Get trained models usage info | Elasticsearch API documentation (original) (raw)

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Path parameters

• model_id
  string | array[string] Required
  The unique identifier of the trained model or a model alias. It can be a comma-separated list or a wildcard expression.

Query parameters

• 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.
• Skips the specified number of models.
• 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
    * Hide deployment_stats attributes Show deployment_stats attributes object
    * Hide adaptive_allocations attributes Show adaptive_allocations attributes object
    * If true, adaptive_allocations is enabled
    * Specifies the minimum number of allocations to scale to. If set, it must be greater than or equal to 0. If not defined, the deployment scales to 0.
    * Specifies the maximum number of allocations to scale to. If set, it must be greater than or equal to min_number_of_allocations.
    * Hide allocation_status attributes Show allocation_status attributes object
    * The current number of nodes where the model is allocated.
    * Values are started, starting, or fully_allocated.
    * The desired number of nodes for model allocation.
    * The sum of error_count for all nodes in the deployment.
    * The sum of inference_count for all nodes in the deployment.
    * nodes
    array[object] Required
    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
    * The number of errors when evaluating the trained model.
    * The total number of inference calls made against this node for this model.
    * The number of allocations assigned to this node.
    * The number of inference requests queued to be processed.
    * The number of inference requests that were not processed because the queue was full.
    * The number of threads used by each allocation during inference.
    * The number of inference requests that timed out before being processed.
    * The number of allocations requested.
    * Values are normal or low.
    * The number of inference requests that can be queued before new requests are rejected.
    * 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.
    * The reason for the current deployment state. Usually only populated when the model is not deployed to a node.
    * Time unit for milliseconds
    * Values are started, starting, stopping, or failed.
    * The number of threads used be each allocation during inference.
    * The sum of timeout_count for all nodes in the deployment.
    * 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.
    * Time unit for milliseconds
    * 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
    * *
    object Additional properties
    * Hide model_size_stats attributes Show model_size_stats attributes object
    * The number of ingest pipelines that currently refer to the model.