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src/ray/README.md

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+# Ray
+
+[English](./README.md) | [中文](./README.zh.md)
+
+This service deploys a Ray cluster with 1 head node and 2 worker nodes for distributed computing.
+
+## Services
+
+- `ray-head`: Ray head node with dashboard.
+- `ray-worker-1`: First Ray worker node.
+- `ray-worker-2`: Second Ray worker node.
+
+## Environment Variables
+
+| Variable Name               | Description                | Default Value      |
+| --------------------------- | -------------------------- | ------------------ |
+| RAY_VERSION                 | Ray image version          | `2.42.1-py312`     |
+| RAY_HEAD_NUM_CPUS           | Head node CPU count        | `4`                |
+| RAY_HEAD_MEMORY             | Head node memory (bytes)   | `8589934592` (8GB) |
+| RAY_WORKER_NUM_CPUS         | Worker node CPU count      | `2`                |
+| RAY_WORKER_MEMORY           | Worker node memory (bytes) | `4294967296` (4GB) |
+| RAY_DASHBOARD_PORT_OVERRIDE | Ray Dashboard port         | `8265`             |
+| RAY_CLIENT_PORT_OVERRIDE    | Ray Client Server port     | `10001`            |
+| RAY_GCS_PORT_OVERRIDE       | Ray GCS Server port        | `6379`             |
+
+Please modify the `.env` file as needed for your use case.
+
+## Volumes
+
+- `ray_storage`: Shared storage for Ray temporary files.
+
+## Usage
+
+### Start the Cluster
+
+```bash
+docker-compose up -d
+```
+
+### Access Ray Dashboard
+
+Open your browser and navigate to:
+
+```text
+http://localhost:8265
+```
+
+The dashboard shows cluster status, running jobs, and resource usage.
+
+### Connect from Python Client
+
+```python
+import ray
+
+# Connect to the Ray cluster
+ray.init(address="ray://localhost:10001")
+
+# Run a simple task
+@ray.remote
+def hello_world():
+    return "Hello from Ray!"
+
+# Execute the task
+result = ray.get(hello_world.remote())
+print(result)
+
+# Check cluster resources
+print(ray.cluster_resources())
+```
+
+### Distributed Computing Example
+
+```python
+import ray
+import time
+
+ray.init(address="ray://localhost:10001")
+
+@ray.remote
+def compute_task(x):
+    time.sleep(1)
+    return x * x
+
+# Submit 100 tasks in parallel
+results = ray.get([compute_task.remote(i) for i in range(100)])
+print(f"Sum of squares: {sum(results)}")
+```
+
+### Using Ray Data
+
+```python
+import ray
+
+ray.init(address="ray://localhost:10001")
+
+# Create a dataset
+ds = ray.data.range(1000)
+
+# Process data in parallel
+result = ds.map(lambda x: x * 2).take(10)
+print(result)
+```
+
+## Features
+
+- **Distributed Computing**: Scale Python applications across multiple nodes
+- **Auto-scaling**: Dynamic resource allocation
+- **Ray Dashboard**: Web UI for monitoring and debugging
+- **Ray Data**: Distributed data processing
+- **Ray Train**: Distributed training for ML models
+- **Ray Serve**: Model serving and deployment
+- **Ray Tune**: Hyperparameter tuning
+
+## Notes
+
+- Workers automatically connect to the head node
+- The cluster has 1 head node (4 CPU, 8GB RAM) and 2 workers (2 CPU, 4GB RAM each)
+- Total cluster resources: 8 CPUs, 16GB RAM
+- Add more workers by duplicating the worker service definition
+- For GPU support, use `rayproject/ray-ml` image and configure NVIDIA runtime
+- Ray uses Redis protocol on port 6379 for cluster communication
+
+## Scaling
+
+To add more worker nodes, add new service definitions:
+
+```yaml
+ray-worker-3:
+  <<: *default
+  image: rayproject/ray:${RAY_VERSION:-2.42.1-py312}
+  container_name: ray-worker-3
+  command: ray start --address=ray-head:6379 --block
+  depends_on:
+    - ray-head
+  environment:
+    RAY_NUM_CPUS: ${RAY_WORKER_NUM_CPUS:-2}
+    RAY_MEMORY: ${RAY_WORKER_MEMORY:-4294967296}
+```
+
+## License
+
+Ray is licensed under the Apache License 2.0.