quickstart README updates

quickstart/README.md

@@ -4,11 +4,25 @@ Getting Started with llm-d on Kubernetes.
 
 ## Overview
 
-This guide will walk you through the steps to install and deploy llm-d on a Kubernetes cluster. llm-d consists of the following components:
+This guide will walk you through the steps to install and deploy llm-d on a Kubernetes cluster.
 
-- Gateway API Inference Extension (GIE) - This extension upgrades an ext-proc-capable proxy or gateway - such as Envoy Gateway, kGateway, or the GKE Gateway - to become an inference gateway - supporting inference platform teams self-hosting large language models on Kubernetes. This integration makes it easy to expose and control access to your local OpenAI-compatible chat completion endpoints to other workloads on or off cluster, or to integrate your self-hosted models alongside model-as-a-service providers in a higher level AI Gateway like LiteLLM, Solo AI Gateway, or Apigee.
+**What is llm-d?**
+
+llm-d is an open source project providing distributed inferencing for GenAI runtimes on any Kubernetes cluster. Its highly performant, scalable architecture helps reduce costs through a spectrum of hardware efficiency improvements. The project prioritizes ease of deployment+use as well as SRE needs + day 2 operations associated with running large GPU clusters.
+
+It includes:
+
+- Prefill/decode disaggregation
+- KV Cache distribution, offloading and storage hierarchy
+- AI-aware router with plug points for customizable scorers
+- Operational telemetry for production, prometheus/grafana
+- Kubernetes-based, works on OCP, minikube, and other k8s distributions
+- NIXL inference transfer library
+
+**llm-d consists of the following components:**
 
-   The inference gateway:
+- Gateway API Inference Extension (GIE) - This extension upgrades an ext-proc-capable proxy or gateway - such as Envoy Gateway, kGateway, or the GKE Gateway - to become an inference gateway - supporting inference platform teams self-hosting large language models on Kubernetes. This integration makes it easy to expose and control access to your local OpenAI-compatible chat completion endpoints to other workloads on or off cluster, or to integrate your self-hosted models alongside model-as-a-service providers in a higher level AI Gateway like LiteLLM, Solo AI Gateway, or Apigee.
+  The inference gateway:
   - Improves the tail latency and throughput of LLM completion requests against Kubernetes-hosted model servers using an extendable request scheduling algorithm that is kv-cache and request cost aware, avoiding evictions or queueing as load increases
   - Provides Kubernetes-native declarative APIs to route client model names to use-case specific LoRA adapters and control incremental rollout of new adapter versions, A/B traffic splitting, and safe blue-green base model and model server upgrades
   - Adds end to end observability around service objective attainment
@@ -27,6 +41,7 @@ This guide will walk you through the steps to install and deploy llm-d on a Kube
   - Supports independent scaling of prefill and decode instances
   - Supports independent node affinities for prefill and decode instances
   - Supports model loading from OCI images, HuggingFace public and private registries, and PVCs
+
 - Metrics Service (Prometheus)
 
 ### Architecture
@@ -35,6 +50,17 @@ This guide will walk you through the steps to install and deploy llm-d on a Kube
 
 ## Hardware Profiles
 
+Tested on:
+
+- Minikube on AWS
+  - single g6e.12xlarge
+- Red Hat OpenShift on AWS
+  - 6 x m5.4xlarge
+  - 2 x g6e.2xlarge
+  - OpenShift 4.17.21
+  - NVIDIA GPU Operator 24.9.2
+  - OpenShift Data Foundation 4.17.6
+
 ## Client Configuration
 
 ### Required tools
@@ -48,9 +74,10 @@ Following prerequisite are required for the installer to work.
 - [Kustomize – official install docs](https://kubectl.docs.kubernetes.io/installation/kustomize/)
 - [kubectl – install & setup](https://kubernetes.io/docs/tasks/tools/install-kubectl/)
 
-You can use the installer script that installs all the required dependencies.
+You can use the installer script that installs all the required dependencies.  Currently only Linux is supported.
 
 ```bash
+# Currently Linux only
 ./install-deps.sh
 ```
 
@@ -59,7 +86,9 @@ You can use the installer script that installs all the required dependencies.
 - [llm-d-deployer GitHub repo – clone here](https://github.com/neuralmagic/llm-d-deployer.git)
 - [Quay.io Registry – sign-up & credentials](https://quay.io/)
 - [Red Hat Registry – terms & access](https://access.redhat.com/registry/)
-- [HuggingFace HF_TOKEN](https://huggingface.co/docs/hub/en/security-tokens)
+- [HuggingFace HF_TOKEN](https://huggingface.co/docs/hub/en/security-tokens) with download access for the model you want to use.  By default the sample application will use [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
+  > ⚠️ You may need to visit Hugging Face [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) and
+  > accept the usage terms to pull this with your HF token if you have not already done so.
 
 Registry Authentication: The installer looks for an auth file in:
 
@@ -85,9 +114,6 @@ podman login quay.io --authfile ~/.config/containers/auth.json
 podman login registry.redhat.io --authfile ~/.config/containers/auth.json
 ```
 
-> ⚠️ You may need to visit Hugging Face [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) and
-> accept the usage terms to pull this with your HF token if you have not already done so.
-
 ### Target Platforms
 
 #### Kubernetes
@@ -105,20 +131,31 @@ podman run --rm --security-opt=label=disable --device=nvidia.com/gpu=all ubuntu
 sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
 ```
 
-```bash
-./llmd-installer.sh
-```
-
 #### OpenShift
 
 - OpenShift - This quickstart was tested on OpenShift 4.18. Older versions may work but have not been tested.
-- NVIDIA GPU Operator and NFD Operator - The installation instructions can be found [here](https://docs.nvidia.com/datacenter/cloud-native/openshift/latest/steps-overview.html)
+- NVIDIA GPU Operator and NFD Operator - The installation instructions can be found [here](https://docs.nvidia.com/datacenter/cloud-native/openshift/latest/steps-overview.html).
+- OpenShift Data Foundation - The installation instructions can be found [here](https://docs.redhat.com/en/documentation/red_hat_openshift_data_foundation/4.17/html/deploying_and_managing_openshift_data_foundation_using_red_hat_openstack_platform/deploying_openshift_data_foundation_on_red_hat_openstack_platform_in_internal_mode).  OF is not required, but a ReadWriteMany storage class is required.
+- NO Service Mesh or Istio installation as it will conflict with the gateway
 
 ## llm-d Installation
 
-The llm-d-deployer contains all the helm charts necessary to deploy llm-d. To facilitate the installation of the helm charts, the `llmd-installer.sh` script is provided. This script will populate the necessary manifests in the `manifests` directory. After this, it will apply all the manifests in order to bring up the cluster.
+The llm-d-deployer contains all the helm charts necessary to deploy llm-d. To facilitate the installation of the helm charts, the `llmd-installer.sh` script is provided. This script will populate the necessary manifests in the `manifests` directory.
+After this, it will apply all the manifests in order to bring up the cluster.
+
+The llmd-installer.sh script aims to simplify the installation of llm-d using the llm-d-deployer as it's main function.  It scripts as many of the steps as possible to make the installation process more streamlined.  This includes:
 
-Before proceeding with the installation, ensure you have installed the required dependencies
+- Installing the GAIE infrastructure
+- Creating the namespace with any special configurations
+- Creating the pull secret to download the images
+- Creating storage and downloading the model
+- Creating the model service CRDs
+- Applying the helm charts
+- Deploying the sample app (model service)
+
+It also supports uninstalling the llm-d infrastructure and the sample app.
+
+Before proceeding with the installation, ensure you have completed the prerequisites and are able to issue kubectl commands to your cluster by configuring your `~/.kube/config` file or by using the `oc login` command.
 
 ### Usage
 
@@ -145,7 +182,7 @@ The installer needs to be run from the `llm-d-deployer/quickstart` directory.
 
 ### Install llm-d on an Existing Kubernetes Cluster
 
-The storage class used is `efs-sc`. Modify [model-storage-rwx-pvc.yaml](../helpers/k8s/model-storage-rwx-pvc.yaml)
+The storage class used for AWS ec2 is `efs-sc`. Modify [model-storage-rwx-pvc.yaml](../helpers/k8s/model-storage-rwx-pvc.yaml)
 for a different type.
 
 ```bash
@@ -155,48 +192,58 @@ export HF_TOKEN="your-token"
 
 ### Install on OpenShift with OF installed
 
+Before running the installer, ensure you have logged into the cluster.  For example:
+
 ```bash
-export HF_TOKEN="your-token"
-./llmd-installer.sh --storage-class ocs-storagecluster-cephfs --storage-size 15Gi
+oc login --token=sha256~yourtoken --server=https://api.yourcluster.com:6443
 ```
 
-## Model Service
+The installer will create a ReadWriteMany PVC and download the model to it, if you are using OF, you can pass in the `--storage-class ocs-storagecluster-cephfs` flag.
 
-### Customizing the ModelService
-
-The ModelService looks like:
-
-```yaml
-kind: ModelService
-metadata:
-spec:
+```bash
+export HF_TOKEN="your-token"
+./llmd-installer.sh --storage-class ocs-storagecluster-cephfs --storage-size 15Gi
 ```
 
-### Creating a New Model Service
+### Validation
 
-To create a new model service, you can edit the ModelService custom resource for your needs. Examples have been included.
+#### A simple request
+
+For GPU-enabled clusters, you can quickly verify the setup. Once both the prefill and
+decode pods are running and ready, simply send a curl request to the gateway to confirm that chat
+completions are working.  You can execute the `test-request.sh` script to test the chat completions, or run the following on your own.
+If everything is working as expected, you should receive a response.  You should also see activity in the epp pod.
 
 ```bash
-kubectl apply -f modelservice.yaml
+NAMESPACE=llm-d
+MODEL_ID=Llama-32-3B-Instruct
+GATEWAY_ADDRESS=$(kubectl get gateway -n ${NAMESPACE} | tail -n 1 | awk '{print $3}')
+kubectl run --rm -i curl-temp --image=curlimages/curl --restart=Never -- \
+  curl -X POST \
+  "http://${GATEWAY_ADDRESS}/v1/chat/completions" \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "'${MODEL_ID}'",
+    "messages": [{"content": "Who are you?", "role": "user"}],
+    "stream": false
+  }'
 ```
 
-### Validation
-
-For GPU-enabled clusters, you can quickly verify the setup. Once both the prefill and
-decode pods are running and ready, simply send a curl request to the decode endpoint to confirm that chat
-completions are working.
+For additional troubleshooting, you can check to see if the prefill and decode pods responding to requests.
 
 ```bash
 NAMESPACE=llm-d
-POD_IP=$(kubectl get pods -n ${NAMESPACE} -o jsonpath='{range .items[*]}{.metadata.name}{" "}{.status.podIP}{"\n"}{end}' | grep llama-32-3b-instruct-model-service-decode | awk '{print $2}')
+MODEL_ID=Llama-32-3B-Instruct
+POD_IP=$(kubectl get pods -n ${NAMESPACE} -o jsonpath='{range .items[*]}{.metadata.name}{" "}{.status.podIP}{"\n"}{end}' | grep decode | awk '{print $2}')
 kubectl run --rm -i curl-temp --image=curlimages/curl --restart=Never -- \
   curl -X POST \
   "http://${POD_IP}:8000/v1/chat/completions" \
   -H 'accept: application/json' \
   -H 'Content-Type: application/json' \
   -d '{
-    "model": "Llama-3.2-3B-Instruct",
-    "messages": [{"content": "Who won the World Series in 1986?", "role": "user"}],
+    "model": "'${MODEL_ID}'",
+    "messages": [{"content": "Who are you?", "role": "user"}],
     "stream": false
   }'
 ```

quickstart/test-request.sh

@@ -0,0 +1,43 @@
+NAMESPACE=${1:-llm-d}
+MODEL_ID=${2:-Llama-32-3B-Instruct}
+
+POD_IP=$(kubectl get pods -n ${NAMESPACE} -o jsonpath='{range .items[*]}{.metadata.name}{" "}{.status.podIP}{"\n"}{end}' | grep decode | awk '{print $2}')
+echo "Testing request to ${NAMESPACE} at Pod IP ${POD_IP}"
+
+kubectl run --rm -i curl-temp --image=curlimages/curl --restart=Never -- \
+    curl -X GET \
+    "http://${POD_IP}:8000/v1/models" \
+    -H 'accept: application/json' \
+    -H 'Content-Type: application/json'
+
+
+kubectl run --rm -i curl-temp --image=curlimages/curl --restart=Never -- \
+  curl -X POST \
+  "http://${POD_IP}:8000/v1/chat/completions" \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "'${MODEL_ID}'",
+    "messages": [{"content": "Who are you?", "role": "user"}],
+    "stream": false
+  }'
+
+GATEWAY_ADDRESS=$(kubectl get gateway -n ${NAMESPACE} | tail -n 1 | awk '{print $3}')
+echo "Testing request to ${NAMESPACE} at Gateway IP ${GATEWAY_ADDRESS}"
+
+kubectl run --rm -i curl-temp --image=curlimages/curl --restart=Never -- \
+    curl -X GET \
+    "http://${GATEWAY_ADDRESS}/v1/models" \
+    -H 'accept: application/json' \
+    -H 'Content-Type: application/json'
+
+kubectl run --rm -i curl-temp --image=curlimages/curl --restart=Never -- \
+  curl -X POST \
+  "http://${GATEWAY_ADDRESS}/v1/chat/completions" \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "'${MODEL_ID}'",
+    "messages": [{"content": "Who are you?", "role": "user"}],
+    "stream": false
+  }'