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This version of the OpenSearch documentation is no longer maintained. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.

Deploy a model

The deploy model operation reads the model’s chunks from the model index and then creates an instance of the model to cache into memory. This operation requires the model_id.

For information about user access for this API, see Model access control considerations.

Path and HTTP methods

POST /_plugins/_ml/models/<model_id>/_deploy

Example request: Deploying to all available ML nodes

In this example request, OpenSearch deploys the model to any available OpenSearch ML node:

POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy

Example request: Deploying to a specific node

If you want to reserve the memory of other ML nodes within your cluster, you can deploy your model to a specific node(s) by specifying the node_ids in the request body:

POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy
{
    "node_ids": ["4PLK7KJWReyX0oWKnBA8nA"]
}

Example response

{
  "task_id" : "hA8P44MBhyWuIwnfvTKP",
  "status" : "DEPLOYING"
}

Check the status of model deployment

To see the status of your model deployment and retrieve the model ID created for the new model version, pass the task_id as a path parameter to the Tasks API:

GET /_plugins/_ml/tasks/hA8P44MBhyWuIwnfvTKP

The response contains the model ID of the model version:

{
  "model_id": "Qr1YbogBYOqeeqR7sI9L",
  "task_type": "DEPLOY_MODEL",
  "function_name": "TEXT_EMBEDDING",
  "state": "COMPLETED",
  "worker_node": [
    "N77RInqjTSq_UaLh1k0BUg"
  ],
  "create_time": 1685478486057,
  "last_update_time": 1685478491090,
  "is_async": true
}

If a cluster or node is restarted, then you need to redeploy the model. To learn how to set up automatic redeployment, see Enable auto redeploy.

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