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Co-authored-by: Rafael Vasquez <[email protected]> Signed-off-by: Joe Runde <[email protected]>
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docs/deploying/rhoai.md

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# Using Red Hat OpenShift AI
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[Red Hat OpenShift AI](https://www.redhat.com/en/products/ai/openshift-ai) is a cloud-native AI platform that bundles together many popular model management projects, including [kserve](https://kserve.github.io/website/latest/).
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[Red Hat OpenShift AI](https://www.redhat.com/en/products/ai/openshift-ai) is a cloud-native AI platform that bundles together many popular model management projects, including [KServe](https://kserve.github.io/website/latest/).
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This example shows how to use kserve with RHOAI to deploy a model on OpenShift, using a modelcar image to load the model without requiring any connection to huggingface hub.
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This example shows how to use KServe with RHOAI to deploy a model on OpenShift, using a modelcar image to load the model without requiring any connection to Huggingface Hub.
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## Deploying with kserve
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## Deploying with KServe
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!!! note
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**Prerequisite**: Ensure that you have a running Kubernetes cluster with RHOAI installed, image pull credentials for `registry.redhat.io/rhelai1`, and Spyre accelerators.
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**Prerequisites**:
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- A running Kubernetes cluster with RHOAI installed
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- Image pull credentials for `registry.redhat.io/rhelai1`
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- Spyre accelerators available in the cluster
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<!-- TODO: Link to public docs for cluster setup -->
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1. Create a ServingRuntime to be used for your models
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1. Create a ServingRuntime to serve your models.
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```yaml
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apiVersion: serving.kserve.io/v1alpha1
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protocol: TCP
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```
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2. Create an InferenceService for each model you want to deploy. This example demonstrates how to deploy `ibm-granite/granite-3.1-8b-instruct`.
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2. Create an InferenceService for each model you want to deploy. This example demonstrates how to deploy the [Granite](https://www.ibm.com/granite) model `ibm-granite/granite-3.1-8b-instruct`.
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```yaml
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apiVersion: serving.kserve.io/v1beta1

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