Deploy and scale Ray-based ML workloads on Anyscale platform.
## Anyscale MCP Server: Scalable Ray-Based ML Infrastructure The **Anyscale MCP Server** integrates Ray-based distributed computing into Google Antigravity. This platform enables developers to scale Python applications from laptops to clusters, making ML training, inference, and data processing accessible through natural language commands. ### Why Anyscale MCP? Anyscale simplifies distributed computing: - **Ray Foundation**: Built on the popular Ray framework - **Auto-Scaling**: Dynamically adjust compute resources - **Production Ready**: Enterprise-grade infrastructure - **Model Serving**: Deploy models with Ray Serve - **Antigravity Native**: AI-assisted distributed computing ### Key Features #### 1. Distributed Training ```python import ray from ray import train from ray.train.torch import TorchTrainer # Define training function def train_func(config): model = create_model() optimizer = create_optimizer(model, config["lr"]) for epoch in range(config["epochs"]): train_epoch(model, optimizer) return model # Distributed training with Ray trainer = TorchTrainer( train_func, train_loop_config={"lr": 0.001, "epochs": 10}, scaling_config=train.ScalingConfig(num_workers=4) ) result = trainer.fit() ``` #### 2. Model Serving ```python from ray import serve @serve.deployment(num_replicas=2) class ModelDeployment: def __init__(self): self.model = load_model() async def __call__(self, request): data = await request.json() return self.model.predict(data) app = ModelDeployment.bind() serve.run(app) ``` #### 3. Data Processing at Scale ```python import ray @ray.remote def process_batch(batch): # Process data in parallel across cluster return transformed_batch # Distribute across available workers results = ray.get([process_batch.remote(b) for b in batches]) ``` ### Configuration ```json { "mcpServers": { "anyscale": { "command": "npx", "args": ["-y", "@anthropic/mcp-anyscale"], "env": { "ANYSCALE_API_KEY": "your-api-key", "ANYSCALE_PROJECT": "your-project" } } } } ``` ### Use Cases **Distributed Training**: Scale model training across GPU clusters with automatic resource management and fault tolerance. **Hyperparameter Tuning**: Run thousands of experiments in parallel with Ray Tune for optimal model configurations. **Batch Inference**: Process millions of predictions efficiently with auto-scaling Ray Serve deployments. The Anyscale MCP Server empowers Antigravity users with enterprise-grade distributed computing capabilities.
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