elasticsearch-js/docs/reference/api/MlPutTrainedModelRequest.md

MlPutTrainedModelRequest [interface-MlPutTrainedModelRequest]

Name	Type	Description
body	string	({ [key: string]: any; } & { model_id?: never; defer_definition_decompression?: never; wait_for_completion?: never; compressed_definition?: never; definition?: never; description?: never; inference_config?: never; input?: never; metadata?: never; model_type?: never; model_size_bytes?: never; platform_architecture?: never; tags?: never; prefix_strings?: never; })
compressed_definition	string	The compressed (GZipped and Base64 encoded) inference definition of the model. If compressed_definition is specified, then definition cannot be specified.
defer_definition_decompression	boolean	If set to true and a compressed_definition is provided, the request defers definition decompression and skips relevant validations.
definition	MlPutTrainedModelDefinition	The inference definition for the model. If definition is specified, then compressed_definition cannot be specified.
description	string	A human-readable description of the inference trained model.
inference_config	MlInferenceConfigCreateContainer	The default configuration for inference. This can be either a regression or classification configuration. It must match the underlying definition.trained_model's target_type. For pre-packaged models such as ELSER the config is not required.
input	MlPutTrainedModelInput	The input field names for the model definition.
metadata	any	An object map that contains metadata about the model.
model_id	Id	The unique identifier of the trained model.
model_size_bytes	long	The estimated memory usage in bytes to keep the trained model in memory. This property is supported only if defer_definition_decompression is true or the model definition is not supplied.
model_type	MlTrainedModelType	The model type.
platform_architecture	string	The platform architecture (if applicable) of the trained mode. If the model only works on one platform, because it is heavily optimized for a particular processor architecture and OS combination, then this field specifies which. The format of the string must match the platform identifiers used by Elasticsearch, so one of, linux-x86_64, linux-aarch64, darwin-x86_64, darwin-aarch64, or windows-x86_64. For portable models (those that work independent of processor architecture or OS features), leave this field unset.
prefix_strings	MlTrainedModelPrefixStrings	Optional prefix strings applied at inference
querystring	{ [key: string]: any; } & { model_id?: never; defer_definition_decompression?: never; wait_for_completion?: never; compressed_definition?: never; definition?: never; description?: never; inference_config?: never; input?: never; metadata?: never; model_type?: never; model_size_bytes?: never; platform_architecture?: never; tags?: never; prefix_strings?: never; }	All values in querystring will be added to the request querystring.
tags	string[]	An array of tags to organize the model.
wait_for_completion	boolean	Whether to wait for all child operations (e.g. model download) to complete.