CleanRoomsML / Client / create_trained_model

create_trained_model#

CleanRoomsML.Client.create_trained_model(**kwargs)#

Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.

Request Syntax

response = client.create_trained_model(
    membershipIdentifier='string',
    name='string',
    configuredModelAlgorithmAssociationArn='string',
    hyperparameters={
        'string': 'string'
    },
    environment={
        'string': 'string'
    },
    resourceConfig={
        'instanceCount': 123,
        'instanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
        'volumeSizeInGB': 123
    },
    stoppingCondition={
        'maxRuntimeInSeconds': 123
    },
    dataChannels=[
        {
            'mlInputChannelArn': 'string',
            'channelName': 'string'
        },
    ],
    description='string',
    kmsKeyArn='string',
    tags={
        'string': 'string'
    }
)

Parameters:

membershipIdentifier (string) –
[REQUIRED]

The membership ID of the member that is creating the trained model.
name (string) –
[REQUIRED]

The name of the trained model.
configuredModelAlgorithmAssociationArn (string) –
[REQUIRED]

The associated configured model algorithm used to train this model.
hyperparameters (dict) –
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.
- (string) –
  - (string) –
environment (dict) –
The environment variables to set in the Docker container.
- (string) –
  - (string) –
resourceConfig (dict) –
[REQUIRED]

Information about the EC2 resources that are used to train this model.
- instanceCount (integer) –
  
  The number of resources that are used to train the model.
- instanceType (string) – [REQUIRED]
  
  The instance type that is used to train the model.
- volumeSizeInGB (integer) – [REQUIRED]
  
  The maximum size of the instance that is used to train the model.
stoppingCondition (dict) –
The criteria that is used to stop model training.
- maxRuntimeInSeconds (integer) –
  
  The maximum amount of time, in seconds, that model training can run before it is terminated.
dataChannels (list) –
[REQUIRED]

Defines the data channels that are used as input for the trained model request.
- (dict) –
  
  Information about the model training data channel. A training data channel is a named data source that the training algorithms can consume.
  - mlInputChannelArn (string) – [REQUIRED]
    
    The Amazon Resource Name (ARN) of the ML input channel for this model training data channel.
  - channelName (string) – [REQUIRED]
    
    The name of the training data channel.
description (string) – The description of the trained model.
kmsKeyArn (string) – The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the trained ML model and the associated data.
tags (dict) –
The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:
- Maximum number of tags per resource - 50.
- For each resource, each tag key must be unique, and each tag key can have only one value.
- Maximum key length - 128 Unicode characters in UTF-8.
- Maximum value length - 256 Unicode characters in UTF-8.
- If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
- Tag keys and values are case sensitive.
- Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
- (string) –
  - (string) –

Return type:

dict

Returns:

Response Syntax

{
    'trainedModelArn': 'string'
}

Response Structure

(dict) –
- trainedModelArn (string) –
  
  The Amazon Resource Name (ARN) of the trained model.

Exceptions

CleanRoomsML.Client.exceptions.ConflictException
CleanRoomsML.Client.exceptions.ValidationException
CleanRoomsML.Client.exceptions.AccessDeniedException
CleanRoomsML.Client.exceptions.ResourceNotFoundException
CleanRoomsML.Client.exceptions.ServiceQuotaExceededException