docs: remove Ray-based execution page

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

docs/.nav.yml

@@ -106,7 +106,6 @@ nav:
     - Feature Design:
       - Runtime and Stage Execution:
         - Disaggregated Inference: design/feature/disaggregated_inference.md
-        - Ray-based Execution: design/feature/ray_based_execution.md
         - Async Chunk: design/feature/async_chunk.md
         - Async Diffusion Output: design/feature/async_diffusion_output.md
         - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md

docs/design/feature/ray_based_execution.md

@@ -1,59 +0,0 @@
-# Distributed utils
-
-This directory (vllm_omni/distributed/ray_utils) contains utilities for distributed execution in vllm-omni, supporting both **Ray** and **Multiprocessing** backends.
-## 1. Installation
-```bash
-pip install "ray[default]"
-```
-## 2. Ray Utils
-
-The `ray_utils` module provides helper functions for managing Ray clusters and actors, which is essential for:
-*   **Multi-node deployment**: Running pipeline stages across different physical machines.
-*   **Resource management**: Efficient GPU/CPU allocation.
-
-### 2.1 Basic Usage
-
-To use the Ray backend, specify `worker_backend="ray"` when initializing the engine.
-
-**Command Line Example:**
-```bash
-vllm serve Qwen/Qwen2.5-Omni-7B \
-  --omni \
-  --port 8091 \
-  --worker-backend ray \
-  --ray-address auto
-```
-
-### 2.2 Cluster Setup
-
-**Step 1: Start Head Node**
-Run this on your primary machine:
-```bash
-ray start --head --port=6399
-```
-
-**Step 2: Connect Worker Nodes**
-Run this on each worker machine:
-```bash
-ray start --address=<HEAD_NODE_IP>:6399
-```
-
-> **Tip**: For a complete cluster setup script, refer to the vLLM example:
-> [run_cluster.sh](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/run_cluster.sh)
-
-### 2.3 Distributed Connector Support
-
-When running on Ray, the system automatically adapts its communication strategy:
-
-*   **Cross-Node**: Recommended to use `MooncakeTransferEngineConnector` (RDMA, fastest) or `MooncakeStoreConnector` (TCP fallback).
-*   **Same-Node**: Can still use `SharedMemoryConnector` for efficiency, or Ray's native object store (plasma).
-*   **SHM payload routing**: `SharedMemoryConnector` always stores payloads in `/dev/shm`.
-
-### 2.4 Internal Helpers
-
-*   **`initialize_ray_cluster`**: Connects to an existing Ray cluster or starts a local one.
-
-## 3. Troubleshooting
-
-*   **Connection Issues**: Ensure the Ray head node is accessible and ports (default 6399 in this example) are open.
-*   **Version Mismatch**: Ensure all nodes run the same version of Ray and Python.

docs/design/index.md

@@ -13,7 +13,6 @@ while the source paths remain stable so existing links continue to work.
 ### Runtime and stage execution
 
 - [Disaggregated Inference](feature/disaggregated_inference.md)
-- [Ray-based Execution](feature/ray_based_execution.md)
 - [Async Chunk](feature/async_chunk.md)
 - [Async Diffusion Output](feature/async_diffusion_output.md)
 - [Async Omni Output Materialization](feature/omni_async_output_materialization.md)