You're viewing version 2.13 of the OpenSearch documentation. This version is no longer maintained.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 in memory. This operation requires the model_id
.
Starting with OpenSearch version 2.13, externally hosted models are deployed automatically by default when you send a Predict API request for the first time. To disable automatic deployment for an externally hosted model, set plugins.ml_commons.model_auto_deploy.enable
to false
:
PUT _cluster/settings
{
"persistent": {
"plugins.ml_commons.model_auto_deploy.enable": "false"
}
}
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.