elasticsearch/docs/reference/ml/trained-models/apis/get-trained-models-stats.asciidoc

[role="xpack"]
[[get-trained-models-stats]]
= Get trained models statistics API
[subs="attributes"]
++++
<titleabbrev>Get trained models stats</titleabbrev>
++++

Retrieves usage information for trained models.


[[ml-get-trained-models-stats-request]]
== {api-request-title}

`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`


[[ml-get-trained-models-stats-prereq]]
== {api-prereq-title}

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


[[ml-get-trained-models-stats-desc]]
== {api-description-title}

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.


[[ml-get-trained-models-stats-path-params]]
== {api-path-parms-title}

`<model_id>`::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id-or-alias]


[[ml-get-trained-models-stats-query-params]]
== {api-query-parms-title}

`allow_no_match`::
(Optional, Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-match-models]

`from`::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=from-models]

`size`::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=size-models]

[role="child_attributes"]
[[ml-get-trained-models-stats-results]]
== {api-response-body-title}

`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
[%collapsible%open]
====
`deployment_stats`:::
(list)
A collection of deployment stats if one of the provided `model_id` values
is deployed
+
.Properties of deployment stats
[%collapsible%open]
=====
`allocation_status`:::
(object)
The detailed allocation status given the deployment configuration.
+
.Properties of allocation stats
[%collapsible%open]
======
`allocation_count`:::
(integer)
The current number of nodes where the model is allocated.

`cache_size`:::
(<<byte-units,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.
======

`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)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]

`nodes`:::
(array of objects)
The deployment stats for each node that currently has the model allocated.
+
.Properties of node stats
[%collapsible%open]
======
`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_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_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
[%collapsible%open]
========
`attributes`:::
(object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=node-attributes]

`ephemeral_id`:::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=node-ephemeral-id]

`id`:::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=node-id]

`name`:::
(string) The node name.

`transport_address`:::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=node-transport-address]
========

`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
[%collapsible%open]
========
`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.

`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
[%collapsible%open]
=====

`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-ml-settings>> for the appropriate
settings.

`failure_count`:::
(integer)
The number of failures when using the model for inference.

`timestamp`:::
(<<time-units,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 <<cluster-nodes-stats>>.

`model_id`:::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]

`model_size_stats`:::
(object)
A collection of model size stats fields.
+
.Properties of model size stats
[%collapsible%open]
=====

`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.
====

[[ml-get-trained-models-stats-response-codes]]
== {api-response-codes-title}

`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.

[[ml-get-trained-models-stats-example]]
== {api-examples-title}

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

[source,console]
--------------------------------------------------
GET _ml/trained_models/_stats
--------------------------------------------------
// TEST[skip:TBD]


The API returns the following results:

[source,console-result]
----
{
  "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
                  }
                }
              }
            ]
          }
        }
      }
    }
  ]
}
----
// NOTCONSOLE