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Copy file name to clipboardExpand all lines: e2e/inference/README.md
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# Overview
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Intel AI inference end-to-end solution with RHOCP is based on the Intel® Data Center GPU Flex Series provisioning, Intel® OpenVINO™, and [Red Hat OpenShift Data Science](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-data-science) (RHODS) on RHOCP. There are two AI inference modes verified with Intel® Xeon® processors and Intel Data Center GPU Flex Series with RHOCP.
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* Interactive mode – RHODS provides OpenVINO based Jupyter Notebooks for users to interactively debug the inference applications or [optimize the models](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) on RHOCP using data center GPU cards or Intel Xeon processors.
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Intel AI inference end-to-end solution with RHOCP is based on the Intel® Data Center GPU Flex Series provisioning, Intel® OpenVINO™, and [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) (RHOAI) on RHOCP. There are two AI inference modes verified with Intel® Xeon® processors and Intel Data Center GPU Flex Series with RHOCP.
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* Interactive mode – RHOAI provides OpenVINO based Jupyter Notebooks for users to interactively debug the inference applications or [optimize the models](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) on RHOCP using data center GPU cards or Intel Xeon processors.
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* Deployment mode – [OpenVINO Model Sever](https://github.com/openvinotoolkit/model_server) (OVMS) can be used to deploy the inference workloads in data center and edge computing environments on RHOCP.
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**NOTE**: To ensure the AI inference workloads work properly, please follow the workaround section in the [known SeLinux regression issue](https://github.com/intel/intel-technology-enabling-for-openshift/issues/107).
* Provisioning Intel Data Center GPU Flex Series. Follow steps [here](/README.md#provisioning-intel-hardware-features-on-rhocp)
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* Setup out of tree drivers for Intel GPU provisioning. Follow the steps [here](/kmmo/README.md)
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* Setup Intel device plugins operator and create Intel GPU device plugin. Follow the steps [here](/device_plugins/README.md)
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## Install RHODS
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The Red Hat certified RHODS operator is published at [Red Hat Ecosystem Catalog](https://catalog.redhat.com/software/container-stacks/detail/63b85b573112fe5a95ee9a3a). You can use the command line interface (CLI) or web console to install it.
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## Install RHOAI
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The Red Hat certified RHOAI operator is published at [Red Hat Ecosystem Catalog](https://catalog.redhat.com/software/container-stacks/detail/63b85b573112fe5a95ee9a3a). You can use the command line interface (CLI) or web console to install it.
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### Install using CLI (To be added)
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### Install using web console
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1. On the RHOCP web console, click Operators → OperatorHub.
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2. Search RedHat OpenShift Data Science Operator and click Install. The operator is installed in the namespace redhat-ods-operator.
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2. Search RedHat OpenShift AI Operator and click Install. The operator is installed in the namespace redhat-ods-operator.
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### Verification
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1. Navigate to Operators → Installed Operators page.
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2. Ensure that in the redhat-ods-operator namespace, RedHat OpenShift Data Science Status is InstallSucceeded
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3. Click on ```Search -> Routes -> rhods-dashboard``` from the web console and access the RHODS UI link.
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Note: When installing the operator, the default ```kfdef``` Custom Resource (CR) is created. This CR enables the RHODS dashboard for users to browse and launch Jupyter Notebooks projects on an RHOCP cluster. Please refer to this [link](https://github.com/red-hat-data-services/odh-deployer) for more details about kfdef.
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2. Ensure that in the redhat-ods-operator namespace, RedHat OpenShift AI status is InstallSucceeded
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3. Click on ```Search -> Routes -> rhods-dashboard``` from the web console and access the RHOAI UI link.
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Note: When installing the operator, the default ```kfdef``` Custom Resource (CR) is created. This CR enables the dashboard for users to browse and launch Jupyter Notebooks projects on an RHOCP cluster. Please refer to this [link](https://github.com/red-hat-data-services/odh-deployer) for more details about kfdef.
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## Install OpenVINO operator
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The OpenVINO operator is published at [Red Hat Ecosystem Catalog](https://catalog.redhat.com/software/container-stacks/detail/60649a56209af65d24b7ca9e). You can use the CLI or web console to install it.
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### Install using CLI (To be added)
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### Install using web console
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Follow this [link](https://github.com/openvinotoolkit/operator/blob/v1.1.0/docs/operator_installation.md) to install the operator via the web console.
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## Work with interactive mode
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To enable the interactive mode, the OpenVINO notebook CR needs to be created and integrated with RHODS.
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1. Click on the ```create Notebook``` option in this [link](https://github.com/red-hat-data-services/odh-deployer)from the web console and follow these [steps](https://github.com/openvinotoolkit/operator/blob/main/docs/notebook_in_rhods.md) to create the notebook CR.
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2. Enable Intel Data Center GPU on RHODS Dashboard- **Technical Preview feature**
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To enable the interactive mode, the OpenVINO notebook CR needs to be created and integrated with RHOAI.
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1. Click on the ```create Notebook``` option from the web console and follow these [steps](https://github.com/openvinotoolkit/operator/blob/main/docs/notebook_in_rhods.md) to create the notebook CR.
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2. Enable Intel Data Center GPU on RHOAI Dashboard- **Technical Preview feature**
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Create AcceleratoProfile in the ```redhat-ods-applications``` namespace
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4. Navigate to ```Search -> Networking -> Routes``` from the web console and access ```rhods-dashboard``` route in the ```redhat-ods-applications``` namespace, as in the image below. Click on the location link to launch RHODS dashboard.
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4. Navigate to ```Search -> Networking -> Routes``` from the web console and access ```rhods-dashboard``` route in the ```redhat-ods-applications``` namespace, as in the image below. Click on the location link to launch RHOAI dashboard.
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