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Elastic Docs ›Elasticsearch Guide [8.15] ›REST APIs ›Machine learning trained model APIs

Get trained models statistics API

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Get trained models statistics API

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Retrieves usage information for trained models.

Request

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GET _ml/trained_models/_stats

GET _ml/trained_models/_all/_stats

GET _ml/trained_models/<model_id_or_deployment_id>/_stats

GET _ml/trained_models/<model_id_or_deployment_id>,<model_id_2_or_deployment_id_2>/_stats

GET _ml/trained_models/<model_id_pattern*_or_deployment_id_pattern*>,<model_id_2_or_deployment_id_2>/_stats

Prerequisites

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

Description

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

Path parameters

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<model_id_or_deployment_id>
(Optional, string) The unique identifier of the model or the deployment. If a model has multiple deployments, and the ID of one of the deployments matches the model ID, then the model ID takes precedence; the results are returned for all deployments of the model.

Query parameters

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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

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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.
cache_size
(byte value) The inference cache size (in memory outside the JVM heap) per node for the model.
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.
deployment_id
A unique identifier for the deployment of the model.
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.
model_id
(string) The unique identifier of the trained 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. The average is calculated over the lifetime of the deployment.
average_inference_time_ms_excluding_cache_hits
(double) The average time to perform inference on the trained model excluding occasions where the response comes from the cache. Cached inference calls return very quickly as the model is not evaluated, by excluding cache hits this value is an accurate measure of the average time taken to evaluate the model.
average_inference_time_ms_last_minute
(double) The average time for each inference call to complete on this node in the last minute.
error_count
(integer) The number of errors when evaluating the trained model.
inference_cache_hit_count
(integer) The total number of inference calls made against this node for this model that were served from the inference cache.
inference_cache_hit_count_last_minute
(integer) The number of inference calls made against this node for this model in the last minute that were served from the inference cache.
inference_count
(integer) The total number of inference calls made against this node for this model.
last_access
(long) The epoch time stamp of the last inference call for the model on this node.
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_allocations
(integer) The number of allocations assigned to this node.
number_of_pending_requests
(integer) The number of inference requests queued to be processed.
peak_throughput_per_minute
(integer) The peak number of requests processed in a 1 minute period.
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.
threads_per_allocation
(integer) The number of threads for each allocation during inference. This value is limited by the number of hardware threads on the node; it might therefore differ from the threads_per_allocation value in the Start trained model deployment API.
timeout_count
(integer) The number of inference requests that timed out before being processed.
throughput_last_minute
(integer) The number of requests processed in the last 1 minute.
number_of_allocations
(integer) The requested number of allocations for the trained model deployment.
peak_throughput_per_minute
(integer) The peak number of requests processed in a 1 minute period for all nodes in the deployment. This is calculated as the sum of each node’s peak_throughput_per_minute value.
priority
(string) The deployment priority.
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.
threads_per_allocation
(integer) The number of threads per allocation used by the inference process.
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

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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

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

resp = client.ml.get_trained_models_stats()
print(resp)
response = client.ml.get_trained_models_stats
puts response
const response = await client.ml.getTrainedModelsStats();
console.log(response);
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
                  }
                }
              }
            ]
          }
        }
      }
    }
  ]
}
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