R/cleanroomsml_service.R

In paws.security.identity: 'Amazon Web Services' Security, Identity, & Compliance Services

# This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common new_handlers new_service set_config merge_config
NULL

#' AWS Clean Rooms ML
#'
#' @description
#' Welcome to the *Amazon Web Services Clean Rooms ML API Reference*.
#' 
#' Amazon Web Services Clean Rooms ML provides a privacy-enhancing method
#' for two parties to identify similar users in their data without the need
#' to share their data with each other. The first party brings the training
#' data to Clean Rooms so that they can create and configure an audience
#' model (lookalike model) and associate it with a collaboration. The
#' second party then brings their seed data to Clean Rooms and generates an
#' audience (lookalike segment) that resembles the training data.
#' 
#' To learn more about Amazon Web Services Clean Rooms ML concepts,
#' procedures, and best practices, see the [Clean Rooms User
#' Guide](https://docs.aws.amazon.com/clean-rooms/latest/userguide/machine-learning.html).
#' 
#' To learn more about SQL commands, functions, and conditions supported in
#' Clean Rooms, see the [Clean Rooms SQL
#' Reference](https://docs.aws.amazon.com/clean-rooms/latest/sql-reference/sql-reference.html).
#'
#' @param
#' config
#' Optional configuration of credentials, endpoint, and/or region.
#' \itemize{
#' \item{\strong{credentials}: \itemize{
#' \item{\strong{creds}: \itemize{
#' \item{\strong{access_key_id}: AWS access key ID}
#' \item{\strong{secret_access_key}: AWS secret access key}
#' \item{\strong{session_token}: AWS temporary session token}
#' }}
#' \item{\strong{profile}: The name of a profile to use. If not given, then the default profile is used.}
#' \item{\strong{anonymous}: Set anonymous credentials.}
#' }}
#' \item{\strong{endpoint}: The complete URL to use for the constructed client.}
#' \item{\strong{region}: The AWS Region used in instantiating the client.}
#' \item{\strong{close_connection}: Immediately close all HTTP connections.}
#' \item{\strong{timeout}: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.}
#' \item{\strong{s3_force_path_style}: Set this to `true` to force the request to use path-style addressing, i.e. `http://s3.amazonaws.com/BUCKET/KEY`.}
#' \item{\strong{sts_regional_endpoint}: Set sts regional endpoint resolver to regional or legacy \url{https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html}}
#' }
#' @param
#' credentials
#' Optional credentials shorthand for the config parameter
#' \itemize{
#' \item{\strong{creds}: \itemize{
#' \item{\strong{access_key_id}: AWS access key ID}
#' \item{\strong{secret_access_key}: AWS secret access key}
#' \item{\strong{session_token}: AWS temporary session token}
#' }}
#' \item{\strong{profile}: The name of a profile to use. If not given, then the default profile is used.}
#' \item{\strong{anonymous}: Set anonymous credentials.}
#' }
#' @param
#' endpoint
#' Optional shorthand for complete URL to use for the constructed client.
#' @param
#' region
#' Optional shorthand for AWS Region used in instantiating the client.
#'
#' @section Service syntax:
#' ```
#' svc <- cleanroomsml(
#'   config = list(
#'     credentials = list(
#'       creds = list(
#'         access_key_id = "string",
#'         secret_access_key = "string",
#'         session_token = "string"
#'       ),
#'       profile = "string",
#'       anonymous = "logical"
#'     ),
#'     endpoint = "string",
#'     region = "string",
#'     close_connection = "logical",
#'     timeout = "numeric",
#'     s3_force_path_style = "logical",
#'     sts_regional_endpoint = "string"
#'   ),
#'   credentials = list(
#'     creds = list(
#'       access_key_id = "string",
#'       secret_access_key = "string",
#'       session_token = "string"
#'     ),
#'     profile = "string",
#'     anonymous = "logical"
#'   ),
#'   endpoint = "string",
#'   region = "string"
#' )
#' ```
#'
#' @examples
#' \dontrun{
#' svc <- cleanroomsml()
#' svc$cancel_trained_model(
#'   Foo = 123
#' )
#' }
#'
#' @section Operations:
#' \tabular{ll}{
#'  \link[=cleanroomsml_cancel_trained_model]{cancel_trained_model} \tab Submits a request to cancel the trained model job\cr
#'  \link[=cleanroomsml_cancel_trained_model_inference_job]{cancel_trained_model_inference_job} \tab Submits a request to cancel a trained model inference job\cr
#'  \link[=cleanroomsml_create_audience_model]{create_audience_model} \tab Defines the information necessary to create an audience model\cr
#'  \link[=cleanroomsml_create_configured_audience_model]{create_configured_audience_model} \tab Defines the information necessary to create a configured audience model\cr
#'  \link[=cleanroomsml_create_configured_model_algorithm]{create_configured_model_algorithm} \tab Creates a configured model algorithm using a container image stored in an ECR repository\cr
#'  \link[=cleanroomsml_create_configured_model_algorithm_association]{create_configured_model_algorithm_association} \tab Associates a configured model algorithm to a collaboration for use by any member of the collaboration\cr
#'  \link[=cleanroomsml_create_ml_input_channel]{create_ml_input_channel} \tab Provides the information to create an ML input channel\cr
#'  \link[=cleanroomsml_create_trained_model]{create_trained_model} \tab Creates a trained model from an associated configured model algorithm using data from any member of the collaboration\cr
#'  \link[=cleanroomsml_create_training_dataset]{create_training_dataset} \tab Defines the information necessary to create a training dataset\cr
#'  \link[=cleanroomsml_delete_audience_generation_job]{delete_audience_generation_job} \tab Deletes the specified audience generation job, and removes all data associated with the job\cr
#'  \link[=cleanroomsml_delete_audience_model]{delete_audience_model} \tab Specifies an audience model that you want to delete\cr
#'  \link[=cleanroomsml_delete_configured_audience_model]{delete_configured_audience_model} \tab Deletes the specified configured audience model\cr
#'  \link[=cleanroomsml_delete_configured_audience_model_policy]{delete_configured_audience_model_policy} \tab Deletes the specified configured audience model policy\cr
#'  \link[=cleanroomsml_delete_configured_model_algorithm]{delete_configured_model_algorithm} \tab Deletes a configured model algorithm\cr
#'  \link[=cleanroomsml_delete_configured_model_algorithm_association]{delete_configured_model_algorithm_association} \tab Deletes a configured model algorithm association\cr
#'  \link[=cleanroomsml_delete_ml_configuration]{delete_ml_configuration} \tab Deletes a ML modeling configuration\cr
#'  \link[=cleanroomsml_delete_ml_input_channel_data]{delete_ml_input_channel_data} \tab Provides the information necessary to delete an ML input channel\cr
#'  \link[=cleanroomsml_delete_trained_model_output]{delete_trained_model_output} \tab Deletes the output of a trained model\cr
#'  \link[=cleanroomsml_delete_training_dataset]{delete_training_dataset} \tab Specifies a training dataset that you want to delete\cr
#'  \link[=cleanroomsml_get_audience_generation_job]{get_audience_generation_job} \tab Returns information about an audience generation job\cr
#'  \link[=cleanroomsml_get_audience_model]{get_audience_model} \tab Returns information about an audience model\cr
#'  \link[=cleanroomsml_get_collaboration_configured_model_algorithm_association]{get_collaboration_configured_model_algorithm_association} \tab Returns information about the configured model algorithm association in a collaboration\cr
#'  \link[=cleanroomsml_get_collaboration_ml_input_channel]{get_collaboration_ml_input_channel} \tab Returns information about a specific ML input channel in a collaboration\cr
#'  \link[=cleanroomsml_get_collaboration_trained_model]{get_collaboration_trained_model} \tab Returns information about a trained model in a collaboration\cr
#'  \link[=cleanroomsml_get_configured_audience_model]{get_configured_audience_model} \tab Returns information about a specified configured audience model\cr
#'  \link[=cleanroomsml_get_configured_audience_model_policy]{get_configured_audience_model_policy} \tab Returns information about a configured audience model policy\cr
#'  \link[=cleanroomsml_get_configured_model_algorithm]{get_configured_model_algorithm} \tab Returns information about a configured model algorithm\cr
#'  \link[=cleanroomsml_get_configured_model_algorithm_association]{get_configured_model_algorithm_association} \tab Returns information about a configured model algorithm association\cr
#'  \link[=cleanroomsml_get_ml_configuration]{get_ml_configuration} \tab Returns information about a specific ML configuration\cr
#'  \link[=cleanroomsml_get_ml_input_channel]{get_ml_input_channel} \tab Returns information about an ML input channel\cr
#'  \link[=cleanroomsml_get_trained_model]{get_trained_model} \tab Returns information about a trained model\cr
#'  \link[=cleanroomsml_get_trained_model_inference_job]{get_trained_model_inference_job} \tab Returns information about a trained model inference job\cr
#'  \link[=cleanroomsml_get_training_dataset]{get_training_dataset} \tab Returns information about a training dataset\cr
#'  \link[=cleanroomsml_list_audience_export_jobs]{list_audience_export_jobs} \tab Returns a list of the audience export jobs\cr
#'  \link[=cleanroomsml_list_audience_generation_jobs]{list_audience_generation_jobs} \tab Returns a list of audience generation jobs\cr
#'  \link[=cleanroomsml_list_audience_models]{list_audience_models} \tab Returns a list of audience models\cr
#'  \link[=cleanroomsml_list_collaboration_configured_model_algorithm_associations]{list_collaboration_configured_model_algorithm_associations} \tab Returns a list of the configured model algorithm associations in a collaboration\cr
#'  \link[=cleanroomsml_list_collaboration_ml_input_channels]{list_collaboration_ml_input_channels} \tab Returns a list of the ML input channels in a collaboration\cr
#'  \link[=cleanroomsml_list_collaboration_trained_model_export_jobs]{list_collaboration_trained_model_export_jobs} \tab Returns a list of the export jobs for a trained model in a collaboration\cr
#'  \link[=cleanroomsml_list_collaboration_trained_model_inference_jobs]{list_collaboration_trained_model_inference_jobs} \tab Returns a list of trained model inference jobs in a specified collaboration\cr
#'  \link[=cleanroomsml_list_collaboration_trained_models]{list_collaboration_trained_models} \tab Returns a list of the trained models in a collaboration\cr
#'  \link[=cleanroomsml_list_configured_audience_models]{list_configured_audience_models} \tab Returns a list of the configured audience models\cr
#'  \link[=cleanroomsml_list_configured_model_algorithm_associations]{list_configured_model_algorithm_associations} \tab Returns a list of configured model algorithm associations\cr
#'  \link[=cleanroomsml_list_configured_model_algorithms]{list_configured_model_algorithms} \tab Returns a list of configured model algorithms\cr
#'  \link[=cleanroomsml_list_ml_input_channels]{list_ml_input_channels} \tab Returns a list of ML input channels\cr
#'  \link[=cleanroomsml_list_tags_for_resource]{list_tags_for_resource} \tab Returns a list of tags for a provided resource\cr
#'  \link[=cleanroomsml_list_trained_model_inference_jobs]{list_trained_model_inference_jobs} \tab Returns a list of trained model inference jobs that match the request parameters\cr
#'  \link[=cleanroomsml_list_trained_models]{list_trained_models} \tab Returns a list of trained models\cr
#'  \link[=cleanroomsml_list_training_datasets]{list_training_datasets} \tab Returns a list of training datasets\cr
#'  \link[=cleanroomsml_put_configured_audience_model_policy]{put_configured_audience_model_policy} \tab Create or update the resource policy for a configured audience model\cr
#'  \link[=cleanroomsml_put_ml_configuration]{put_ml_configuration} \tab Assigns information about an ML configuration\cr
#'  \link[=cleanroomsml_start_audience_export_job]{start_audience_export_job} \tab Export an audience of a specified size after you have generated an audience\cr
#'  \link[=cleanroomsml_start_audience_generation_job]{start_audience_generation_job} \tab Information necessary to start the audience generation job\cr
#'  \link[=cleanroomsml_start_trained_model_export_job]{start_trained_model_export_job} \tab Provides the information necessary to start a trained model export job\cr
#'  \link[=cleanroomsml_start_trained_model_inference_job]{start_trained_model_inference_job} \tab Defines the information necessary to begin a trained model inference job\cr
#'  \link[=cleanroomsml_tag_resource]{tag_resource} \tab Adds metadata tags to a specified resource\cr
#'  \link[=cleanroomsml_untag_resource]{untag_resource} \tab Removes metadata tags from a specified resource\cr
#'  \link[=cleanroomsml_update_configured_audience_model]{update_configured_audience_model} \tab Provides the information necessary to update a configured audience model
#' }
#'
#' @return
#' A client for the service. You can call the service's operations using
#' syntax like `svc$operation(...)`, where `svc` is the name you've assigned
#' to the client. The available operations are listed in the
#' Operations section.
#'
#' @rdname cleanroomsml
#' @export
cleanroomsml <- function(config = list(), credentials = list(), endpoint = NULL, region = NULL) {
  config <- merge_config(
    config,
    list(
      credentials = credentials,
      endpoint = endpoint,
      region = region
    )
  )
  svc <- .cleanroomsml$operations
  svc <- set_config(svc, config)
  return(svc)
}

# Private API objects: metadata, handlers, interfaces, etc.
.cleanroomsml <- list()

.cleanroomsml$operations <- list()

.cleanroomsml$metadata <- list(
  service_name = "cleanroomsml",
  endpoints = list("^(us|eu|ap|sa|ca|me|af|il|mx)\\-\\w+\\-\\d+$" = list(endpoint = "cleanrooms-ml.{region}.amazonaws.com", global = FALSE), "^cn\\-\\w+\\-\\d+$" = list(endpoint = "cleanrooms-ml.{region}.amazonaws.com.cn", global = FALSE), "^us\\-gov\\-\\w+\\-\\d+$" = list(endpoint = "cleanrooms-ml.{region}.amazonaws.com", global = FALSE), "^us\\-iso\\-\\w+\\-\\d+$" = list(endpoint = "cleanrooms-ml.{region}.c2s.ic.gov", global = FALSE), "^us\\-isob\\-\\w+\\-\\d+$" = list(endpoint = "cleanrooms-ml.{region}.sc2s.sgov.gov", global = FALSE), "^eu\\-isoe\\-\\w+\\-\\d+$" = list(endpoint = "cleanrooms-ml.{region}.cloud.adc-e.uk", global = FALSE), "^us\\-isof\\-\\w+\\-\\d+$" = list(endpoint = "cleanrooms-ml.{region}.csp.hci.ic.gov", global = FALSE)),
  service_id = "CleanRoomsML",
  api_version = "2023-09-06",
  signing_name = "cleanrooms-ml",
  json_version = "",
  target_prefix = ""
)

.cleanroomsml$service <- function(config = list(), op = NULL) {
  handlers <- new_handlers("restjson", "v4")
  new_service(.cleanroomsml$metadata, handlers, config, op)
}