Repository: kubernetes-sigs/gateway-api-inference-extension

Issue:
Consider using markdown.extensions.nl2br
One of the common frustrations with mkdocs-material is how new lines are handled. Some examples:

Will not be rendered as a list:
```
Paragraph text
* List
* Item
```

Will be rendered as a list:
```
Paragraph text

* List
* Item
```

This is a particular pain point in #129 where we're adding generated API reference docs using https://github.com/elastic/crd-ref-docs that are running into this problem. This has also been a recurring pain point in Gateway API documentation, with far too many lists missing the correct line breaks and therefore not being rendered correctly.

If we were to enable the `markdown.extensions.nl2br` extension, the above problems would go away, but in their place we'd get a different one. It's common to add line breaks in markdown to break up paragraphs for simpler code review and editing, for example, wrapping at 80 chars. If we do this, it creates a strange effect:

**Before extension:**
<img width="719" alt="Screenshot 2025-01-09 at 9 29 56 PM" src="https://github.com/user-attachments/assets/5baaaac4-2c49-42f6-907a-f28c832543bf" />


**After extension:**
<img width="592" alt="Screenshot 2025-01-09 at 9 29 46 PM" src="https://github.com/user-attachments/assets/6e278e28-7ac4-420f-b6a9-9469dabab195" />

**Markdown source:**
```
Gateway API Inference Extension is an official Kubernetes project focused on
extending [Gateway API](https://gateway-api.sigs.k8s.io/) with inference
specific routing extensions.

The overall resource model focuses on 2 new inference-focused
[personas](/concepts/roles-and-personas) and corresponding resources that
they are expected to manage:
```


Hints from issue comments:
@robscott: /kind documentation@kfswain: I prefer dealing with the newline vs the odd wrapping. WDYT?@k8s-triage-robot: The Kubernetes project currently lacks enough contributors to adequately respond to all issues.

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/lifecycle stale

[1]: https://www.kubernetes.dev/docs/guide/issue-triage/@k8s-triage-robot: The Kubernetes project currently lacks enough active contributors to adequately respond to all issues.

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/lifecycle rotten

[1]: https://www.kubernetes.dev/docs/guide/issue-triage/@k8s-triage-robot: The Kubernetes project currently lacks enough active contributors to adequately respond to all issues and PRs.

This bot triages issues according to the following rules:
- After 90d of inactivity, `lifecycle/stale` is applied
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/close not-planned

[1]: https://www.kubernetes.dev/docs/guide/issue-triage/@k8s-ci-robot: @k8s-triage-robot: Closing this issue, marking it as "Not Planned".

<details>

In response to [this](https://github.com/kubernetes-sigs/gateway-api-inference-extension/issues/183#issuecomment-3114607814):

>The Kubernetes project currently lacks enough active contributors to adequately respond to all issues and PRs.
>
>This bot triages issues according to the following rules:
>- After 90d of inactivity, `lifecycle/stale` is applied
>- After 30d of inactivity since `lifecycle/stale` was applied, `lifecycle/rotten` is applied
>- After 30d of inactivity since `lifecycle/rotten` was applied, the issue is closed
>
>You can:
>- Reopen this issue with `/reopen`
>- Mark this issue as fresh with `/remove-lifecycle rotten`
>- Offer to help out with [Issue Triage][1]
>
>Please send feedback to sig-contributor-experience at [kubernetes/community](https://github.com/kubernetes/community).
>
>/close not-planned
>
>[1]: https://www.kubernetes.dev/docs/guide/issue-triage/


Instructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md).  If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes-sigs/prow](https://github.com/kubernetes-sigs/prow/issues/new?title=Prow%20issue:) repository.
</details>

Solution patch:
diff --git a/pkg/ext-proc/backend/inferencemodel_reconciler.go b/pkg/ext-proc/backend/inferencemodel_reconciler.go
index c4aeb58..3164e09 100644
--- a/pkg/ext-proc/backend/inferencemodel_reconciler.go
+++ b/pkg/ext-proc/backend/inferencemodel_reconciler.go
@@ -25,7 +25,7 @@ func (c *InferenceModelReconciler) Reconcile(ctx context.Context, req ctrl.Reque
 	if req.Namespace != c.PoolNamespacedName.Namespace {
 		return ctrl.Result{}, nil
 	}
-	klog.V(1).Info("reconciling InferenceModel", req.NamespacedName)
+	klog.V(1).Infof("reconciling InferenceModel %v", req.NamespacedName)
 
 	service := &v1alpha1.InferenceModel{}
 	if err := c.Get(ctx, req.NamespacedName, service); err != nil {
diff --git a/pkg/manifests/ext_proc.yaml b/pkg/manifests/ext_proc.yaml
index 4eb0415..45bc264 100644
--- a/pkg/manifests/ext_proc.yaml
+++ b/pkg/manifests/ext_proc.yaml
@@ -69,8 +69,7 @@ spec:
     spec:
       containers:
       - name: inference-gateway-ext-proc
-        # TODO(https://github.com/kubernetes-sigs/gateway-api-inference-extension/issues/34) Update the image and args.
-        image: us-central1-docker.pkg.dev/k8s-staging-images/llm-instance-gateway/epp@sha256:1ef39ee79c55db6436f9d4cb14cc957dea647f57b8a7ff0363f60b0711695103
+        image: us-central1-docker.pkg.dev/k8s-staging-images/llm-instance-gateway/epp:main
         args:
         - -poolName
         - "vllm-llama2-7b-pool"
diff --git a/site-src/api-types/inferencemodel.md b/site-src/api-types/inferencemodel.md
index 12a351b..54fe573 100644
--- a/site-src/api-types/inferencemodel.md
+++ b/site-src/api-types/inferencemodel.md
@@ -7,8 +7,13 @@
 
 ## Background
 
-TODO
+An InferenceModel allows the Inference Workload Owner to define:
+
+- Which Model/LoRA adapter(s) to consume.
+  - Mapping from a client facing model name to the target model name in the InferencePool.
+  - InferenceModel allows for traffic splitting between adapters _in the same InferencePool_ to allow for new LoRA adapter versions to be easily rolled out.
+- Criticality of the requests to the InferenceModel.
 
 ## Spec
 
-TODO
\ No newline at end of file
+The full spec of the InferenceModel is defined [here](/reference/spec/#inferencemodel).
\ No newline at end of file
diff --git a/site-src/api-types/inferencepool.md b/site-src/api-types/inferencepool.md
index fdae7e0..baa604b 100644
--- a/site-src/api-types/inferencepool.md
+++ b/site-src/api-types/inferencepool.md
@@ -7,12 +7,28 @@
 
 ## Background
 
-InferencePool is
+The InferencePool resource is a logical grouping of compute resources, e.g. Pods, that run model servers. The InferencePool would deploy its own routing, and offer administrative configuration to the Platform Admin. 
+
+It is expected for the InferencePool to:
+
+ - Enforce fair consumption of resources across competing workloads
+ - Efficiently route requests across shared compute (as displayed by the PoC)
+ 
+It is _not_ expected for the InferencePool to:
+
+ - Enforce any common set of adapters or base models are available on the Pods
+ - Manage Deployments of Pods within the Pool
+ - Manage Pod lifecycle of pods within the pool 
+
+Additionally, any Pod that seeks to join an InferencePool would need to support a protocol, defined by this project, to ensure the Pool has adequate information to intelligently route requests.
+
+`InferencePool` has some small overlap with `Service`, displayed here:
 
 <!-- Source: https://docs.google.com/presentation/d/11HEYCgFi-aya7FS91JvAfllHiIlvfgcp7qpi_Azjk4E/edit#slide=id.g292839eca6d_1_0 -->
 <img src="/images/inferencepool-vs-service.png" alt="Comparing InferencePool with Service" class="center" width="550" />
 
+The InferencePool is _not_ intended to be a mask of the Service object, simply exposing the absolute bare minimum required to allow the Platform Admin to focus less on networking, and more on Pool management. 
 
 ## Spec
 
-TODO
\ No newline at end of file
+The full spec of the InferencePool is defined [here](/reference/spec/#inferencepool).
\ No newline at end of file
diff --git a/site-src/concepts/api-overview.md b/site-src/concepts/api-overview.md
index f7b50d8..94e7625 100644
--- a/site-src/concepts/api-overview.md
+++ b/site-src/concepts/api-overview.md
@@ -1,3 +1,16 @@
 # API Overview
 
-TODO
\ No newline at end of file
+## Bakcground
+The Gateway API Inference Extension project is an extension of the Kubernetes Gateway API for serving Generative AI models on Kubernetes. Gateway API Inference Extension facilitates standardization of APIs for Kubernetes cluster operators and developers running generative AI inference, while allowing flexibility for underlying gateway implementations (such as Envoy Proxy) to iterate on mechanisms for optimized serving of models. 
+
+<img src="/images/inference-overview.svg" alt="Overview of API integration" class="center" width="1000" />
+
+## API Resources
+
+### InferencePool
+
+InferencePool represents a set of Inference-focused Pods and an extension that will be used to route to them. Within the broader Gateway API resource model, this resource is considered a "backend". In practice, that means that you'd replace a Kubernetes Service with an InferencePool. This resource has some similarities to Service (a way to select Pods and specify a port), but has some unique capabilities. With InferenceModel, you can configure a routing extension as well as inference-specific routing optimizations. For more information on this resource, refer to our [InferencePool documentation](/api-types/inferencepool.md) or go directly to the [InferencePool spec](/reference/spec/#inferencepool).
+
+### InferenceModel
+
+An InferenceModel represents a model or adapter, and configuration associated with that model. This resource enables you to configure the relative criticality of a model, and allows you to seamlessly translate the requested model name to one or more backend model names. Multiple InferenceModels can be attached to an InferencePool. For more information on this resource, refer to our [InferenceModel documentation](/api-types/inferencemodel.md) or go directly to the [InferenceModel spec](/reference/spec/#inferencemodel).
diff --git a/site-src/concepts/roles-and-personas.md b/site-src/concepts/roles-and-personas.md
index 4a344e1..b11f43e 100644
--- a/site-src/concepts/roles-and-personas.md
+++ b/site-src/concepts/roles-and-personas.md
@@ -1,3 +1,26 @@
 # Roles and Personas
 
-TODO
\ No newline at end of file
+Before diving into the details of the API, decriptions of the personas these APIs were designed for will help convey the thought process of the API design.
+
+## Inference Platform Admin
+
+The Inference Platform Admin creates and manages the infrastructure necessary to run LLM workloads. Including handling Ops for:
+
+  - Hardware
+  - Model Server
+  - Base Model
+  - Resource Allocation for Workloads
+  - Gateway configuration
+  - etc
+
+## Inference Workload Owner
+
+An Inference Workload Owner persona owns and manages 1 or many Generative AI Workloads (LLM focused *currently*). This includes:
+
+- Defining criticality
+- Managing fine-tunes
+  - LoRA Adapters
+  - System Prompts
+  - Prompt Cache
+  - etc.
+- Managing rollout of adapters
diff --git a/site-src/images/inference-overview.svg b/site-src/images/inference-overview.svg
new file mode 100644
index 0000000..a82c09e
--- /dev/null
+++ b/site-src/images/inference-overview.svg
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/site-src/index.md b/site-src/index.md
index d8dfb77..04d1fad 100644
--- a/site-src/index.md
+++ b/site-src/index.md
@@ -11,28 +11,24 @@ they are expected to manage:
 <!-- Source: https://docs.google.com/presentation/d/11HEYCgFi-aya7FS91JvAfllHiIlvfgcp7qpi_Azjk4E/edit#slide=id.g292839eca6d_1_0 -->
 <img src="/images/resource-model.png" alt="Gateway API Inference Extension Resource Model" class="center" width="550" />
 
+## Key Features 
+Gateway API Inference Extension, along with a reference implementation in Envoy Proxy, provides the following key features: 
+
+- **Model-aware routing**: Instead of simply routing based on the path of the request, Gateway API Inference Extension allows you to route to models based on the model names. This is enabled by support for GenAI Inference API specifications (such as OpenAI API) in the gateway implementations such as in Envoy Proxy. This model-aware routing also extends to Low-Rank Adaptation (LoRA) fine-tuned models.
+
+- **Serving priority**: Gateway API Inference Extension allows you to specify the serving priority of your models. For example, you can specify that your models for online inference of chat tasks (which is more latency sensitive) have a higher [*Criticality*](/reference/spec/#criticality) than a model for latency tolerant tasks such as a summarization. 
+
+- **Model rollouts**:  Gateway API Inference Extension allows you to incrementally roll out new model versions by traffic splitting definitions based on the model names. 
+
+- **Extensibility for Inference Services**: Gateway API Inference Extension defines extensibility pattern for additional Inference services to create bespoke routing capabilities should out of the box solutions not fit your needs.
+
+
+- **Customizable Load Balancing for Inference**: Gateway API Inference Extension defines a pattern for customizable load balancing and request routing that is optimized for Inference. Gateway API Inference Extension provides a reference implementation of model endpoint picking leveraging metrics emitted from the model servers. This endpoint picking mechanism can be used in lieu of traditional load balancing mechanisms. Model Server-aware load balancing ("smart" load balancing as its sometimes referred to in this repo) has been proven to reduce the serving latency and improve utilization of accelerators in your clusters.
+
+
 ## API Resources
 
-### InferencePool
-
-InferencePool represents a set of Inference-focused Pods and an extension that
-will be used to route to them. Within the broader Gateway API resource model,
-this resource is considered a "backend". In practice, that means that you'd
-replace a Kubernetes Service with an InferencePool. This resource has some
-similarities to Service (a way to select Pods and specify a port), but will
-expand to have some inference-specific capabilities. When combined with
-InferenceModel, you can configure a routing extension as well as
-inference-specific routing optimizations. For more information on this resource,
-refer to our [InferencePool documentation](/api-types/inferencepool).
-
-### InferenceModel
-
-An InferenceModel represents a model or adapter, and its associated
-configuration. This resource enables you to configure the relative criticality
-of a model, and allows you to seamlessly translate the requested model name to
-one or more backend model names. Multiple InferenceModels can be attached to an
-InferencePool. For more information on this resource, refer to our
-[InferenceModel documentation](/api-types/inferencemodel).
+Head to our [API overview](/concepts/api-overview/#api-overview) to start exploring our APIs!
 
 ## Composable Layers
 
diff --git a/simulations/llm_ig_simulation/src/__init__.py b/tools/simulations/llm_ig_simulation/src/__init__.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/__init__.py
rename to tools/simulations/llm_ig_simulation/src/__init__.py
diff --git a/simulations/llm_ig_simulation/src/benchmark_one_server.py b/tools/simulations/llm_ig_simulation/src/benchmark_one_server.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/benchmark_one_server.py
rename to tools/simulations/llm_ig_simulation/src/benchmark_one_server.py
diff --git a/simulations/llm_ig_simulation/src/constants.py b/tools/simulations/llm_ig_simulation/src/constants.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/constants.py
rename to tools/simulations/llm_ig_simulation/src/constants.py
diff --git a/simulations/llm_ig_simulation/src/continous_batching.py b/tools/simulations/llm_ig_simulation/src/continous_batching.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/continous_batching.py
rename to tools/simulations/llm_ig_simulation/src/continous_batching.py
diff --git a/simulations/llm_ig_simulation/src/llmactor.py b/tools/simulations/llm_ig_simulation/src/llmactor.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/llmactor.py
rename to tools/simulations/llm_ig_simulation/src/llmactor.py
diff --git a/simulations/llm_ig_simulation/src/loadbalancer.py b/tools/simulations/llm_ig_simulation/src/loadbalancer.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/loadbalancer.py
rename to tools/simulations/llm_ig_simulation/src/loadbalancer.py
diff --git a/simulations/llm_ig_simulation/src/main.py b/tools/simulations/llm_ig_simulation/src/main.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/main.py
rename to tools/simulations/llm_ig_simulation/src/main.py
diff --git a/simulations/llm_ig_simulation/src/request.py b/tools/simulations/llm_ig_simulation/src/request.py
similarity index 100%
rename from simulations/llm_ig_simulation/src/request.py
rename to tools/simulations/llm_ig_simulation/src/request.py
diff --git a/simulations/llm_inference_gateway_simulation.ipynb b/tools/simulations/llm_inference_gateway_simulation.ipynb
similarity index 100%
rename from simulations/llm_inference_gateway_simulation.ipynb
rename to tools/simulations/llm_inference_gateway_simulation.ipynb


Test patch:
diff --git a/test/e2e/e2e_test.go b/test/e2e/e2e_test.go
index 4a1f776..8e5968f 100644
--- a/test/e2e/e2e_test.go
+++ b/test/e2e/e2e_test.go
@@ -64,7 +64,7 @@ var _ = ginkgo.Describe("InferencePool", func() {
 			for _, m := range infModel.Spec.TargetModels {
 				expected = append(expected, m.Name)
 			}
-			actual := []string{}
+			actual := make(map[string]int)
 			gomega.Eventually(func() error {
 				resp, err := testutils.ExecCommandInPod(ctx, cfg, scheme, kubeCli, nsName, "curl", "curl", curlCmd)
 				if err != nil {
@@ -75,12 +75,16 @@ var _ = ginkgo.Describe("InferencePool", func() {
 				}
 				for _, m := range expected {
 					if strings.Contains(resp, m) {
-						actual = append(actual, m)
+						actual[m] = 0
 					}
 				}
+				var got []string
+				for m := range actual {
+					got = append(got, m)
+				}
 				// Compare ignoring order
-				if !cmp.Equal(actual, expected, cmpopts.SortSlices(func(a, b string) bool { return a < b })) {
-					return fmt.Errorf("actual (%v) != expected (%v); resp=%q", actual, expected, resp)
+				if !cmp.Equal(got, expected, cmpopts.SortSlices(func(a, b string) bool { return a < b })) {
+					return fmt.Errorf("actual (%v) != expected (%v); resp=%q", got, expected, resp)
 				}
 
 				return nil
@@ -94,7 +98,7 @@ var _ = ginkgo.Describe("InferencePool", func() {
 func newInferenceModel(ns string) *infextv1a1.InferenceModel {
 	targets := []infextv1a1.TargetModel{
 		{
-			Name:   modelName + "%-0",
+			Name:   modelName + "-0",
 			Weight: ptr.To(int32(50)),
 		},
 		{