ML Commons API
Table of contents
- Train model
- Get model information
- Upload a model
- Load model
- Unload a model
- Example: Unload model from all ML nodes
- Response: Unload model from all ML nodes
- Example: Unload specific models from specific nodes
- Response: Unload specific models from specific nodes
- Response: Unload all models from specific nodes
- Example: Unload specific models from all nodes
- Response: Unload specific models from all nodes
- Search model
- Delete model
- Profile
- Predict
- Train and predict
- Get task information
- Search task
- Delete task
- Stats
- Execute
The Machine Learning (ML) commons API lets you train ML algorithms synchronously and asynchronously, make predictions with that trained model, and train and predict with the same data set.
In order to train tasks through the API, three inputs are required.
- Algorithm name: Must be one of a FunctionName. This determines what algorithm the ML Engine runs. To add a new function, see How To Add a New Function.
- Model hyper parameters: Adjust these parameters to make the model train better.
- Input data: The data input that trains the ML model, or applies the ML models to predictions. You can input data in two ways, query against your index or use data frame.
Train model
Training can occur both synchronously and asynchronously.
Request
The following examples use the kmeans algorithm to train index data.
Train with kmeans synchronously
POST /_plugins/_ml/_train/kmeans
{
"parameters": {
"centroids": 3,
"iterations": 10,
"distance_type": "COSINE"
},
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
Train with kmeans asynchronously
POST /_plugins/_ml/_train/kmeans?async=true
{
"parameters": {
"centroids": 3,
"iterations": 10,
"distance_type": "COSINE"
},
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
Response
Synchronously
For synchronous responses, the API returns the model_id, which can be used to get or delete a model.
{
"model_id" : "lblVmX8BO5w8y8RaYYvN",
"status" : "COMPLETED"
}
Asynchronously
For asynchronous responses, the API returns the task_id, which can be used to get or delete a task.
{
"task_id" : "lrlamX8BO5w8y8Ra2otd",
"status" : "CREATED"
}
Get model information
You can retrieve information on your model using the model_id.
GET /_plugins/_ml/models/<model-id>
The API returns information on the model, the algorithm used, and the content found within the model.
{
"name" : "KMEANS",
"algorithm" : "KMEANS",
"version" : 1,
"content" : ""
}
Upload a model
Use the upload operation to upload a custom model to a model index. ML Commons splits the model into smaller chunks and saves those chunks in the model’s index.
POST /_plugins/_ml/models/_upload
Request fields
All request fields are required.
| Field | Data type | Description |
|---|---|---|
name | string | The name of the model. |
version | integer | The version number of the model. |
model_format | string | The portable format of the model file. Currently only supports TORCH_SCRIPT. |
model_config | json object | The model’s configuration, including the model_type, embedding_dimension, and framework_type. all_config is an optional JSON string which contains all model configurations. |
url | string | The URL which contains the model. |
Example
The following example request uploads version 1.0.0 of an 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"
}
Response
OpenSearch responds with the task_id and task status.
{
"task_id" : "ew8I44MBhyWuIwnfvDIH",
"status" : "CREATED"
}
To see the status of your model upload, enter the task_id into the task API. Use the model_id from the task response once the upload is complete. For example:
{
"model_id" : "WWQI44MBbzI2oUKAvNUt",
"task_type" : "UPLOAD_MODEL",
"function_name" : "TEXT_EMBEDDING",
"state" : "COMPLETED",
"worker_node" : "KzONM8c8T4Od-NoUANQNGg",
"create_time" : 1665961344003,
"last_update_time" : 1665961373047,
"is_async" : true
}
Load model
The load model operation reads the model’s chunks from the model index, then creates an instance of the model to cache into memory. This operation requires the model_id.
POST /_plugins/_ml/models/<model_id>/_load
Example: Load into all available ML nodes
In this example request, OpenSearch loads the model into any available OpenSearch ML node:
POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_load
Example: 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 the node_ids in the request body:
POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_load
{
"node_ids": ["4PLK7KJWReyX0oWKnBA8nA"]
}
Response
{
"task_id" : "hA8P44MBhyWuIwnfvTKP",
"status" : "CREATED"
}
Unload a model
To unload a model from memory, use the unload operation.
POST /_plugins/_ml/models/<model_id>/_unload
Example: Unload model from all ML nodes
POST /_plugins/_ml/models/MGqJhYMBbbh0ushjm8p_/_unload
Response: Unload model from all ML nodes
{
"s5JwjZRqTY6nOT0EvFwVdA": {
"stats": {
"MGqJhYMBbbh0ushjm8p_": "unloaded"
}
}
}
Example: Unload specific models from specific nodes
POST /_plugins/_ml/models/_unload
{
"node_ids": ["sv7-3CbwQW-4PiIsDOfLxQ"],
"model_ids": ["KDo2ZYQB-v9VEDwdjkZ4"]
}
Response: Unload specific models from specific nodes
{
"sv7-3CbwQW-4PiIsDOfLxQ" : {
"stats" : {
"KDo2ZYQB-v9VEDwdjkZ4" : "unloaded"
}
}
}
Response: Unload all models from specific nodes
{
"sv7-3CbwQW-4PiIsDOfLxQ" : {
"stats" : {
"KDo2ZYQB-v9VEDwdjkZ4" : "unloaded",
"-8o8ZYQBvrLMaN0vtwzN" : "unloaded"
}
}
}
Example: Unload specific models from all nodes
{
"model_ids": ["KDo2ZYQB-v9VEDwdjkZ4"]
}
Response: Unload specific models from all nodes
{
"sv7-3CbwQW-4PiIsDOfLxQ" : {
"stats" : {
"KDo2ZYQB-v9VEDwdjkZ4" : "unloaded"
}
}
}
Search model
Use this command to search models you’ve already created.
POST /_plugins/_ml/models/_search
{query}
Example: Query all models
POST /_plugins/_ml/models/_search
{
"query": {
"match_all": {}
},
"size": 1000
}
Example: Query models with algorithm “FIT_RCF”
POST /_plugins/_ml/models/_search
{
"query": {
"term": {
"algorithm": {
"value": "FIT_RCF"
}
}
}
}
Response
{
"took" : 8,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 2.4159138,
"hits" : [
{
"_index" : ".plugins-ml-model",
"_id" : "-QkKJX8BvytMh9aUeuLD",
"_version" : 1,
"_seq_no" : 12,
"_primary_term" : 15,
"_score" : 2.4159138,
"_source" : {
"name" : "FIT_RCF",
"version" : 1,
"content" : "xxx",
"algorithm" : "FIT_RCF"
}
},
{
"_index" : ".plugins-ml-model",
"_id" : "OxkvHn8BNJ65KnIpck8x",
"_version" : 1,
"_seq_no" : 2,
"_primary_term" : 8,
"_score" : 2.4159138,
"_source" : {
"name" : "FIT_RCF",
"version" : 1,
"content" : "xxx",
"algorithm" : "FIT_RCF"
}
}
]
}
}
Delete model
Deletes a model based on the model_id
DELETE /_plugins/_ml/models/<model_id>
The API returns the following:
{
"_index" : ".plugins-ml-model",
"_id" : "MzcIJX8BA7mbufL6DOwl",
"_version" : 2,
"result" : "deleted",
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"_seq_no" : 27,
"_primary_term" : 18
}
Profile
Returns runtime information on ML tasks and models. This operation can help debug issues with models at runtime.
GET /_plugins/_ml/profile
GET /_plugins/_ml/profile/models
GET /_plugins/_ml/profile/tasks
Path parameters
| Parameter | Data type | Description |
|---|---|---|
| model_id | string | Returns runtime data for a specific model. You can string together multiple model_ids to return multiple model profiles. |
| tasks | string | Returns runtime data for a specific task. You can string together multiple task_ids to return multiple task profiles. |
Request fields
All profile body request fields are optional.
| Field | Data type | Description |
|---|---|---|
| node_ids | string | Returns all tasks and profiles from a specific node. |
| model_ids | string | Returns runtime data for a specific model. You can string together multiple model_ids to return multiple model profiles. |
| task_ids | string | Returns runtime data for a specific task. You can string together multiple task_ids to return multiple task profiles. |
| return_all_tasks | boolean | Determines whether or not a request returns all tasks. When set to false task profiles are left out of the response. |
| return_all_models | boolean | Determines whether or not a profile request returns all models. When set to false model profiles are left out of the response. |
Example: Return all tasks and models on a specific node
GET /_plugins/_ml/profile
{
"node_ids": ["KzONM8c8T4Od-NoUANQNGg"],
"return_all_tasks": true,
"return_all_models": true
}
Response: Return all tasks and models on a specific node
{
"nodes" : {
"qTduw0FJTrmGrqMrxH0dcA" : { # node id
"models" : {
"WWQI44MBbzI2oUKAvNUt" : { # model id
"worker_nodes" : [ # routing table
"KzONM8c8T4Od-NoUANQNGg"
]
}
}
},
...
"KzONM8c8T4Od-NoUANQNGg" : { # node id
"models" : {
"WWQI44MBbzI2oUKAvNUt" : { # model id
"model_state" : "LOADED", # model status
"predictor" : "org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel@592814c9",
"worker_nodes" : [ # routing table
"KzONM8c8T4Od-NoUANQNGg"
],
"predict_request_stats" : { # predict request stats on this node
"count" : 2, # total predict requests on this node
"max" : 89.978681, # max latency in milliseconds
"min" : 5.402,
"average" : 47.6903405,
"p50" : 47.6903405,
"p90" : 81.5210129,
"p99" : 89.13291418999998
}
}
}
},
...
}
Predict
ML Commons can predict new data with your trained model either from indexed data or a data frame. To use the Predict API, the model_id is required.
POST /_plugins/_ml/_predict/<algorithm_name>/<model_id>
Request
POST /_plugins/_ml/_predict/kmeans/<model-id>
{
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
Response
{
"status" : "COMPLETED",
"prediction_result" : {
"column_metas" : [
{
"name" : "ClusterID",
"column_type" : "INTEGER"
}
],
"rows" : [
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
}
]
}
Train and predict
Use to train and then immediately predict against the same training data set. Can only be used with unsupervised learning models and the following algorithms:
- BATCH_RCF
- FIT_RCF
- kmeans
Example: Train and predict with indexed data
POST /_plugins/_ml/_train_predict/kmeans
{
"parameters": {
"centroids": 2,
"iterations": 10,
"distance_type": "COSINE"
},
"input_query": {
"query": {
"bool": {
"filter": [
{
"range": {
"k1": {
"gte": 0
}
}
}
]
}
},
"size": 10
},
"input_index": [
"test_data"
]
}
Example: Train and predict with data directly
POST /_plugins/_ml/_train_predict/kmeans
{
"parameters": {
"centroids": 2,
"iterations": 1,
"distance_type": "EUCLIDEAN"
},
"input_data": {
"column_metas": [
{
"name": "k1",
"column_type": "DOUBLE"
},
{
"name": "k2",
"column_type": "DOUBLE"
}
],
"rows": [
{
"values": [
{
"column_type": "DOUBLE",
"value": 1.00
},
{
"column_type": "DOUBLE",
"value": 2.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 1.00
},
{
"column_type": "DOUBLE",
"value": 4.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 1.00
},
{
"column_type": "DOUBLE",
"value": 0.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 10.00
},
{
"column_type": "DOUBLE",
"value": 2.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 10.00
},
{
"column_type": "DOUBLE",
"value": 4.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 10.00
},
{
"column_type": "DOUBLE",
"value": 0.00
}
]
}
]
}
}
Response
{
"status" : "COMPLETED",
"prediction_result" : {
"column_metas" : [
{
"name" : "ClusterID",
"column_type" : "INTEGER"
}
],
"rows" : [
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
}
]
}
}
Get task information
You can retrieve information about a task using the task_id.
GET /_plugins/_ml/tasks/<task_id>
The response includes information about the task.
{
"model_id" : "l7lamX8BO5w8y8Ra2oty",
"task_type" : "TRAINING",
"function_name" : "KMEANS",
"state" : "COMPLETED",
"input_type" : "SEARCH_QUERY",
"worker_node" : "54xOe0w8Qjyze00UuLDfdA",
"create_time" : 1647545342556,
"last_update_time" : 1647545342587,
"is_async" : true
}
Search task
Search tasks based on parameters indicated in the request body.
GET /_plugins/_ml/tasks/_search
{query body}
Example: Search task which “function_name” is “KMEANS”
GET /_plugins/_ml/tasks/_search
{
"query": {
"bool": {
"filter": [
{
"term": {
"function_name": "KMEANS"
}
}
]
}
}
}
Response
{
"took" : 12,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : ".plugins-ml-task",
"_id" : "_wnLJ38BvytMh9aUi-Ia",
"_version" : 4,
"_seq_no" : 29,
"_primary_term" : 4,
"_score" : 0.0,
"_source" : {
"last_update_time" : 1645640125267,
"create_time" : 1645640125209,
"is_async" : true,
"function_name" : "KMEANS",
"input_type" : "SEARCH_QUERY",
"worker_node" : "jjqFrlW7QWmni1tRnb_7Dg",
"state" : "COMPLETED",
"model_id" : "AAnLJ38BvytMh9aUi-M2",
"task_type" : "TRAINING"
}
},
{
"_index" : ".plugins-ml-task",
"_id" : "wwRRLX8BydmmU1x6I-AI",
"_version" : 3,
"_seq_no" : 38,
"_primary_term" : 7,
"_score" : 0.0,
"_source" : {
"last_update_time" : 1645732766656,
"create_time" : 1645732766472,
"is_async" : true,
"function_name" : "KMEANS",
"input_type" : "SEARCH_QUERY",
"worker_node" : "A_IiqoloTDK01uZvCjREaA",
"state" : "COMPLETED",
"model_id" : "xARRLX8BydmmU1x6I-CG",
"task_type" : "TRAINING"
}
}
]
}
}
Delete task
Delete a task based on the task_id.
ML Commons does not check the task status when running the Delete request. There is a risk that a currently running task could be deleted before the task completes. To check the status of a task, run GET /_plugins/_ml/tasks/<task_id> before task deletion.
DELETE /_plugins/_ml/tasks/{task_id}
The API returns the following:
{
"_index" : ".plugins-ml-task",
"_id" : "xQRYLX8BydmmU1x6nuD3",
"_version" : 4,
"result" : "deleted",
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"_seq_no" : 42,
"_primary_term" : 7
}
Stats
Get statistics related to the number of tasks.
To receive all stats, use:
GET /_plugins/_ml/stats
To receive stats for a specific node, use:
GET /_plugins/_ml/<nodeId>/stats/
To receive stats for a specific node and return a specified stat, use:
GET /_plugins/_ml/<nodeId>/stats/<stat>
To receive information on a specific stat from all nodes, use:
GET /_plugins/_ml/stats/<stat>
Example: Get all stats
GET /_plugins/_ml/stats
Response
{
"zbduvgCCSOeu6cfbQhTpnQ" : {
"ml_executing_task_count" : 0
},
"54xOe0w8Qjyze00UuLDfdA" : {
"ml_executing_task_count" : 0
},
"UJiykI7bTKiCpR-rqLYHyw" : {
"ml_executing_task_count" : 0
},
"zj2_NgIbTP-StNlGZJlxdg" : {
"ml_executing_task_count" : 0
},
"jjqFrlW7QWmni1tRnb_7Dg" : {
"ml_executing_task_count" : 0
},
"3pSSjl5PSVqzv5-hBdFqyA" : {
"ml_executing_task_count" : 0
},
"A_IiqoloTDK01uZvCjREaA" : {
"ml_executing_task_count" : 0
}
}
Execute
Some algorithms, such as Localization, don’t require trained models. You can run no-model-based algorithms using the execute API.
POST _plugins/_ml/_execute/<algorithm_name>
Example: Execute localization
The following example uses the Localization algorithm to find subset-level information for aggregate data (for example, aggregated over time) that demonstrates the activity of interest, such as spikes, drops, changes, or anomalies.
POST /_plugins/_ml/_execute/anomaly_localization
{
"index_name": "rca-index",
"attribute_field_names": [
"attribute"
],
"aggregations": [
{
"sum": {
"sum": {
"field": "value"
}
}
}
],
"time_field_name": "timestamp",
"start_time": 1620630000000,
"end_time": 1621234800000,
"min_time_interval": 86400000,
"num_outputs": 10
}
Upon execution, the API returns the following:
"results" : [
{
"name" : "sum",
"result" : {
"buckets" : [
{
"start_time" : 1620630000000,
"end_time" : 1620716400000,
"overall_aggregate_value" : 65.0
},
{
"start_time" : 1620716400000,
"end_time" : 1620802800000,
"overall_aggregate_value" : 75.0,
"entities" : [
{
"key" : [
"attr0"
],
"contribution_value" : 1.0,
"base_value" : 2.0,
"new_value" : 3.0
},
{
"key" : [
"attr1"
],
"contribution_value" : 1.0,
"base_value" : 3.0,
"new_value" : 4.0
},
{
"key" : [
"attr2"
],
"contribution_value" : 1.0,
"base_value" : 4.0,
"new_value" : 5.0
},
{
"key" : [
"attr3"
],
"contribution_value" : 1.0,
"base_value" : 5.0,
"new_value" : 6.0
},
{
"key" : [
"attr4"
],
"contribution_value" : 1.0,
"base_value" : 6.0,
"new_value" : 7.0
},
{
"key" : [
"attr5"
],
"contribution_value" : 1.0,
"base_value" : 7.0,
"new_value" : 8.0
},
{
"key" : [
"attr6"
],
"contribution_value" : 1.0,
"base_value" : 8.0,
"new_value" : 9.0
},
{
"key" : [
"attr7"
],
"contribution_value" : 1.0,
"base_value" : 9.0,
"new_value" : 10.0
},
{
"key" : [
"attr8"
],
"contribution_value" : 1.0,
"base_value" : 10.0,
"new_value" : 11.0
},
{
"key" : [
"attr9"
],
"contribution_value" : 1.0,
"base_value" : 11.0,
"new_value" : 12.0
}
]
},
...
]
}
}
]
}