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* link:{odhdocshome}/working-in-your-data-science-ide/#collaborating-on-jupyter-notebooks-by-using-git_{context}[Collaborating on Jupyter notebooks by using Git]
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* link:{odhdocshome}/working-in-your-data-science-ide/#collaborating-on-jupyter-notebooks-by-using-git_ide[Collaborating on Jupyter notebooks by using Git]
* link:https://github.com/stratus-ss/openshift-ai/blob/main/docs/rendered/OpenShift_AI_CLI.md#nvidia---configuring-time-slicing[OpenShift AI CLI - Configuring Time Slicing]
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//* link:https://github.com/stratus-ss/openshift-ai/blob/main/docs/rendered/OpenShift_AI_CLI.md#nvidia---configuring-time-slicing[OpenShift AI CLI - Configuring Time Slicing]
After you have installed {productname-short} and added users, you can access the URL for your {productname-short} console and share the URL with the users to let them log in and work on their models.
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ifndef::upstream[]
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.Prerequisites
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* You have installed {productname-short} on your {openshift-platform} cluster.
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* You have added at least one user to the user group for {productname-short}.
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.Procedure
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. Log in to {openshift-platform} web console.
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. Click the application launcher (image:images/osd-app-launcher.png[The application launcher]).
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. Right-click *{productname-long}* and copy the URL for your {productname-short} instance.
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. Provide this instance URL to your data scientists to let them log in to {productname-short}.
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.Verification
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* Confirm that you and your users can log in to {productname-short} by using the instance URL.
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*Note:* In the {productname-long} dashboard, users can view the list of the installed {productname-short} components, their corresponding source (upstream) components, and the versions of the installed components, as described in link:{rhoaidocshome}{default-format-url}/getting_started_with_{url-productname-long}/logging-in_get-started#viewing-installed-components_get-started[Viewing installed components].
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[role="_additional-resources"]
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.Additional resources
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* link:{rhoaidocshome}{default-format-url}/getting_started_with_{url-productname-long}/logging-in_get-started[Logging in to {productname-short}]
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* link:{rhoaidocshome}{default-format-url}/managing_openshift_ai/managing-users-and-groups#adding-users-to-user-groups_managing-rhoai[Adding users to {productname-short} user groups]
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endif::[]
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ifdef::upstream[]
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.Prerequisites
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* You have installed the {productname-short} Operator.
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.Procedure
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. Log in to {openshift-platform} web console.
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. Click the application launcher (image:images/osd-app-launcher.png[The application launcher]).
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. Right-click *{productname-long}* and copy the URL for your {productname-short} instance.
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. Give this URL to your users to let them log in to {productname-short} dashboard.
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.Verification
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* Confirm that you and your users can log in to the {productname-short} dashboard by using the URL.
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*Note:* In the {productname-long} dashboard, users can view the list of the installed {productname-short} components, their corresponding source (upstream) components, and the versions of the installed components, as described in link:{odhdocshome}/getting-started-with-open-data-hub/#viewing-installed-components_get-started[Viewing installed {productname-short} components].
Copy file name to clipboardExpand all lines: modules/adding-a-tested-and-verified-runtime-for-the-multi-model-serving-platform.adoc
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[role='_additional-resources']
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.Additional resources
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ifndef::upstream[]
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* To learn how to configure a model server that uses a model-serving runtime that you have added, see link:{rhoaidocshome}{default-format-url}/deploying_models/deploying-models_rhoai-user#adding-a-model-server-for-the-multi-model-serving-platform_rhoai-user[Adding a model server to your data science project].
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* To learn how to configure a model server that uses a model-serving runtime that you have added, see link:{rhoaidocshome}{default-format-url}/deploying_models/deploying_models_on_the_multi_model_serving_platform#adding-a-model-server-for-the-multi-model-serving-platform_rhoai-user[Adding a model server to your data science project].
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endif::[]
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ifdef::upstream[]
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* To learn how to configure a model server that uses a model-serving runtime that you have added, see link:{odhdocshome}/deploying-models/#adding-a-model-server-for-the-multi-model-serving-platform_odh-user[Adding a model server to your data science project].
Copy file name to clipboardExpand all lines: modules/adding-an-odh-instance.adoc
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.Procedure
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. In the OpenShift web console, select *Operators* -> *Installed Operators*.
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. On the *Installed Operators* page, click the *Project* list and select the `odh` project. The page filters to only display installed operators in the `odh` project.
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. On the *Installed Operators* page, click the *Project* list and select the `pass:attributes[{dbd-config-default-namespace}]` project.
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. Find and click the *Open Data Hub Operator* to display the details for the currently installed version.
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. On the *KfDef* tile, click *Create instance*. A `KfDef` object is a specification designed to control provisioning and management of a Kubeflow deployment. A default `KfDef` object is created when you install Open Data Hub Operator. This default configuration deploys the required Open Data Hub core components. For more information, see link:https://opendatahub.io/docs/tiered-components[Tiered Components].
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. On the *Create KfDef* page, leave *opendatahub* as the name. Click *Create* to create an Open Data Hub kfdef object named *opendatahub* and begin the deployment of the components.
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.Verification
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. Select *Operators* -> *Installed Operators*.
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. On the *Installed Operators* page, click the *Project* list and select the `odh` project.
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. On the *Installed Operators* page, click the *Project* list and select the `pass:attributes[{dbd-config-default-namespace}]` project.
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. Find and click *Open Data Hub Operator*.
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. Click the *Kf Def* tab and confirm that *opendatahub* is displayed.
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. Select *Home* -> *Projects*.
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. On the *Projects* page, find and select the *odh* project.
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. On the *Projects* page, find and select the `pass:attributes[{dbd-config-default-namespace}]` project.
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. On the *Project details* page, click the *Workloads* tab and confirm that the Open Data Hub core components are running. For a description of the components, see link:https://opendatahub.io/docs/tiered-components[Tiered Components].
Copy file name to clipboardExpand all lines: modules/api-workbench-creating.adoc
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endif::[]
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* You have created a data science project. In the example in this procedure, the project is named `my-data-science-project`.
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//will probably need to fix these links
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ifdef::upstream[]
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* You know the URL for the workbench image that you want to use in the workbench. The example in this procedure uses the custom image that you created in link:{odhdocshome}/api-workbench/#api-custom-image-creating_api-workbench[Creating a custom image by using the `ImageStream` CRD].
Copy file name to clipboardExpand all lines: modules/configuring-monitoring-for-the-multi-model-serving-platform.adoc
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** link:https://docs.redhat.com/en/documentation/openshift_dedicated/{osd-latest-version}/html/cli_tools/openshift-cli-oc#installing-openshift-cli[Installing the OpenShift CLI^] for OpenShift Dedicated
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** link:https://docs.redhat.com/en/documentation/red_hat_openshift_service_on_aws_classic_architecture/{rosa-classic-latest-version}/html/cli_tools/openshift-cli-oc#installing-openshift-cli[Installing the OpenShift CLI^] for {rosa-classic-productname}
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endif::[]
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* You are familiar with link:https://docs.redhat.com/en/documentation/openshift_container_platform/{ocp-latest-version}/html/monitoring/index#preparing-to-configure-the-monitoring-stack-uwm[creating a config map] for monitoring a user-defined workflow. You will perform similar steps in this procedure.
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* You are familiar with link:https://docs.redhat.com/en/documentation/openshift_container_platform/{ocp-latest-version}/html/monitoring/index#enabling-monitoring-for-user-defined-projects-uwm_preparing-to-configure-the-monitoring-stack-uwm[enabling monitoring] for user-defined projects in OpenShift. You will perform similar steps in this procedure.
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* You are familiar with link:https://docs.redhat.com/en/documentation/openshift_container_platform/{ocp-latest-version}/html/monitoring/configuring-core-platform-monitoring#preparing-to-configure-the-monitoring-stack[creating a config map] for monitoring a user-defined workflow. You will perform similar steps in this procedure.
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* You are familiar with link:https://docs.redhat.com/en/documentation/openshift_container_platform/{ocp-latest-version}/html/monitoring/configuring-user-workload-monitoring#enabling-monitoring-for-user-defined-projects-uwm_preparing-to-configure-the-monitoring-stack-uwm[enabling monitoring] for user-defined projects in OpenShift. You will perform similar steps in this procedure.
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* You have link:https://docs.redhat.com/en/documentation/openshift_container_platform/{ocp-latest-version}/html/monitoring/configuring-user-workload-monitoring#granting-users-permission-to-monitor-user-defined-projects_preparing-to-configure-the-monitoring-stack-uwm[assigned] the `monitoring-rules-view` role to users that will monitor metrics.
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