Rename PodGangSet to PodCliqueSet (#186)

* Update operator/api to now use PodCliqueSet instead of PodGangSet.
* Update doc strings for scheduler/api to use PodCliqueSet instead of
  PodGangSet.
* Regenerated CRDS, clientset and deepcopy functions.
* Regerated API docs.
* Initial commit on adaption of PodGangSet to PodCliqueSet in the rest
  of the code base.
* adapted charts to PodCliqueSet and the changed OperatorConfig
* adapted operator/samples/simple to use PodCliqueSet
* Adapted operator/internal/components to use PodCliqueSet
* Changed docs/installation.md to reflect PodCliqueSet changes.
* Renamed component/podgangset to component/podcliqueset
* Adapted operator/internal/controller to use PodCliqueSet.
* Adapted rest of the operator/internal packages to use PodCliqueSet.
* Adapted operator/test to use PodCliqueSet
* Adapted docs to use PodCliqueSet
* Corrected charts to now use PodCliqueSet
* Corrected Dockerfile label to use PodCliqueSet
* Changed docs/assets to use PodCliqueSet

---------

Signed-off-by: madhav bhargava <madhav.bhargava@sap.com>

Author: unmarshall

README.md

@@ -4,7 +4,7 @@
 
 # Grove
 
-Grove is a Kubernetes API purpose-built for orchestrating AI workloads in GPU clusters, where a single custom resource allows you to hierarchically compose multiple AI components with flexible gang-scheduling and auto-scaling specfications at multiple levels. Through native support for network topology-aware gang scheduling, multi-dimensional auto-scaling and prescriptive startup ordering, Grove enables developers to define complex AI stacks in a concise, declarative, and framework-agnostic manner.
+Grove is a Kubernetes API purpose-built for orchestrating AI workloads in GPU clusters, where a single custom resource allows you to hierarchically compose multiple AI components with flexible gang-scheduling and auto-scaling specification at multiple levels. Through native support for network topology-aware gang scheduling, multidimensional auto-scaling and prescriptive startup ordering, Grove enables developers to define complex AI stacks in a concise, declarative, and framework-agnostic manner.
 
 Grove was originally motivated by the challenges of orchestrating multinode, disaggregated inference systems. It provides a consistent and unified API that allows users to define, configure, and scale prefill, decode, and any other components like routing within a single custom resource. However, it is flexible enough to map naturally to the roles, scaling behaviors, and dependencies of any real-world inference systems, from "traditional" single node aggregated inference to agentic pipelines with multiple models.
 
@@ -15,24 +15,24 @@ Modern inference systems are often no longer single-pod workloads. They involve
 
 ## Core Concepts
 
-The Grove API consists of a user API and a scheduling API. While the user API (`PodGangSet`, `PodClique`, `PodCliqueScalingGroup`) allows users to represent their AI workloads, the scheduling API (`PodGang`) enables scheduler integration to support the network topology-optimized gang-scheduling and auto-scaling requirements of the workload.
+The Grove API consists of a user API and a scheduling API. While the user API (`PodCliqueSet`, `PodClique`, `PodCliqueScalingGroup`) allows users to represent their AI workloads, the scheduling API (`PodGang`) enables scheduler integration to support the network topology-optimized gang-scheduling and auto-scaling requirements of the workload.
 
-| Concept                                                      | Description                                                  |
-| ------------------------------------------------------------ | ------------------------------------------------------------ |
-| [PodGangSet](operator/api/core/v1alpha1/podgangset.go) | The top-level Grove object that defines a group of components managed and colocated together. Also supports autoscaling with topology aware spread of PodGangSet replicas for availability. |
-| [PodClique](operator/api/core/v1alpha1/podclique.go) | A group of pods representing a specific role (e.g., leader, worker, frontend). Each clique has an independent configuration and supports custom scaling logic. |
-| [PodCliqueScalingGroup](operator/api/core/v1alpha1/scalinggroup.go) | A set of PodCliques that scale and are scheduled together. Ideal for tightly coupled roles like prefill leader and worker. |
-| [PodGang](scheduler/api/core/v1alpha1/podgang.go)            | The scheduler API that defines a unit of gang-scheduling. A PodGang is a collection of groups of similar pods, where each pod group defines a minimum number of replicas guaranteed for gang-scheduling. |
+| Concept                                                             | Description                                                                                                                                                                                              |
+|---------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [PodCliqueSet](operator/api/core/v1alpha1/podcliqueset.go)          | The top-level Grove object that defines a group of components managed and colocated together. Also supports autoscaling with topology aware spread of PodCliqueSet replicas for availability.            |
+| [PodClique](operator/api/core/v1alpha1/podclique.go)                | A group of pods representing a specific role (e.g., leader, worker, frontend). Each clique has an independent configuration and supports custom scaling logic.                                           |
+| [PodCliqueScalingGroup](operator/api/core/v1alpha1/scalinggroup.go) | A set of PodCliques that scale and are scheduled together. Ideal for tightly coupled roles like prefill leader and worker.                                                                               |
+| [PodGang](scheduler/api/core/v1alpha1/podgang.go)                   | The scheduler API that defines a unit of gang-scheduling. A PodGang is a collection of groups of similar pods, where each pod group defines a minimum number of replicas guaranteed for gang-scheduling. |
 
 
 ## Key Capabilities
 
 - **Declarative composition of Role-Based Pod Groups**
-  `PodGangSet` API provides users a capability to declaratively compose tightly coupled group of pods with explicit role based logic, e.g. disaggregated roles in a model serving stack such as `prefill`, `decode` and `routing`.
+  `PodCliqueSet` API provides users a capability to declaratively compose tightly coupled group of pods with explicit role based logic, e.g. disaggregated roles in a model serving stack such as `prefill`, `decode` and `routing`.
 - **Flexible Gang Scheduling**
-  `PodClique`'s and `PodCliqueScalingGroup`s allow users to specify flexible gang-scheduling requirements at multiple levels within a `PodGangSet` to prevent resource deadlocks.
+  `PodClique`'s and `PodCliqueScalingGroup`s allow users to specify flexible gang-scheduling requirements at multiple levels within a `PodCliqueSet` to prevent resource deadlocks.
 - **Multi-level Horizontal Auto-Scaling**
-  Supports pluggable horizontal auto-scaling solutions to scale `PodGangSet`, `PodClique` and `PodCliqueScalingGroup` custom resources.
+  Supports pluggable horizontal auto-scaling solutions to scale `PodCliqueSet`, `PodClique` and `PodCliqueScalingGroup` custom resources.
 - **Network Topology-Aware Scheduling**
   Allows specifying network topology pack and spread constraints to optimize for both network performance and service availability.
 - **Custom Startup Dependencies**