class Aws::CleanRoomsML::Types::CreateTrainedModelRequest

@see docs.aws.amazon.com/goto/WebAPI/cleanroomsml-2023-09-06/CreateTrainedModelRequest AWS API Documentation
@return [Hash<String,String>]
of aws do not count against your tags per resource limit.
count against the limit of 50 tags. Tags with only the key prefix
not, then Clean Rooms ML considers it to be a user tag and will
this prefix. If a tag value has aws as its prefix but the key does
cannot edit or delete tag keys with this prefix. Values can have
such as a prefix for keys as it is reserved for AWS use. You
* Do not use aws:, AWS:, or any upper or lowercase combination of
* Tag keys and values are case sensitive.
characters: + - = . _ : / @.
numbers, and spaces representable in UTF-8, and the following
allowed characters. Generally allowed characters are: letters,
resources, remember that other services may have restrictions on
* If your tagging schema is used across multiple services and
* Maximum value length - 256 Unicode characters in UTF-8.
* Maximum key length - 128 Unicode characters in UTF-8.
can have only one value.
* For each resource, each tag key must be unique, and each tag key
* Maximum number of tags per resource - 50.
The following basic restrictions apply to tags:
optional value, both of which you define.
categorize and organize them. Each tag consists of a key and an
The optional metadata that you apply to the resource to help you
@!attribute [rw] tags
@return [String]
the associated data.
encrypt and decrypt customer-owned data in the trained ML model and
The Amazon Resource Name (ARN) of the KMS key. This key is used to
@!attribute [rw] kms_key_arn
@return [String]
The description of the trained model.
@!attribute [rw] description
@return [String]
algorithms.
using named pipes, which can improve performance for certain
* ‘Pipe` - The training data is streamed to the training algorithm
datasets.
to the training algorithm, providing faster access for large
* `FastFile` - The training data is streamed directly from Amazon S3
and made available as files.
* `File` - The training data is downloaded to the training instance
algorithm. Valid values are:
determines how the training data is made available to the training
The input mode for accessing the training data. This parameter
@!attribute [rw] training_input_mode
@return [Array<Types::ModelTrainingDataChannel>]
and `incrementalTrainingDataChannels`).
Limit: Maximum of 20 channels total (including both `dataChannels`
model request.
Defines the data channels that are used as input for the trained
@!attribute [rw] data_channels
@return [Array<Types::IncrementalTrainingDataChannel>]
`incrementalTrainingDataChannels` and `dataChannels`).
Limit: Maximum of 20 channels total (including both
trained model and its version.
incremental training data channel that references a previously
updates without retraining from scratch. You can specify up to one
Incremental training allows you to create a new trained model with
model.
Specifies the incremental training data channels for the trained
@!attribute [rw] incremental_training_data_channels
@return [Types::StoppingCondition]
The criteria that is used to stop model training.
@!attribute [rw] stopping_condition
@return [Types::ResourceConfig]
model.
Information about the EC2 resources that are used to train this
@!attribute [rw] resource_config
@return [Hash<String,String>]
The environment variables to set in the Docker container.
@!attribute [rw] environment
@return [Hash<String,String>]
process.
model. You set hyperparameters before you start the learning
Algorithm-specific parameters that influence the quality of the
@!attribute [rw] hyperparameters
@return [String]
The associated configured model algorithm used to train this model.
@!attribute [rw] configured_model_algorithm_association_arn
@return [String]
The name of the trained model.
@!attribute [rw] name
@return [String]
The membership ID of the member that is creating the trained model.
@!attribute [rw] membership_identifier