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

Interface MlStartTrainedModelDeploymentRequest

Name	Type	Description
adaptive_allocations	MlAdaptiveAllocationsSettings	Adaptive allocations configuration. When enabled, the number of allocations is set based on the current load. If adaptive_allocations is enabled, do not set the number of allocations manually.
body	string	({ [key: string]: any; } & { model_id?: never; cache_size?: never; deployment_id?: never; number_of_allocations?: never; priority?: never; queue_capacity?: never; threads_per_allocation?: never; timeout?: never; wait_for?: never; adaptive_allocations?: never; })
cache_size	ByteSize	The inference cache size (in memory outside the JVM heap) per node for the model. The default value is the same size as the model_size_bytes. To disable the cache, 0b can be provided.
deployment_id	string	A unique identifier for the deployment of the model.
model_id	Id	The unique identifier of the trained model. Currently, only PyTorch models are supported.
number_of_allocations	integer	The number of model allocations on each node where the model is deployed. All allocations on a node share the same copy of the model in memory but use a separate set of threads to evaluate the model. Increasing this value generally increases the throughput. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads. If adaptive_allocations is enabled, do not set this value, because it’s automatically set.
priority	MlTrainingPriority	The deployment priority.
querystring	{ [key: string]: any; } & { model_id?: never; cache_size?: never; deployment_id?: never; number_of_allocations?: never; priority?: never; queue_capacity?: never; threads_per_allocation?: never; timeout?: never; wait_for?: never; adaptive_allocations?: never; }	All values in querystring will be added to the request querystring.
queue_capacity	integer	Specifies the number of inference requests that are allowed in the queue. After the number of requests exceeds this value, new requests are rejected with a 429 error.
threads_per_allocation	integer	Sets the number of threads used by each model allocation during inference. This generally increases the inference speed. The inference process is a compute-bound process; any number greater than the number of available hardware threads on the machine does not increase the inference speed. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads.
timeout	Duration	Specifies the amount of time to wait for the model to deploy.
wait_for	MlDeploymentAllocationState	Specifies the allocation status to wait for before returning.