Get trained models usage info Added in 7.10.0
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.
Query parameters
-
allow_no_match 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.
If true, it returns an empty array when there are no matches and the subset of results when there are partial matches.
-
from number
Skips the specified number of models.
-
size number
Specifies the maximum number of models to obtain.
Responses
-
200 application/json
Hide response attributes Show response attributes object
-
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.
-
An array of trained model statistics, which are sorted by the model_id value in ascending order.
Hide trained_model_stats attributes Show trained_model_stats attributes object
-
deployment_stats object
Additional properties are allowed.
Hide deployment_stats attributes Show deployment_stats attributes object
-
adaptive_allocations object
Additional properties are allowed.
Hide adaptive_allocations attributes Show adaptive_allocations attributes object
-
min_number_of_allocations number
-
max_number_of_allocations number
-
allocation_status object
Additional properties are allowed.
Hide allocation_status attributes Show allocation_status attributes object
-
The current number of nodes where the model is allocated.
-
Values are
started
,starting
, orfully_allocated
. -
The desired number of nodes for model allocation.
-
cache_size number | string
-
error_count number
The sum of
error_count
for all nodes in the deployment. -
inference_count number
The sum of
inference_count
for all nodes in the deployment. -
The deployment stats for each node that currently has the model allocated. In serverless, stats are reported for a single unnamed virtual node.
Hide nodes attributes Show nodes attributes object
-
error_count number
The number of errors when evaluating the trained model.
-
inference_count number
The total number of inference calls made against this node for this model.
-
inference_cache_hit_count number
-
number_of_allocations number
The number of allocations assigned to this node.
-
number_of_pending_requests number
The number of inference requests queued to be processed.
-
rejection_execution_count number
The number of inference requests that were not processed because the queue was full.
-
Additional properties are allowed.
-
threads_per_allocation number
The number of threads used by each allocation during inference.
-
timeout_count number
The number of inference requests that timed out before being processed.
-
number_of_allocations number
The number of allocations requested.
-
Values are
normal
orlow
. -
queue_capacity number
The number of inference requests that can be queued before new requests are rejected.
-
rejected_execution_count number
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 thequeue_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 number
Time unit for milliseconds
-
state string
Values are
started
,starting
,stopping
, orfailed
. -
threads_per_allocation number
The number of threads used be each allocation during inference.
-
timeout_count number
The sum of
timeout_count
for all nodes in the deployment.
-
-
inference_stats object
Additional properties are allowed.
Hide inference_stats attributes Show inference_stats attributes object
-
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
, the cache is not being appropriately used. This can be solved by increasing the cache size or its time-to-live (TTL). Refer to general machine learning settings for the appropriate settings. -
The number of failures when using the model for inference.
-
The total number of times the model has been called for inference. This is across all inference contexts, including all pipelines.
-
The number of inference calls where all the training features for the model were missing.
-
timestamp number
Time unit for milliseconds
-
-
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 the nodes stats API.
Hide ingest attribute Show ingest attribute object
-
Additional properties are allowed.
-
-
Additional properties are allowed.
Hide model_size_stats attributes Show model_size_stats attributes object
model_size_bytes number | string Required
required_native_memory_bytes number | string Required
-
The number of ingest pipelines that currently refer to the model.
-
-
curl \
-X GET http://api.example.com/_ml/trained_models/_stats