opensearch-docs-cn/_ml-commons-plugin/model-serving-framework.md

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Model-serving framework

ML Commons allows you to serve custom models and use those models to make inferences. For those who want to run their PyTorch deep learning model inside an OpenSearch cluster, you can upload and run that model with the ML Commons REST API.

This page outlines the steps required to upload a custom model and run it with the ML Commons plugin.

Prerequisites

To upload a custom model to OpenSearch, you need to prepare it outside of your OpenSearch cluster. You can use a pretrained model, like one from Huggingface, or train a new model in accordance with your needs.

Model support

As of OpenSearch 2.4, the model-serving framework only supports text embedding models without GPU acceleration.

Model format

To use a model in OpenSearch, you'll need to export the model into a portable format. As of Version 2.4, OpenSearch only supports the TorchScript format.

Furthermore, files must be saved as zip files before upload. Therefore, to ensure that ML Commons can upload your model, compress your TorchScript file before uploading. You can download an example file here.

Model size

Most deep learning models are over 100 MBs, making it difficult to fit the model into a single document. OpenSearch splits the model file into smaller chunks to be stored in a model index. When allocating machine learning (ML) or data nodes for your OpenSearch cluster, be aware of the size of your model to prevent any downtime when making inferences.

Upload model to OpenSearch

Use the URL upload operation for models that already exist on another server, such as GitHub or S3.

POST /_plugins/_ml/models/_upload

The URL upload method requires the following request fields.

Field	Data Type	Description
name	string	The name of the model.
version	string	The version number of the model. Since OpenSearch does not enforce a specific version schema for models, you can choose any number or format that makes sense for your models.
model_format	string	The portable format of the model file. Currently only supports TORCH_SCRIPT.
model_config	string	The model's configuration, including the model_type, embedding_dimension, and framework_type.
url	string	The URL where the model is located.

Sample request

The following sample request uploads version 1.0.0 of a natural language processing (NLP) sentence transformation model named all-MiniLM-L6-v2:

POST /_plugins/_ml/models/_upload
{
  "name": "all-MiniLM-L6-v2",
  "version": "1.0.0",
  "description": "test model",
  "model_format": "TORCH_SCRIPT",
  "model_config": {
    "model_type": "bert",
    "embedding_dimension": 384,
    "framework_type": "sentence_transformers"
  },
  "url": "https://github.com/opensearch-project/ml-commons/raw/2.x/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/text_embedding/all-MiniLM-L6-v2_torchscript_sentence-transformer.zip?raw=true"
}

Sample response

OpenSearch responds with the task_id and task status:

{
  "task_id" : "ew8I44MBhyWuIwnfvDIH", 
  "status" : "CREATED"
}

To see the status of your model upload, pass the task_id into the task API.

Load the model

The load model operation reads the model's chunks from the model index and then creates an instance of the model to load into memory. The bigger the model, the more chunks the model is split into. The more chunks a model index contains, the longer it takes for the model to load into memory.

Get the model_id

To load a model, you need the model_id. To find the model_id, take the task_id from the model's upload operations API response and use the GET _ml/tasks API.

This example request uses the task_id from the upload example.

GET /_plugins/_ml/tasks/ew8I44MBhyWuIwnfvDIH

OpenSearch responds with the model_id:

{
  "model_id" : "WWQI44MBbzI2oUKAvNUt", 
  "task_type" : "UPLOAD_MODEL",
  "function_name" : "TEXT_EMBEDDING",
  "state" : "COMPLETED",
  "worker_node" : "KzONM8c8T4Od-NoUANQNGg",
  "create_time" : 3455961564003,
  "last_update_time" : 3216361373241,
  "is_async" : true
}

Load the model from the model index

With the model_id, you can now load the model from the model's index in order to deploy the model to ML nodes. The load API reads model chunks from the model index, creates an instance of that model, and saves the model instance in the ML node's cache.

Add the model_id to the load API:

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

By default, the ML Commons setting plugins.ml_commons.only_run_on_ml_node is set to false. When false, models load on ML nodes first. If no ML nodes exist, models load on data nodes. When running ML models in production, set plugins.ml_commons.only_run_on_ml_node to true so that models only load on ML nodes.

Sample request: Load into any available ML node

In this example request, OpenSearch loads the model into all available OpenSearch node:

POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_load

Sample request: Load into a specific node

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

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

Sample response

All models load asynchronously. Therefore, the load API responds with a new task_id based on the load and responds with a new status for the task.

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

Check the model load status

With your task_id from the load response, you can use the GET _ml/tasks API to see the load status of your model. Before a loaded model can be used for inferences, the load task's state must be COMPLETED.

Sample request

GET /_plugins/_ml/tasks/hA8P44MBhyWuIwnfvTKP

Sample response

{
  "model_id" : "WWQI44MBbzI2oUKAvNUt",
  "task_type" : "LOAD_MODEL",
  "function_name" : "TEXT_EMBEDDING",
  "state" : "COMPLETED",
  "worker_node" : "KzONM8c8T4Od-NoUANQNGg",
  "create_time" : 1665961803150,
  "last_update_time" : 1665961815959,
  "is_async" : true
}

Use the loaded model for inferences

After the model has been loaded, you can enter the model_id into the predict API to perform inferences.

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

Sample request

POST /_plugins/_ml/_predict/text_embedding/WWQI44MBbzI2oUKAvNUt
{
  "text_docs":[ "today is sunny"],
  "return_number": true,
  "target_response": ["sentence_embedding"]
}

Sample response

{
  "inference_results" : [
    {
      "output" : [
        {
          "name" : "sentence_embedding",
          "data_type" : "FLOAT32",
          "shape" : [
            384
          ],
          "data" : [
            -0.023315024,
            0.08975691,
            0.078479774,
            ...
          ]
        }
      ]
    }
  ]
}

Unload the model

If you're done making predictions with your model, use the unload operation to remove the model from your memory cache. The model will remain accessible in the model index.

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

Sample request

POST /_plugins/_ml/models/MGqJhYMBbbh0ushjm8p_/_unload

Sample response

{
    "s5JwjZRqTY6nOT0EvFwVdA": {
        "stats": {
            "MGqJhYMBbbh0ushjm8p_": "deleted"
        }
    }
}