Get trained models statistics API

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Get trained models statistics API

Retrieves usage information for trained models.

Request

GET _ml/trained_models/_stats

GET _ml/trained_models/_all/_stats

GET _ml/trained_models/<model_id>/_stats

GET _ml/trained_models/<model_id>,<model_id_2>/_stats

GET _ml/trained_models/<model_id_pattern*>,<model_id_2>/_stats

Prerequisites

Requires the monitor_ml cluster privilege. This privilege is included in the machine_learning_user built-in role.

Description

You can get usage information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.

Path parameters

<model_id>: (Optional, string) The unique identifier of the trained model or a model alias.

Query parameters

allow_no_match

(Optional, Boolean) Specifies what to do when the request:

Contains wildcard expressions and there are no models that match.
Contains the _all string or no identifiers and there are no matches.
Contains wildcard expressions and there are only partial matches.

The default value is true, which returns an empty array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

from

(Optional, integer) Skips the specified number of models. The default value is 0.

size

(Optional, integer) Specifies the maximum number of models to obtain. The default value is 100.

Response body

count

(integer) The total number of trained model statistics that matched the requested ID patterns. Could be higher than the number of items in the trained_model_stats array as the size of the array is restricted by the supplied size parameter.

trained_model_stats

(array) An array of trained model statistics, which are sorted by the model_id value in ascending order.

Properties of trained model stats

deployment_stats

(list) A collection of deployment stats if one of the provided model_id values is deployed

Properties of deployment stats

allocation_status

(object) The detailed allocation status given the deployment configuration.

Properties of allocation stats

allocation_count

(integer) The current number of nodes where the model is allocated.

state

(string) The detailed allocation state related to the nodes.

starting: Allocations are being attempted but no node currently has the model allocated.
started: At least one node has the model allocated.
fully_allocated: The deployment is fully allocated and satisfies the target_allocation_count.

target_allocation_count

(integer) The desired number of nodes for model allocation.

error_count

(integer) The sum of error_count for all nodes in the deployment.

inference_count

(integer) The sum of inference_count for all nodes in the deployment.

inference_threads

(integer) The number of threads used by the inference process.

model_id

(string) The unique identifier of the trained model.

model_threads

(integer) The number of threads used when sending inference requests to the model.

nodes

(array of objects) The deployment stats for each node that currently has the model allocated.

Properties of node stats

average_inference_time_ms

(double) The average time for each inference call to complete on this node.

error_count

(integer) The number of errors when evaluating the trained model.

inference_count

(integer) The total number of inference calls made against this node for this model.

inference_threads

(integer) The number of threads used by the inference process. This value is limited by the number of hardware threads on the node; it might therefore differ from the inference_threads value in the Start trained model deployment API.

last_access

(long) The epoch time stamp of the last inference call for the model on this node.

model_threads

(integer) The number of threads used when sending inference requests to the model. This value is limited by the number of hardware threads on the node; it might therefore differ from the model_threads value in the Start trained model deployment API.

node

(object) Information pertaining to the node.

Properties of node

attributes: (object) Lists node attributes such as ml.machine_memory or ml.max_open_jobs settings.
ephemeral_id: (string) The ephemeral ID of the node.
id: (string) The unique identifier of the node.
name: (string) The node name.
transport_address: (string) The host and port where transport HTTP connections are accepted.

number_of_pending_requests

(integer) The number of inference requests queued to be processed.

routing_state

(object) The current routing state and reason for the current routing state for this allocation.

Properties of routing_state

reason: (string) The reason for the current state. Usually only populated when the routing_state is failed.
routing_state: (string) The current routing state.

starting: The model is attempting to allocate on this model, inference calls are not yet accepted.
started: The model is allocated and ready to accept inference requests.
stopping: The model is being deallocated from this node.
stopped: The model is fully deallocated from this node.
failed: The allocation attempt failed, see reason field for the potential cause.

rejected_execution_count

(integer) The number of inference requests that were not processed because the queue was full.

start_time

(long) The epoch timestamp when the allocation started.

timeout_count

(integer) The number of inference requests that timed out before being processed.

rejected_execution_count

(integer) The sum of rejected_execution_count for all nodes in the deployment. Individual nodes reject an inference request if the inference queue is full. The queue size is controlled by the queue_capacity setting in the Start trained model deployment API.

reason

(string) The reason for the current deployment state. Usually only populated when the model is not deployed to a node.

start_time

(long) The epoch timestamp when the deployment started.

state

(string) The overall state of the deployment. The values may be:

starting: The deployment has recently started but is not yet usable as the model is not allocated on any nodes.
started: The deployment is usable as at least one node has the model allocated.
stopping: The deployment is preparing to stop and deallocate the model from the relevant nodes.

timeout_count

(integer) The sum of timeout_count for all nodes in the deployment.

queue_capacity

(integer) The number of inference requests that may be queued before new requests are rejected.

inference_stats

(object) A collection of inference stats fields.

Properties of inference stats

missing_all_fields_count: (integer) The number of inference calls where all the training features for the model were missing.
inference_count: (integer) The total number of times the model has been called for inference. This is across all inference contexts, including all pipelines.
cache_miss_count: (integer) The number of times the model was loaded for inference and was not retrieved from the cache. If this number is close to the inference_count, then the cache is not being appropriately used. This can be solved by increasing the cache size or its time-to-live (TTL). See General machine learning settings for the appropriate settings.
failure_count: (integer) The number of failures when using the model for inference.
timestamp: (time units) The time when the statistics were last updated.

ingest

(object) A collection of ingest stats for the model across all nodes. The values are summations of the individual node statistics. The format matches the ingest section in Nodes stats.

model_id

(string) The unique identifier of the trained model.

model_size_stats

(object) A collection of model size stats fields.

Properties of model size stats

model_size_bytes: (integer) The size of the model in bytes.
required_native_memory_bytes: (integer) The amount of memory required to load the model in bytes.

pipeline_count

(integer) The number of ingest pipelines that currently refer to the model.

Response codes

404 (Missing resources): If allow_no_match is false, this code indicates that there are no resources that match the request or only partial matches for the request.

Examples

The following example gets usage information for all the trained models:

GET _ml/trained_models/_stats

The API returns the following results:

{
  "count": 2,
  "trained_model_stats": [
    {
      "model_id": "flight-delay-prediction-1574775339910",
      "pipeline_count": 0,
      "inference_stats": {
        "failure_count": 0,
        "inference_count": 4,
        "cache_miss_count": 3,
        "missing_all_fields_count": 0,
        "timestamp": 1592399986979
      }
    },
    {
      "model_id": "regression-job-one-1574775307356",
      "pipeline_count": 1,
      "inference_stats": {
        "failure_count": 0,
        "inference_count": 178,
        "cache_miss_count": 3,
        "missing_all_fields_count": 0,
        "timestamp": 1592399986979
      },
      "ingest": {
        "total": {
          "count": 178,
          "time_in_millis": 8,
          "current": 0,
          "failed": 0
        },
        "pipelines": {
          "flight-delay": {
            "count": 178,
            "time_in_millis": 8,
            "current": 0,
            "failed": 0,
            "processors": [
              {
                "inference": {
                  "type": "inference",
                  "stats": {
                    "count": 178,
                    "time_in_millis": 7,
                    "current": 0,
                    "failed": 0
                  }
                }
              }
            ]
          }
        }
      }
    }
  ]
}

« Get trained models API Infer trained model deployment API »

Get trained models statistics API

Request

Prerequisites

Description

Path parameters

Query parameters

Response body

Response codes

Examples

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