ML Infrastructure Bearer Token

Anyscale REST API

Scalable ML infrastructure for Ray workloads

Anyscale provides a unified platform for building, deploying, and managing distributed machine learning applications using Ray. The API enables developers to programmatically create compute clusters, deploy ML models, manage workloads, and monitor distributed training jobs. It's designed for teams scaling from prototypes to production ML systems with complex distributed computing requirements.

Base URL https://api.anyscale.com/v2

API Endpoints

Method	Endpoint	Description
GET	`/clusters`	List all Ray clusters in your organization
POST	`/clusters`	Create a new Ray cluster with specified compute resources
GET	`/clusters/{cluster_id}`	Get details about a specific Ray cluster
DELETE	`/clusters/{cluster_id}`	Terminate a running Ray cluster
POST	`/services`	Deploy a Ray Serve application as a managed service
GET	`/services`	List all deployed Ray Serve services
GET	`/services/{service_id}`	Get details and status of a deployed service
PATCH	`/services/{service_id}`	Update a deployed service configuration
DELETE	`/services/{service_id}`	Delete a deployed Ray Serve service
POST	`/jobs`	Submit a Ray job for execution on a cluster
GET	`/jobs`	List all jobs and their execution status
GET	`/jobs/{job_id}`	Get detailed information about a specific job
GET	`/jobs/{job_id}/logs`	Retrieve logs from a running or completed job
DELETE	`/jobs/{job_id}`	Cancel a running job
GET	`/compute-configs`	List available compute configurations and instance types

Code Examples

curl -X POST https://api.anyscale.com/v2/clusters \
  -H 'Authorization: Bearer anyscale_your_api_token_here' \
  -H 'Content-Type: application/json' \
  -d '{
    "name": "ml-training-cluster",
    "compute_config": "ml.m5.4xlarge",
    "min_workers": 2,
    "max_workers": 10,
    "ray_version": "2.9.0"
  }'

const response = await fetch('https://api.anyscale.com/v2/clusters', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer anyscale_your_api_token_here',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    name: 'ml-training-cluster',
    compute_config: 'ml.m5.4xlarge',
    min_workers: 2,
    max_workers: 10,
    ray_version: '2.9.0'
  })
});

const cluster = await response.json();
console.log('Cluster ID:', cluster.id);

import requests

headers = {
    'Authorization': 'Bearer anyscale_your_api_token_here',
    'Content-Type': 'application/json'
}

data = {
    'name': 'ml-training-cluster',
    'compute_config': 'ml.m5.4xlarge',
    'min_workers': 2,
    'max_workers': 10,
    'ray_version': '2.9.0'
}

response = requests.post(
    'https://api.anyscale.com/v2/clusters',
    headers=headers,
    json=data
)

cluster = response.json()
print(f"Cluster ID: {cluster['id']}")

Connect Anyscale to AI

Deploy a Anyscale MCP server on IOX Cloud and connect it to Claude, ChatGPT, Cursor, or any AI client. Your AI assistant gets direct access to Anyscale through these tools:

create_ray_cluster Create and configure a new Ray cluster with specified compute resources and autoscaling parameters for distributed ML workloads

deploy_ml_service Deploy a trained ML model as a scalable Ray Serve service with automatic load balancing and version management

submit_training_job Submit a distributed training job to an Anyscale cluster with resource specifications and monitoring capabilities

monitor_cluster_metrics Retrieve real-time metrics and resource utilization data from Ray clusters including CPU, GPU, and memory usage

manage_job_lifecycle Start, stop, monitor, and retrieve logs from Ray jobs running on Anyscale infrastructure

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Anyscale REST API

API Endpoints

Code Examples

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