ray job specification, so the KubeRay Operator cannot create a submitter Kubernetes Job to submit the Ray job.
-Instead, users must configure the Ray job specification to set `submissionMode=HTTPMode` only, so that the KubeRay Operator sends a request to the RayCluster to create a Ray job.
-====
-endif::[]
-* You have access to the data sets and models that the distributed workload uses.
-* You have access to the Python dependencies for the distributed workload.
-
 ifndef::upstream[]
 * You have removed any previously installed instances of the CodeFlare Operator, as described in the Knowledgebase solution link:https://access.redhat.com/solutions/7043796[How to migrate from a separately installed CodeFlare Operator in your data science cluster].
 endif::[]
@@ -159,10 +137,14 @@ endif::[]
 . Click the default instance name (for example, *default-dsc*) to open the instance details page.
 . Click the *YAML* tab to show the instance specifications.
 . Enable the required distributed workloads components.
-In the `spec:components` section, set the `managementState` field correctly for the required components.
-The `trainingoperator` component is required only if you want to use the Kubeflow Training Operator to tune models.
-The list of required components depends on whether the distributed workload is run from a pipeline or notebook or both, as shown in the following table.
+In the `spec:components` section, set the `managementState` field correctly for the required components:
++
+* If you want to use the CodeFlare framework to tune models, enable the `codeflare`, `kueue`, and `ray` components.
+* if you want to use the Kubeflow Training Operator to tune models, enable the `kueue` and `trainingoperator` components.
+* The list of required components depends on whether the distributed workload is run from a pipeline or notebook or both, as shown in the following table.
+
 +
+
 .Components required for distributed workloads
 [cols="38,18,18,26"]
 |===
@@ -230,3 +212,12 @@ In each case, check the status as follows:
 +
 When the status of the *codeflare-operator-manager-_<pod-id>_*, *kuberay-operator-_<pod-id>_*, and *kueue-controller-manager-_<pod-id>_* pods is *Running*, the pods are ready to use.
 .. To see more information about each pod, click the pod name to open the pod details page, and then click the *Logs* tab.
+
+.Next Step
+ifdef::upstream[]
+Configure the distributed workloads feature as described in link:{odhdocshome}/managing-openshift-ai/#managing_distributed_workloads[Managing distributed workloads].
+endif::[]
+ifndef::upstream[]
+Configure the distributed workloads feature as described in link:{rhoaidocshome}{default-format-url}/managing_openshift_ai/managing_distributed_workloads[Managing distributed workloads].
+endif::[]
+

modules/monitoring-the-training-job.adoc

@@ -11,10 +11,10 @@ The example training job in this section is based on the IBM and Hugging Face tu
 .Prerequisites
 
 ifndef::upstream[]
-* You have access to a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You have access to a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/managing_openshift_ai/managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 ifdef::upstream[]
-* You have access to a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You have access to a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/managing-openshift-ai/#managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 
 ifndef::upstream[]

modules/running-distributed-data-science-workloads-disconnected-env.adoc

@@ -29,7 +29,7 @@ endif::[]
 
 
 .Procedure
-. Configure the disconnected data science cluster to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+. Configure the disconnected data science cluster to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/managing_openshift_ai/managing_distributed_workloads[Managing distributed workloads].
 . In the `ClusterConfiguration` section of the notebook or pipeline, ensure that the `image` value specifies a Ray cluster image that you can access from the disconnected environment:
 * Notebooks use the Ray cluster image to create a Ray cluster when running the notebook.
 * Pipelines use the Ray cluster image to create a Ray cluster during the pipeline run.

modules/running-distributed-data-science-workloads-from-ds-pipelines.adoc

@@ -8,45 +8,12 @@ To run a distributed workload from a pipeline, you must first update the pipelin
 
 .Prerequisites
 ifndef::upstream[]
-* You can access a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{rhoaidocshome}{default-format-url}/managing_openshift_ai/managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 ifdef::upstream[]
-* You can access a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-distributed-workloads_distributed-workloads[Configuring distributed workloads].
+* You can access a data science cluster that is configured to run distributed workloads as described in link:{odhdocshome}/managing-openshift-ai/#managing_distributed_workloads[Managing distributed workloads].
 endif::[]
 
-ifndef::upstream[]
-* Your cluster administrator has created the required Kueue resources as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads#configuring-quota-management-for-distributed-workloads_distributed-workloads[Configuring quota management for distributed workloads].
-endif::[]
-ifdef::upstream[]
-* Your cluster administrator has created the required Kueue resources as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-quota-management-for-distributed-workloads_distributed-workloads[Configuring quota management for distributed workloads].
-endif::[]
-
-ifndef::upstream[]
-* Optional: Your cluster administrator has defined a _default_ local queue for the Ray cluster by creating a `LocalQueue` resource and adding the following annotation to the configuration details for that `LocalQueue` resource, as described in link:{rhoaidocshome}{default-format-url}/working_with_distributed_workloads/configuring-distributed-workloads_distributed-workloads#configuring-quota-management-for-distributed-workloads_distributed-workloads[Configuring quota management for distributed workloads]:
-+
-[source,bash]
-----
-"kueue.x-k8s.io/default-queue": "true"
-----
-+
-[NOTE]
-====
-If your cluster administrator does not define a default local queue, you must specify a local queue in each pipeline.
-====
-endif::[]
-ifdef::upstream[]
-* Optional: Your cluster administrator has defined a _default_ local queue for the Ray cluster by creating a `LocalQueue` resource and adding the following annotation to the configuration details for that `LocalQueue` resource, as described in link:{odhdocshome}/working-with-distributed-workloads/#configuring-quota-management-for-distributed-workloads_distributed-workloads[Configuring quota management for distributed workloads]:
-+
-[source,bash]
-----
-"kueue.x-k8s.io/default-queue": "true"
-----
-+
-[NOTE]
-====
-If your cluster administrator does not define a default local queue, you must specify a local queue in each pipeline.
-====
-endif::[]
 
 * You can access the following software from your data science cluster:
 ** A Ray cluster image that is compatible with your hardware architecture