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.