[ML] add deployment_stats to trained model stats

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

@@ -26,7 +26,7 @@ Retrieves usage information for trained models.
 [[ml-get-trained-models-stats-prereq]]
 == {api-prereq-title}
 
-Requires the `monitor_ml` cluster privilege. This privilege is included in the 
+Requires the `monitor_ml` cluster privilege. This privilege is included in the
 `machine_learning_user` built-in role.
 
 
@@ -86,6 +86,131 @@ include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
 (integer)
 The number of ingest pipelines that currently refer to the model.
 
+`deployment_stats`:::
+(list)
+A collection of deployment stats if one of the provided `model_id` values
+is deployed
++
+.Properties of deployment stats
+[%collapsible%open]
+=====
+`model_id`:::
+(string)
+include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
+
+`model_size`:::
+(<<byte-units,byte value>>)
+The size of the loaded model in bytes.
+
+`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 un-allocate the model from the relevant nodes.
+--
+
+`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.
+
+`target_allocation_count`:::
+(integer)
+The desired number of nodes for model allocation.
+
+`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`.
+--
+======
+
+`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.
+
+`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]
+========
+
+`routing_state`:::
+(object)
+The current routing state and reason for the current routing state for this allocation.
++
+--
+* `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 de-allocated from this node.
+* `stopped`: The model is fully de-allocated from this node.
+* `failed`: The allocation attempt failed, see `reason` field for the potential cause.
+--
+
+`reason`:::
+(string)
+The reason for the current state. Usually only populated when the `routing_state` is `failed`.
+
+`start_time`:::
+(long)
+The epoch timestamp when the allocation started.
+
+======
+=====
+
 `inference_stats`:::
 (object)
 A collection of inference stats fields.

docs/reference/ml/df-analytics/apis/index.asciidoc

@@ -19,7 +19,6 @@ include::explain-dfanalytics.asciidoc[leveloffset=+2]
 include::get-dfanalytics.asciidoc[leveloffset=+2]
 include::get-dfanalytics-stats.asciidoc[leveloffset=+2]
 include::get-trained-models.asciidoc[leveloffset=+2]
-include::get-trained-model-deployment-stats.asciidoc[leveloffset=+2]
 include::get-trained-models-stats.asciidoc[leveloffset=+2]
 //INFER
 include::infer-trained-model-deployment.asciidoc[leveloffset=+2]

docs/reference/ml/df-analytics/apis/ml-df-analytics-apis.asciidoc

@@ -25,7 +25,6 @@ You can use the following APIs to perform {infer} operations:
 * <<delete-trained-models-aliases>>
 * <<get-trained-models>>
 * <<get-trained-models-stats>>
-* <<get-trained-model-deployment-stats>>
 
 You can deploy a trained model to make predictions in an ingest pipeline or in
 an aggregation. Refer to the following documentation to learn more: