75 lines
1.9 KiB
Markdown
75 lines
1.9 KiB
Markdown
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---
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layout: default
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title: Deploy model
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parent: Model APIs
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grand_parent: ML Commons API
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nav_order: 30
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---
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# Deploy a model
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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`.
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For information about user access for this API, see [Model access control considerations]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/model-apis/index/#model-access-control-considerations).
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## Path and HTTP methods
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```json
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POST /_plugins/_ml/models/<model_id>/_deploy
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```
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#### Example request: Deploying to all available ML nodes
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In this example request, OpenSearch deploys the model to any available OpenSearch ML node:
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```json
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POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy
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```
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{% include copy-curl.html %}
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#### Example request: Deploying to a specific node
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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:
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```json
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POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy
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{
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"node_ids": ["4PLK7KJWReyX0oWKnBA8nA"]
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}
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```
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{% include copy-curl.html %}
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#### Example response
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```json
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{
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"task_id" : "hA8P44MBhyWuIwnfvTKP",
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"status" : "DEPLOYING"
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}
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```
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## Check the status of model deployment
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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:
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```json
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GET /_plugins/_ml/tasks/hA8P44MBhyWuIwnfvTKP
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```
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{% include copy-curl.html %}
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The response contains the model ID of the model version:
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```json
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{
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"model_id": "Qr1YbogBYOqeeqR7sI9L",
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"task_type": "DEPLOY_MODEL",
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"function_name": "TEXT_EMBEDDING",
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"state": "COMPLETED",
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"worker_node": [
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"N77RInqjTSq_UaLh1k0BUg"
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],
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"create_time": 1685478486057,
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"last_update_time": 1685478491090,
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"is_async": true
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}
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```
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