peytondmurray · peytondmurray · Nov 16, 2023 · Nov 14, 2023
diff --git a/doc/source/cluster/getting-started.rst b/doc/source/cluster/getting-started.rst
@@ -3,6 +3,20 @@
 Ray Clusters Overview
 =====================
 
+.. toctree::
+    :hidden:
+
+    Key Concepts <key-concepts>
+    Deploying on Kubernetes <kubernetes/index>
+    Deploying on VMs <vms/index>
+    metrics
+    configure-manage-dashboard
+    Applications Guide <running-applications/index>
+    faq
+    package-overview
+    usage-stats
+
+
 Ray enables seamless scaling of workloads from a laptop to a large cluster. While Ray
 works out of the box on single machines with just a call to ``ray.init``, to run Ray
 applications on multiple nodes you must first *deploy a Ray cluster*.

diff --git a/doc/source/cluster/kubernetes/benchmarks.md b/doc/source/cluster/kubernetes/benchmarks.md
@@ -2,4 +2,10 @@
 
 # KubeRay Benchmarks
 
-- {ref}`kuberay-mem-scalability`
+```{toctree}
+:hidden:
+
+benchmarks/memory-scalability-benchmark
+```
+
+- {ref}`kuberay-mem-scalability`
diff --git a/doc/source/cluster/kubernetes/examples.md b/doc/source/cluster/kubernetes/examples.md
@@ -2,6 +2,18 @@
 
 # Examples
 
+```{toctree}
+:hidden:
+
+examples/ml-example
+examples/gpu-training-example
+examples/stable-diffusion-rayservice
+examples/mobilenet-rayservice
+examples/text-summarizer-rayservice
+examples/rayjob-batch-inference-example
+```
+
+
 This section presents example Ray workloads to try out on your Kubernetes cluster.
 
 - {ref}`kuberay-ml-example` (CPU-only)

diff --git a/doc/source/cluster/kubernetes/getting-started.md b/doc/source/cluster/kubernetes/getting-started.md
@@ -2,6 +2,15 @@
 
 # Getting Started with KubeRay
 
+```{toctree}
+:hidden:
+
+getting-started/raycluster-quick-start
+getting-started/rayjob-quick-start
+getting-started/rayservice-quick-start
+```
+
+
 ## Custom Resource Definitions (CRDs)
 
 [KubeRay](https://github.com/ray-project/kuberay) is a powerful, open-source Kubernetes operator that simplifies the deployment and management of Ray applications on Kubernetes.

diff --git a/doc/source/cluster/kubernetes/index.md b/doc/source/cluster/kubernetes/index.md
@@ -1,4 +1,17 @@
 # Ray on Kubernetes
+
+```{toctree}
+:hidden:
+
+getting-started
+user-guides
+examples
+k8s-ecosystem
+benchmarks
+troubleshooting
+references
+```
+
 (kuberay-index)=
 ## Overview
 
@@ -36,14 +49,14 @@ The Ray docs present all the information you need to start running Ray workloads
 .. grid:: 1 2 2 2
     :gutter: 1
     :class-container: container pb-3
-    
+
     .. grid-item-card::
 
         **Getting Started**
         ^^^
-    
+
         Learn how to start a Ray cluster and deploy Ray applications on Kubernetes.
-    
+
         +++
         .. button-ref:: kuberay-quickstart
             :color: primary
@@ -56,9 +69,9 @@ The Ray docs present all the information you need to start running Ray workloads
 
         **User Guides**
         ^^^
-    
+
         Learn best practices for configuring Ray clusters on Kubernetes.
-    
+
         +++
         .. button-ref:: kuberay-guides
             :color: primary
@@ -71,9 +84,9 @@ The Ray docs present all the information you need to start running Ray workloads
 
         **Examples**
         ^^^
-    
+
         Try example Ray workloads on Kubernetes.
-    
+
         +++
         .. button-ref:: kuberay-examples
             :color: primary
@@ -86,9 +99,9 @@ The Ray docs present all the information you need to start running Ray workloads
 
         **Ecosystem**
         ^^^
-    
+
         Integrate KubeRay with third party Kubernetes ecosystem tools.
-    
+
         +++
         .. button-ref:: kuberay-ecosystem-integration
             :color: primary
@@ -101,24 +114,24 @@ The Ray docs present all the information you need to start running Ray workloads
 
         **Benchmarks**
         ^^^
-    
+
         Check the KubeRay benchmark results.
-    
+
         +++
         .. button-ref:: kuberay-benchmarks
             :color: primary
             :outline:
             :expand:
 
             Benchmark results
-    
+
     .. grid-item-card::
 
         **Troubleshooting**
         ^^^
-    
+
         Consult the KubeRay troubleshooting guides.
-    
+
         +++
         .. button-ref:: kuberay-troubleshooting
             :color: primary

diff --git a/doc/source/cluster/kubernetes/k8s-ecosystem.md b/doc/source/cluster/kubernetes/k8s-ecosystem.md
@@ -2,6 +2,16 @@
 
 # KubeRay Ecosystem
 
+```{toctree}
+:hidden:
+
+k8s-ecosystem/ingress
+k8s-ecosystem/prometheus-grafana
+k8s-ecosystem/pyspy
+k8s-ecosystem/volcano
+k8s-ecosystem/kubeflow
+```
+
 * {ref}`kuberay-ingress`
 * {ref}`kuberay-prometheus-grafana`
 * {ref}`kuberay-pyspy-integration`

diff --git a/doc/source/cluster/kubernetes/troubleshooting.md b/doc/source/cluster/kubernetes/troubleshooting.md
@@ -2,5 +2,12 @@
 
 # KubeRay Troubleshooting
 
+```{toctree}
+:hidden:
+
+troubleshooting/troubleshooting
+troubleshooting/rayservice-troubleshooting
+```
+
 - {ref}`kuberay-troubleshootin-guides`
-- {ref}`kuberay-raysvc-troubleshoot`
+- {ref}`kuberay-raysvc-troubleshoot`
diff --git a/doc/source/cluster/kubernetes/user-guides.md b/doc/source/cluster/kubernetes/user-guides.md
@@ -2,6 +2,31 @@
 
 # User Guides
 
+```{toctree}
+:hidden:
+
+Deploy Ray Serve Apps <user-guides/rayservice>
+user-guides/rayservice-high-availability
+user-guides/observability
+user-guides/upgrade-guide
+user-guides/k8s-cluster-setup
+user-guides/storage
+user-guides/config
+user-guides/configuring-autoscaling
+user-guides/kuberay-gcs-ft
+user-guides/gke-gcs-bucket
+user-guides/logging
+user-guides/gpu
+user-guides/rayserve-dev-doc
+user-guides/pod-command
+user-guides/pod-security
+user-guides/helm-chart-rbac
+user-guides/tls
+user-guides/k8s-autoscaler
+user-guides/static-ray-cluster-without-kuberay
+```
+
+
 :::{note}
 To learn the basics of Ray on Kubernetes, we recommend taking a look
 at the {ref}`introductory guide <kuberay-quickstart>` first.

diff --git a/doc/source/cluster/kubernetes/user-guides/k8s-cluster-setup.md b/doc/source/cluster/kubernetes/user-guides/k8s-cluster-setup.md
@@ -2,6 +2,13 @@
 
 # Managed Kubernetes services
 
+```{toctree}
+:hidden:
+
+aws-eks-gpu-cluster
+gcp-gke-gpu-cluster
+```
+
 The KubeRay operator and Ray can run on any cloud or on-prem Kubernetes cluster.
 The simplest way to provision a remote Kubernetes cluster is to use a cloud-based managed service.
 We collect a few helpful links for users who are getting started with a managed Kubernetes service.

diff --git a/doc/source/cluster/vms/examples/index.md b/doc/source/cluster/vms/examples/index.md
@@ -2,6 +2,12 @@
 
 # Examples
 
+```{toctree}
+:hidden:
+
+ml-example
+```
+
 :::{note}
 To learn the basics of Ray on Cloud VMs, we recommend taking a look
 at the {ref}`introductory guide <vm-cluster-quick-start>` first.

diff --git a/doc/source/cluster/vms/index.md b/doc/source/cluster/vms/index.md
@@ -1,6 +1,15 @@
 # Ray on Cloud VMs
 (cloud-vm-index)=
 
+```{toctree}
+:hidden:
+
+getting-started
+User Guides <user-guides/index>
+Examples <examples/index>
+references/index
+```
+
 ## Overview
 
 In this section we cover how to launch Ray clusters on Cloud VMs. Ray ships with built-in support
@@ -23,59 +32,59 @@ The Ray docs present all the information you need to start running Ray workloads
 .. grid:: 1 2 2 2
     :gutter: 1
     :class-container: container pb-3
-    
+
     .. grid-item-card::
-    
+
         **Getting Started**
         ^^^
-    
+
         Learn how to start a Ray cluster and deploy Ray applications in the cloud.
-    
+
         +++
         .. button-ref:: vm-cluster-quick-start
             :color: primary
             :outline:
             :expand:
 
             Get Started with Ray on Cloud VMs
-    
+
     .. grid-item-card::
 
         **Examples**
         ^^^
-    
+
         Try example Ray workloads in the Cloud
-    
+
         +++
         .. button-ref:: vm-cluster-examples
             :color: primary
             :outline:
             :expand:
 
             Try example workloads
-    
+
     .. grid-item-card::
 
         **User Guides**
         ^^^
-    
+
         Learn best practices for configuring cloud clusters
-    
+
         +++
         .. button-ref:: vm-cluster-guides
             :color: primary
             :outline:
             :expand:
 
             Read the User Guides
-    
+
     .. grid-item-card::
 
         **API Reference**
         ^^^
-    
+
         Find API references for cloud clusters
-    
+
         +++
         .. button-ref:: vm-cluster-api-references
             :color: primary

diff --git a/doc/source/cluster/vms/user-guides/community/index.rst b/doc/source/cluster/vms/user-guides/community/index.rst
@@ -3,6 +3,13 @@
 Community Supported Cluster Managers
 ====================================
 
+.. toctree::
+    :hidden:
+
+    yarn
+    slurm
+    lsf
+
 .. note::
 
     If you're using AWS, Azure, GCP or vSphere you can use the :ref:`Ray cluster launcher <cluster-index>` to simplify the cluster setup process.

diff --git a/doc/source/cluster/vms/user-guides/index.md b/doc/source/cluster/vms/user-guides/index.md
@@ -2,6 +2,16 @@
 
 # User Guides
 
+```{toctree}
+:hidden:
+
+launching-clusters/index
+large-cluster-best-practices
+configuring-autoscaling
+logging
+Community-supported Cluster Managers <community/index>
+```
+
 :::{note}
 To learn the basics of Ray on Cloud VMs, we recommend taking a look
 at the {ref}`introductory guide <vm-cluster-quick-start>` first.

diff --git a/doc/source/data/data.rst b/doc/source/data/data.rst
@@ -4,6 +4,16 @@
 Ray Data: Scalable Datasets for ML
 ==================================
 
+.. toctree::
+    :hidden:
+
+    Overview <overview>
+    key-concepts
+    user-guide
+    examples/index
+    api/api
+    data-internals
+
 Ray Data is a scalable data processing library for ML workloads. It provides flexible and performant APIs for scaling :ref:`Offline batch inference <batch_inference_overview>` and :ref:`Data preprocessing and ingest for ML training <ml_ingest_overview>`. Ray Data uses `streaming execution <https://www.anyscale.com/blog/streaming-distributed-execution-across-cpus-and-gpus>`__ to efficiently process large datasets.
 
 .. image:: images/dataset.svg