docs: Reorganize documentation on the webpage (#2651)

# What does this PR do?
Reorganizes the Llama stack webpage into more concise index pages,
introduce more of a workflow, and reduce repetition of content.

New nav structure so far based on #2637 

Further discussions in
https://github.com/meta-llama/llama-stack/discussions/2585

**Preview:**
![Screenshot 2025-07-09 at 2 31
53 PM](https://github.com/user-attachments/assets/4c1f3845-b328-4f12-9f20-3f09375007af)

You can also build a full local preview locally 

 **Feedback**
Looking for feedback on page titles and general feedback on the new
structure

**Follow up documentation**
I plan on reducing some sections and standardizing some terminology in a
follow up PR.
More discussions on that in
https://github.com/meta-llama/llama-stack/discussions/2585

docs/source/distributions/index.md

@@ -6,14 +6,9 @@ This section provides an overview of the distributions available in Llama Stack.
 
 ```{toctree}
 :maxdepth: 3
-
+list_of_distributions
+building_distro
+customizing_run_yaml
 importing_as_library
 configuration
-customizing_run_yaml
-list_of_distributions
-kubernetes_deployment
-building_distro
-on_device_distro
-remote_hosted_distro
-self_hosted_distro
 ```

docs/source/distributions/kubernetes_deployment.md

@@ -1,236 +0,0 @@
-# Kubernetes Deployment Guide
-
-Instead of starting the Llama Stack and vLLM servers locally. We can deploy them in a Kubernetes cluster.
-
-### Prerequisites
-In this guide, we'll use a local [Kind](https://kind.sigs.k8s.io/) cluster and a vLLM inference service in the same cluster for demonstration purposes.
-
-Note: You can also deploy the Llama Stack server in an AWS EKS cluster. See [Deploying Llama Stack Server in AWS EKS](#deploying-llama-stack-server-in-aws-eks) for more details.
-
-First, create a local Kubernetes cluster via Kind:
-
-```
-kind create cluster --image kindest/node:v1.32.0 --name llama-stack-test
-```
-
-First set your hugging face token as an environment variable.
-```
-export HF_TOKEN=$(echo -n "your-hf-token" | base64)
-```
-
-Now create a Kubernetes PVC and Secret for downloading and storing Hugging Face model:
-
-```
-cat <<EOF |kubectl apply -f -
-apiVersion: v1
-kind: PersistentVolumeClaim
-metadata:
-  name: vllm-models
-spec:
-  accessModes:
-    - ReadWriteOnce
-  volumeMode: Filesystem
-  resources:
-    requests:
-      storage: 50Gi
----
-apiVersion: v1
-kind: Secret
-metadata:
-  name: hf-token-secret
-type: Opaque
-data:
-  token: $HF_TOKEN
-EOF
-```
-
-
-Next, start the vLLM server as a Kubernetes Deployment and Service:
-
-```
-cat <<EOF |kubectl apply -f -
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: vllm-server
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app.kubernetes.io/name: vllm
-  template:
-    metadata:
-      labels:
-        app.kubernetes.io/name: vllm
-    spec:
-      containers:
-      - name: vllm
-        image: vllm/vllm-openai:latest
-        command: ["/bin/sh", "-c"]
-        args: [
-          "vllm serve meta-llama/Llama-3.2-1B-Instruct"
-        ]
-        env:
-        - name: HUGGING_FACE_HUB_TOKEN
-          valueFrom:
-            secretKeyRef:
-              name: hf-token-secret
-              key: token
-        ports:
-          - containerPort: 8000
-        volumeMounts:
-          - name: llama-storage
-            mountPath: /root/.cache/huggingface
-      volumes:
-      - name: llama-storage
-        persistentVolumeClaim:
-          claimName: vllm-models
----
-apiVersion: v1
-kind: Service
-metadata:
-  name: vllm-server
-spec:
-  selector:
-    app.kubernetes.io/name: vllm
-  ports:
-  - protocol: TCP
-    port: 8000
-    targetPort: 8000
-  type: ClusterIP
-EOF
-```
-
-We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model):
-
-```
-$ kubectl logs -l app.kubernetes.io/name=vllm
-...
-INFO:     Started server process [1]
-INFO:     Waiting for application startup.
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
-```
-
-Then we can modify the Llama Stack run configuration YAML with the following inference provider:
-
-```yaml
-providers:
-  inference:
-  - provider_id: vllm
-    provider_type: remote::vllm
-    config:
-      url: http://vllm-server.default.svc.cluster.local:8000/v1
-      max_tokens: 4096
-      api_token: fake
-```
-
-Once we have defined the run configuration for Llama Stack, we can build an image with that configuration and the server source code:
-
-```
-tmp_dir=$(mktemp -d) && cat >$tmp_dir/Containerfile.llama-stack-run-k8s <<EOF
-FROM distribution-myenv:dev
-
-RUN apt-get update && apt-get install -y git
-RUN git clone https://github.com/meta-llama/llama-stack.git /app/llama-stack-source
-
-ADD ./vllm-llama-stack-run-k8s.yaml /app/config.yaml
-EOF
-podman build -f $tmp_dir/Containerfile.llama-stack-run-k8s -t llama-stack-run-k8s $tmp_dir
-```
-
-### Deploying Llama Stack Server in Kubernetes
-
-We can then start the Llama Stack server by deploying a Kubernetes Pod and Service:
-
-```
-cat <<EOF |kubectl apply -f -
-apiVersion: v1
-kind: PersistentVolumeClaim
-metadata:
-  name: llama-pvc
-spec:
-  accessModes:
-    - ReadWriteOnce
-  resources:
-    requests:
-      storage: 1Gi
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: llama-stack-server
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app.kubernetes.io/name: llama-stack
-  template:
-    metadata:
-      labels:
-        app.kubernetes.io/name: llama-stack
-    spec:
-      containers:
-      - name: llama-stack
-        image: localhost/llama-stack-run-k8s:latest
-        imagePullPolicy: IfNotPresent
-        command: ["python", "-m", "llama_stack.distribution.server.server", "--config", "/app/config.yaml"]
-        ports:
-          - containerPort: 5000
-        volumeMounts:
-          - name: llama-storage
-            mountPath: /root/.llama
-      volumes:
-      - name: llama-storage
-        persistentVolumeClaim:
-          claimName: llama-pvc
----
-apiVersion: v1
-kind: Service
-metadata:
-  name: llama-stack-service
-spec:
-  selector:
-    app.kubernetes.io/name: llama-stack
-  ports:
-  - protocol: TCP
-    port: 5000
-    targetPort: 5000
-  type: ClusterIP
-EOF
-```
-
-### Verifying the Deployment
-We can check that the LlamaStack server has started:
-
-```
-$ kubectl logs -l app.kubernetes.io/name=llama-stack
-...
-INFO:     Started server process [1]
-INFO:     Waiting for application startup.
-INFO:     ASGI 'lifespan' protocol appears unsupported.
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit)
-```
-
-Finally, we forward the Kubernetes service to a local port and test some inference requests against it via the Llama Stack Client:
-
-```
-kubectl port-forward service/llama-stack-service 5000:5000
-llama-stack-client --endpoint http://localhost:5000 inference chat-completion --message "hello, what model are you?"
-```
-
-## Deploying Llama Stack Server in AWS EKS
-
-We've also provided a script to deploy the Llama Stack server in an AWS EKS cluster. Once you have an [EKS cluster](https://docs.aws.amazon.com/eks/latest/userguide/getting-started.html), you can run the following script to deploy the Llama Stack server.
-
-
-```
-cd docs/source/distributions/eks
-./apply.sh
-```
-
-This script will:
-
-- Set up a default storage class for AWS EKS
-- Deploy the Llama Stack server in a Kubernetes Pod and Service

docs/source/distributions/starting_llama_stack_server.md

@@ -28,5 +28,4 @@ If you have built a container image and want to deploy it in a Kubernetes cluste
 
 importing_as_library
 configuration
-kubernetes_deployment
 ```