2026/05/21 - AWS Clean Rooms ML - 15 updated api methods
Changes Collaboration creators can update payment configurations without recreating the collaboration. When multiple payer candidates are configured for a cost type, analysis runners can specify the actual payer at submission time, providing granular control over billing.
{'payerConfiguration': {'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'}}
Provides the information to create an ML input channel. An ML input channel is the result of a query that can be used for ML modeling.
See also: AWS API Documentation
Request Syntax
client.create_ml_input_channel(
membershipIdentifier='string',
configuredModelAlgorithmAssociations=[
'string',
],
inputChannel={
'dataSource': {
'protectedQueryInputParameters': {
'sqlParameters': {
'queryString': 'string',
'analysisTemplateArn': 'string',
'parameters': {
'string': 'string'
}
},
'computeConfiguration': {
'worker': {
'type': 'CR.1X'|'CR.4X',
'number': 123,
'properties': {
'spark': {
'string': 'string'
}
}
}
},
'resultFormat': 'CSV'|'PARQUET'
}
},
'roleArn': 'string'
},
name='string',
retentionInDays=123,
description='string',
kmsKeyArn='string',
tags={
'string': 'string'
},
payerConfiguration={
'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'
}
)
string
[REQUIRED]
The membership ID of the member that is creating the ML input channel.
list
[REQUIRED]
The associated configured model algorithms that are necessary to create this ML input channel.
(string) --
dict
[REQUIRED]
The input data that is used to create this ML input channel.
dataSource (dict) -- [REQUIRED]
The data source that is used to create the ML input channel.
protectedQueryInputParameters (dict) --
Provides information necessary to perform the protected query.
sqlParameters (dict) -- [REQUIRED]
The parameters for the SQL type Protected Query.
queryString (string) --
The query string to be submitted.
analysisTemplateArn (string) --
The Amazon Resource Name (ARN) associated with the analysis template within a collaboration.
parameters (dict) --
The protected query SQL parameters.
(string) --
(string) --
computeConfiguration (dict) --
Provides configuration information for the workers that will perform the protected query.
worker (dict) --
The worker instances that will perform the compute work.
type (string) --
The instance type of the compute workers that are used.
number (integer) --
The number of compute workers that are used.
properties (dict) --
The configuration properties for the worker compute environment. These properties allow you to customize the compute settings for your Clean Rooms workloads.
spark (dict) --
The Spark configuration properties for SQL workloads. This map contains key-value pairs that configure Apache Spark settings to optimize performance for your data processing jobs. You can specify up to 50 Spark properties, with each key being 1-200 characters and each value being 0-500 characters. These properties allow you to adjust compute capacity for large datasets and complex workloads.
(string) --
(string) --
resultFormat (string) --
The format in which the query results should be returned. If not specified, defaults to CSV.
roleArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the role used to run the query specified in the dataSource field of the input channel.
Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an AccessDeniedException error.
string
[REQUIRED]
The name of the ML input channel.
integer
[REQUIRED]
The number of days that the data in the ML input channel is retained.
string
The description of the ML input channel.
string
The Amazon Resource Name (ARN) of the KMS key that is used to access the input channel.
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) --
dict
The payer configuration for the ML input channel. Determines which member account pays for compute and synthetic data costs.
computePayerAccountId (string) --
The account ID of the member that is responsible for paying compute costs.
syntheticDataPayerAccountId (string) --
The account ID of the member that is responsible for paying synthetic data generation costs.
dict
Response Syntax
{
'mlInputChannelArn': 'string'
}
Response Structure
(dict) --
mlInputChannelArn (string) --
The Amazon Resource Name (ARN) of the ML input channel.
{'mlModelTrainingPayerAccountId': 'string'}
Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.
See also: AWS API Documentation
Request Syntax
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.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'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5en.48xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge',
'volumeSizeInGB': 123
},
stoppingCondition={
'maxRuntimeInSeconds': 123
},
incrementalTrainingDataChannels=[
{
'trainedModelArn': 'string',
'versionIdentifier': 'string',
'channelName': 'string'
},
],
dataChannels=[
{
'mlInputChannelArn': 'string',
'channelName': 'string',
's3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key'
},
],
trainingInputMode='File'|'FastFile'|'Pipe',
description='string',
kmsKeyArn='string',
tags={
'string': 'string'
},
mlModelTrainingPayerAccountId='string'
)
string
[REQUIRED]
The membership ID of the member that is creating the trained model.
string
[REQUIRED]
The name of the trained model.
string
[REQUIRED]
The associated configured model algorithm used to train this model.
dict
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.
(string) --
(string) --
dict
The environment variables to set in the Docker container.
(string) --
(string) --
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 volume size of the instance that is used to train the model. Please see EC2 volume limit for volume size limitations on different instance types.
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.
list
Specifies the incremental training data channels for the trained model.
Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.
Limit: Maximum of 20 channels total (including both incrementalTrainingDataChannels and dataChannels).
(dict) --
Defines an incremental training data channel that references a previously trained model. Incremental training allows you to update an existing trained model with new data, building upon the knowledge from a base model rather than training from scratch. This can significantly reduce training time and computational costs while improving model performance with additional data.
trainedModelArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the base trained model to use for incremental training. This model serves as the starting point for the incremental training process.
versionIdentifier (string) --
The version identifier of the base trained model to use for incremental training. If not specified, the latest version of the trained model is used.
channelName (string) -- [REQUIRED]
The name of the incremental training data channel. This name is used to identify the channel during the training process and must be unique within the training job.
list
[REQUIRED]
Defines the data channels that are used as input for the trained model request.
Limit: Maximum of 20 channels total (including both dataChannels and incrementalTrainingDataChannels).
(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.
s3DataDistributionType (string) --
Specifies how the training data stored in Amazon S3 should be distributed to training instances. This parameter controls the data distribution strategy for the training job:
FullyReplicated - The entire dataset is replicated on each training instance. This is suitable for smaller datasets and algorithms that require access to the complete dataset.
ShardedByS3Key - The dataset is distributed across training instances based on Amazon S3 key names. This is suitable for larger datasets and distributed training scenarios where each instance processes a subset of the data.
string
The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:
File - The training data is downloaded to the training instance and made available as files.
FastFile - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.
Pipe - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.
string
The description of the trained model.
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.
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) --
string
The account ID of the member that is responsible for paying for model training costs.
dict
Response Syntax
{
'trainedModelArn': 'string',
'versionIdentifier': 'string'
}
Response Structure
(dict) --
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model.
versionIdentifier (string) --
The unique version identifier assigned to the newly created trained model. This identifier can be used to reference this specific version of the trained model in subsequent operations such as inference jobs or incremental training.
The initial version identifier for the base version of the trained model is "NULL".
{'payerConfiguration': {'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'}}
Returns information about a specific ML input channel in a collaboration.
See also: AWS API Documentation
Request Syntax
client.get_collaboration_ml_input_channel(
mlInputChannelArn='string',
collaborationIdentifier='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the ML input channel that you want to get.
string
[REQUIRED]
The collaboration ID of the collaboration that contains the ML input channel that you want to get.
dict
Response Syntax
{
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'mlInputChannelArn': 'string',
'name': 'string',
'configuredModelAlgorithmAssociations': [
'string',
],
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE',
'statusDetails': {
'statusCode': 'string',
'message': 'string'
},
'retentionInDays': 123,
'numberOfRecords': 123,
'privacyBudgets': {
'accessBudgets': [
{
'resourceArn': 'string',
'details': [
{
'startTime': datetime(2015, 1, 1),
'endTime': datetime(2015, 1, 1),
'remainingBudget': 123,
'budget': 123,
'budgetType': 'CALENDAR_DAY'|'CALENDAR_MONTH'|'CALENDAR_WEEK'|'LIFETIME',
'autoRefresh': 'ENABLED'|'DISABLED'
},
],
'aggregateRemainingBudget': 123
},
]
},
'description': 'string',
'syntheticDataConfiguration': {
'syntheticDataParameters': {
'epsilon': 123.0,
'maxMembershipInferenceAttackScore': 123.0,
'columnClassification': {
'columnMapping': [
{
'columnName': 'string',
'columnType': 'CATEGORICAL'|'NUMERICAL',
'isPredictiveValue': True|False
},
]
}
},
'syntheticDataEvaluationScores': {
'dataPrivacyScores': {
'membershipInferenceAttackScores': [
{
'attackVersion': 'DISTANCE_TO_CLOSEST_RECORD_V1',
'score': 123.0
},
]
}
}
},
'payerConfiguration': {
'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'
},
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'creatorAccountId': 'string'
}
Response Structure
(dict) --
membershipIdentifier (string) --
The membership ID of the membership that contains the ML input channel.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the ML input channel.
mlInputChannelArn (string) --
The Amazon Resource Name (ARN) of the ML input channel.
name (string) --
The name of the ML input channel.
configuredModelAlgorithmAssociations (list) --
The configured model algorithm associations that were used to create the ML input channel.
(string) --
status (string) --
The status of the ML input channel.
statusDetails (dict) --
Details about the status of a resource.
statusCode (string) --
The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions.
message (string) --
The error message that was returned. The message is intended for human consumption and can change at any time. Use the statusCode for programmatic error handling.
retentionInDays (integer) --
The number of days to retain the data for the ML input channel.
numberOfRecords (integer) --
The number of records in the ML input channel.
privacyBudgets (dict) --
Returns the privacy budgets that control access to this Clean Rooms ML input channel. Use these budgets to monitor and limit resource consumption over specified time periods.
accessBudgets (list) --
A list of access budgets that apply to resources associated with this Clean Rooms ML input channel.
(dict) --
An access budget that defines consumption limits for a specific resource within defined time periods.
resourceArn (string) --
The Amazon Resource Name (ARN) of the resource that this access budget applies to.
details (list) --
A list of budget details for this resource. Contains active budget periods that apply to the resource.
(dict) --
The detailed information for a specific budget period, including time boundaries and budget amounts.
startTime (datetime) --
The start time of this budget period.
endTime (datetime) --
The end time of this budget period. If not specified, the budget period continues indefinitely.
remainingBudget (integer) --
The amount of budget remaining in this period.
budget (integer) --
The total budget amount allocated for this period.
budgetType (string) --
The type of budget period. Calendar-based types reset automatically at regular intervals, while LIFETIME budgets never reset.
autoRefresh (string) --
Specifies whether this budget automatically refreshes when the current period ends.
aggregateRemainingBudget (integer) --
The total remaining budget across all active budget periods for this resource.
description (string) --
The description of the ML input channel.
syntheticDataConfiguration (dict) --
The synthetic data configuration for this ML input channel, including parameters for generating privacy-preserving synthetic data and evaluation scores for measuring the privacy of the generated data.
syntheticDataParameters (dict) --
The parameters that control how synthetic data is generated, including privacy settings, column classifications, and other configuration options that affect the data synthesis process.
epsilon (float) --
The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.
maxMembershipInferenceAttackScore (float) --
The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.
columnClassification (dict) --
Classification details for data columns that specify how each column should be treated during synthetic data generation.
columnMapping (list) --
A mapping that defines the classification of data columns for synthetic data generation and specifies how each column should be handled during the privacy-preserving data synthesis process.
(dict) --
Properties that define how a specific data column should be handled during synthetic data generation, including its name, type, and role in predictive modeling.
columnName (string) --
The name of the data column as it appears in the dataset.
columnType (string) --
The data type of the column, which determines how the synthetic data generation algorithm processes and synthesizes values for this column.
isPredictiveValue (boolean) --
Indicates if this column contains predictive values that should be treated as target variables in machine learning models. This affects how the synthetic data generation preserves statistical relationships.
syntheticDataEvaluationScores (dict) --
Evaluation scores that assess the quality and privacy characteristics of the generated synthetic data, providing metrics on data utility and privacy preservation.
dataPrivacyScores (dict) --
Privacy-specific evaluation scores that measure how well the synthetic data protects individual privacy, including assessments of potential privacy risks such as membership inference attacks.
membershipInferenceAttackScores (list) --
Scores that evaluate the vulnerability of the synthetic data to membership inference attacks, which attempt to determine whether a specific individual was a member of the original dataset.
(dict) --
A score that measures the vulnerability of synthetic data to membership inference attacks and provides both the numerical score and the version of the attack methodology used for evaluation.
attackVersion (string) --
The version of the membership inference attack, which consists of the attack type and its version number, used to generate this privacy score.
score (float) --
The numerical score representing the vulnerability to membership inference attacks.
payerConfiguration (dict) --
The payer configuration for the ML input channel.
computePayerAccountId (string) --
The account ID of the member that is responsible for paying compute costs.
syntheticDataPayerAccountId (string) --
The account ID of the member that is responsible for paying synthetic data generation costs.
createTime (datetime) --
The time at which the ML input channel was created.
updateTime (datetime) --
The most recent time at which the ML input channel was updated.
creatorAccountId (string) --
The account ID of the member who created the ML input channel.
{'mlModelTrainingPayerAccountId': 'string'}
Returns information about a trained model in a collaboration.
See also: AWS API Documentation
Request Syntax
client.get_collaboration_trained_model(
trainedModelArn='string',
collaborationIdentifier='string',
versionIdentifier='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the trained model that you want to return information about.
string
[REQUIRED]
The collaboration ID that contains the trained model that you want to return information about.
string
The version identifier of the trained model to retrieve. If not specified, the operation returns information about the latest version of the trained model.
dict
Response Syntax
{
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'trainedModelArn': 'string',
'versionIdentifier': 'string',
'incrementalTrainingDataChannels': [
{
'channelName': 'string',
'versionIdentifier': 'string',
'modelName': 'string'
},
],
'name': 'string',
'description': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED',
'statusDetails': {
'statusCode': 'string',
'message': 'string'
},
'configuredModelAlgorithmAssociationArn': '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.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'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5en.48xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge',
'volumeSizeInGB': 123
},
'trainingInputMode': 'File'|'FastFile'|'Pipe',
'stoppingCondition': {
'maxRuntimeInSeconds': 123
},
'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'metricsStatusDetails': 'string',
'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'logsStatusDetails': 'string',
'trainingContainerImageDigest': 'string',
'mlModelTrainingPayerAccountId': 'string',
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'creatorAccountId': 'string'
}
Response Structure
(dict) --
membershipIdentifier (string) --
The membership ID of the member that created the trained model.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the trained model.
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model.
versionIdentifier (string) --
The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model.
incrementalTrainingDataChannels (list) --
Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.
(dict) --
Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training.
channelName (string) --
The name of the incremental training data channel that was used.
versionIdentifier (string) --
The version identifier of the trained model that was used for incremental training.
modelName (string) --
The name of the base trained model that was used for incremental training.
name (string) --
The name of the trained model.
description (string) --
The description of the trained model.
status (string) --
The status of the trained model.
statusDetails (dict) --
Details about the status of a resource.
statusCode (string) --
The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions.
message (string) --
The error message that was returned. The message is intended for human consumption and can change at any time. Use the statusCode for programmatic error handling.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model.
resourceConfig (dict) --
The EC2 resource configuration that was used to train this model.
instanceCount (integer) --
The number of resources that are used to train the model.
instanceType (string) --
The instance type that is used to train the model.
volumeSizeInGB (integer) --
The volume size of the instance that is used to train the model. Please see EC2 volume limit for volume size limitations on different instance types.
trainingInputMode (string) --
The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm.
stoppingCondition (dict) --
The stopping condition that determined when model training ended.
maxRuntimeInSeconds (integer) --
The maximum amount of time, in seconds, that model training can run before it is terminated.
metricsStatus (string) --
The status of the model metrics.
metricsStatusDetails (string) --
Details about the status information for the model metrics.
logsStatus (string) --
Status information for the logs.
logsStatusDetails (string) --
Details about the status information for the logs.
trainingContainerImageDigest (string) --
Information about the training container image.
mlModelTrainingPayerAccountId (string) --
The account ID of the member that is responsible for paying for model training costs.
createTime (datetime) --
The time at which the trained model was created.
updateTime (datetime) --
The most recent time at which the trained model was updated.
creatorAccountId (string) --
The account ID of the member that created the trained model.
{'payerConfiguration': {'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'}}
Returns information about an ML input channel.
See also: AWS API Documentation
Request Syntax
client.get_ml_input_channel(
mlInputChannelArn='string',
membershipIdentifier='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the ML input channel that you want to get.
string
[REQUIRED]
The membership ID of the membership that contains the ML input channel that you want to get.
dict
Response Syntax
{
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'mlInputChannelArn': 'string',
'name': 'string',
'configuredModelAlgorithmAssociations': [
'string',
],
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE',
'statusDetails': {
'statusCode': 'string',
'message': 'string'
},
'retentionInDays': 123,
'numberOfRecords': 123,
'privacyBudgets': {
'accessBudgets': [
{
'resourceArn': 'string',
'details': [
{
'startTime': datetime(2015, 1, 1),
'endTime': datetime(2015, 1, 1),
'remainingBudget': 123,
'budget': 123,
'budgetType': 'CALENDAR_DAY'|'CALENDAR_MONTH'|'CALENDAR_WEEK'|'LIFETIME',
'autoRefresh': 'ENABLED'|'DISABLED'
},
],
'aggregateRemainingBudget': 123
},
]
},
'description': 'string',
'syntheticDataConfiguration': {
'syntheticDataParameters': {
'epsilon': 123.0,
'maxMembershipInferenceAttackScore': 123.0,
'columnClassification': {
'columnMapping': [
{
'columnName': 'string',
'columnType': 'CATEGORICAL'|'NUMERICAL',
'isPredictiveValue': True|False
},
]
}
},
'syntheticDataEvaluationScores': {
'dataPrivacyScores': {
'membershipInferenceAttackScores': [
{
'attackVersion': 'DISTANCE_TO_CLOSEST_RECORD_V1',
'score': 123.0
},
]
}
}
},
'payerConfiguration': {
'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'
},
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'inputChannel': {
'dataSource': {
'protectedQueryInputParameters': {
'sqlParameters': {
'queryString': 'string',
'analysisTemplateArn': 'string',
'parameters': {
'string': 'string'
}
},
'computeConfiguration': {
'worker': {
'type': 'CR.1X'|'CR.4X',
'number': 123,
'properties': {
'spark': {
'string': 'string'
}
}
}
},
'resultFormat': 'CSV'|'PARQUET'
}
},
'roleArn': 'string'
},
'protectedQueryIdentifier': 'string',
'numberOfFiles': 123.0,
'sizeInGb': 123.0,
'kmsKeyArn': 'string',
'tags': {
'string': 'string'
}
}
Response Structure
(dict) --
membershipIdentifier (string) --
The membership ID of the membership that contains the ML input channel.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the ML input channel.
mlInputChannelArn (string) --
The Amazon Resource Name (ARN) of the ML input channel.
name (string) --
The name of the ML input channel.
configuredModelAlgorithmAssociations (list) --
The configured model algorithm associations that were used to create the ML input channel.
(string) --
status (string) --
The status of the ML input channel.
statusDetails (dict) --
Details about the status of a resource.
statusCode (string) --
The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions.
message (string) --
The error message that was returned. The message is intended for human consumption and can change at any time. Use the statusCode for programmatic error handling.
retentionInDays (integer) --
The number of days to keep the data in the ML input channel.
numberOfRecords (integer) --
The number of records in the ML input channel.
privacyBudgets (dict) --
Returns the privacy budgets that control access to this Clean Rooms ML input channel. Use these budgets to monitor and limit resource consumption over specified time periods.
accessBudgets (list) --
A list of access budgets that apply to resources associated with this Clean Rooms ML input channel.
(dict) --
An access budget that defines consumption limits for a specific resource within defined time periods.
resourceArn (string) --
The Amazon Resource Name (ARN) of the resource that this access budget applies to.
details (list) --
A list of budget details for this resource. Contains active budget periods that apply to the resource.
(dict) --
The detailed information for a specific budget period, including time boundaries and budget amounts.
startTime (datetime) --
The start time of this budget period.
endTime (datetime) --
The end time of this budget period. If not specified, the budget period continues indefinitely.
remainingBudget (integer) --
The amount of budget remaining in this period.
budget (integer) --
The total budget amount allocated for this period.
budgetType (string) --
The type of budget period. Calendar-based types reset automatically at regular intervals, while LIFETIME budgets never reset.
autoRefresh (string) --
Specifies whether this budget automatically refreshes when the current period ends.
aggregateRemainingBudget (integer) --
The total remaining budget across all active budget periods for this resource.
description (string) --
The description of the ML input channel.
syntheticDataConfiguration (dict) --
The synthetic data configuration for this ML input channel, including parameters for generating privacy-preserving synthetic data and evaluation scores for measuring the privacy of the generated data.
syntheticDataParameters (dict) --
The parameters that control how synthetic data is generated, including privacy settings, column classifications, and other configuration options that affect the data synthesis process.
epsilon (float) --
The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.
maxMembershipInferenceAttackScore (float) --
The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.
columnClassification (dict) --
Classification details for data columns that specify how each column should be treated during synthetic data generation.
columnMapping (list) --
A mapping that defines the classification of data columns for synthetic data generation and specifies how each column should be handled during the privacy-preserving data synthesis process.
(dict) --
Properties that define how a specific data column should be handled during synthetic data generation, including its name, type, and role in predictive modeling.
columnName (string) --
The name of the data column as it appears in the dataset.
columnType (string) --
The data type of the column, which determines how the synthetic data generation algorithm processes and synthesizes values for this column.
isPredictiveValue (boolean) --
Indicates if this column contains predictive values that should be treated as target variables in machine learning models. This affects how the synthetic data generation preserves statistical relationships.
syntheticDataEvaluationScores (dict) --
Evaluation scores that assess the quality and privacy characteristics of the generated synthetic data, providing metrics on data utility and privacy preservation.
dataPrivacyScores (dict) --
Privacy-specific evaluation scores that measure how well the synthetic data protects individual privacy, including assessments of potential privacy risks such as membership inference attacks.
membershipInferenceAttackScores (list) --
Scores that evaluate the vulnerability of the synthetic data to membership inference attacks, which attempt to determine whether a specific individual was a member of the original dataset.
(dict) --
A score that measures the vulnerability of synthetic data to membership inference attacks and provides both the numerical score and the version of the attack methodology used for evaluation.
attackVersion (string) --
The version of the membership inference attack, which consists of the attack type and its version number, used to generate this privacy score.
score (float) --
The numerical score representing the vulnerability to membership inference attacks.
payerConfiguration (dict) --
The payer configuration for the ML input channel.
computePayerAccountId (string) --
The account ID of the member that is responsible for paying compute costs.
syntheticDataPayerAccountId (string) --
The account ID of the member that is responsible for paying synthetic data generation costs.
createTime (datetime) --
The time at which the ML input channel was created.
updateTime (datetime) --
The most recent time at which the ML input channel was updated.
inputChannel (dict) --
The input channel that was used to create the ML input channel.
dataSource (dict) --
The data source that is used to create the ML input channel.
protectedQueryInputParameters (dict) --
Provides information necessary to perform the protected query.
sqlParameters (dict) --
The parameters for the SQL type Protected Query.
queryString (string) --
The query string to be submitted.
analysisTemplateArn (string) --
The Amazon Resource Name (ARN) associated with the analysis template within a collaboration.
parameters (dict) --
The protected query SQL parameters.
(string) --
(string) --
computeConfiguration (dict) --
Provides configuration information for the workers that will perform the protected query.
worker (dict) --
The worker instances that will perform the compute work.
type (string) --
The instance type of the compute workers that are used.
number (integer) --
The number of compute workers that are used.
properties (dict) --
The configuration properties for the worker compute environment. These properties allow you to customize the compute settings for your Clean Rooms workloads.
spark (dict) --
The Spark configuration properties for SQL workloads. This map contains key-value pairs that configure Apache Spark settings to optimize performance for your data processing jobs. You can specify up to 50 Spark properties, with each key being 1-200 characters and each value being 0-500 characters. These properties allow you to adjust compute capacity for large datasets and complex workloads.
(string) --
(string) --
resultFormat (string) --
The format in which the query results should be returned. If not specified, defaults to CSV.
roleArn (string) --
The Amazon Resource Name (ARN) of the role used to run the query specified in the dataSource field of the input channel.
Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an AccessDeniedException error.
protectedQueryIdentifier (string) --
The ID of the protected query that was used to create the ML input channel.
numberOfFiles (float) --
The number of files in the ML input channel.
sizeInGb (float) --
The size, in GB, of the ML input channel.
kmsKeyArn (string) --
The Amazon Resource Name (ARN) of the KMS key that was used to create the ML input channel.
tags (dict) --
The optional metadata that you applied 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) --
{'mlModelTrainingPayerAccountId': 'string'}
Returns information about a trained model.
See also: AWS API Documentation
Request Syntax
client.get_trained_model(
trainedModelArn='string',
membershipIdentifier='string',
versionIdentifier='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the trained model that you are interested in.
string
[REQUIRED]
The membership ID of the member that created the trained model that you are interested in.
string
The version identifier of the trained model to retrieve. If not specified, the operation returns information about the latest version of the trained model.
dict
Response Syntax
{
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'trainedModelArn': 'string',
'versionIdentifier': 'string',
'incrementalTrainingDataChannels': [
{
'channelName': 'string',
'versionIdentifier': 'string',
'modelName': 'string'
},
],
'name': 'string',
'description': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED',
'statusDetails': {
'statusCode': 'string',
'message': 'string'
},
'configuredModelAlgorithmAssociationArn': '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.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'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5en.48xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge',
'volumeSizeInGB': 123
},
'trainingInputMode': 'File'|'FastFile'|'Pipe',
'stoppingCondition': {
'maxRuntimeInSeconds': 123
},
'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'metricsStatusDetails': 'string',
'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'logsStatusDetails': 'string',
'trainingContainerImageDigest': 'string',
'mlModelTrainingPayerAccountId': 'string',
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'hyperparameters': {
'string': 'string'
},
'environment': {
'string': 'string'
},
'kmsKeyArn': 'string',
'tags': {
'string': 'string'
},
'dataChannels': [
{
'mlInputChannelArn': 'string',
'channelName': 'string',
's3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key'
},
]
}
Response Structure
(dict) --
membershipIdentifier (string) --
The membership ID of the member that created the trained model.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the trained model.
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model.
versionIdentifier (string) --
The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model.
incrementalTrainingDataChannels (list) --
Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.
(dict) --
Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training.
channelName (string) --
The name of the incremental training data channel that was used.
versionIdentifier (string) --
The version identifier of the trained model that was used for incremental training.
modelName (string) --
The name of the base trained model that was used for incremental training.
name (string) --
The name of the trained model.
description (string) --
The description of the trained model.
status (string) --
The status of the trained model.
statusDetails (dict) --
Details about the status of a resource.
statusCode (string) --
The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions.
message (string) --
The error message that was returned. The message is intended for human consumption and can change at any time. Use the statusCode for programmatic error handling.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create the trained model.
resourceConfig (dict) --
The EC2 resource configuration that was used to create the trained model.
instanceCount (integer) --
The number of resources that are used to train the model.
instanceType (string) --
The instance type that is used to train the model.
volumeSizeInGB (integer) --
The volume size of the instance that is used to train the model. Please see EC2 volume limit for volume size limitations on different instance types.
trainingInputMode (string) --
The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm.
stoppingCondition (dict) --
The stopping condition that was used to terminate model training.
maxRuntimeInSeconds (integer) --
The maximum amount of time, in seconds, that model training can run before it is terminated.
metricsStatus (string) --
The status of the model metrics.
metricsStatusDetails (string) --
Details about the metrics status for the trained model.
logsStatus (string) --
The logs status for the trained model.
logsStatusDetails (string) --
Details about the logs status for the trained model.
trainingContainerImageDigest (string) --
Information about the training image container.
mlModelTrainingPayerAccountId (string) --
The account ID of the member that is responsible for paying for model training costs.
createTime (datetime) --
The time at which the trained model was created.
updateTime (datetime) --
The most recent time at which the trained model was updated.
hyperparameters (dict) --
The hyperparameters that were used to create the trained model.
(string) --
(string) --
environment (dict) --
The EC2 environment that was used to create the trained model.
(string) --
(string) --
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 associated data.
tags (dict) --
The optional metadata that you applied 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) --
dataChannels (list) --
The data channels that were used for the trained model.
(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) --
The Amazon Resource Name (ARN) of the ML input channel for this model training data channel.
channelName (string) --
The name of the training data channel.
s3DataDistributionType (string) --
Specifies how the training data stored in Amazon S3 should be distributed to training instances. This parameter controls the data distribution strategy for the training job:
FullyReplicated - The entire dataset is replicated on each training instance. This is suitable for smaller datasets and algorithms that require access to the complete dataset.
ShardedByS3Key - The dataset is distributed across training instances based on Amazon S3 key names. This is suitable for larger datasets and distributed training scenarios where each instance processes a subset of the data.
{'mlModelInferencePayerAccountId': 'string'}
Returns information about a trained model inference job.
See also: AWS API Documentation
Request Syntax
client.get_trained_model_inference_job(
membershipIdentifier='string',
trainedModelInferenceJobArn='string'
)
string
[REQUIRED]
Provides the membership ID of the membership that contains the trained model inference job that you are interested in.
string
[REQUIRED]
Provides the Amazon Resource Name (ARN) of the trained model inference job that you are interested in.
dict
Response Syntax
{
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'trainedModelInferenceJobArn': 'string',
'configuredModelAlgorithmAssociationArn': 'string',
'name': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE',
'trainedModelArn': 'string',
'trainedModelVersionIdentifier': 'string',
'resourceConfig': {
'instanceType': 'ml.r7i.48xlarge'|'ml.r6i.16xlarge'|'ml.m6i.xlarge'|'ml.m5.4xlarge'|'ml.p2.xlarge'|'ml.m4.16xlarge'|'ml.r7i.16xlarge'|'ml.m7i.xlarge'|'ml.m6i.12xlarge'|'ml.r7i.8xlarge'|'ml.r7i.large'|'ml.m7i.12xlarge'|'ml.m6i.24xlarge'|'ml.m7i.24xlarge'|'ml.r6i.8xlarge'|'ml.r6i.large'|'ml.g5.2xlarge'|'ml.m5.large'|'ml.m7i.48xlarge'|'ml.m6i.16xlarge'|'ml.p2.16xlarge'|'ml.g5.4xlarge'|'ml.m7i.16xlarge'|'ml.c4.2xlarge'|'ml.c5.2xlarge'|'ml.c6i.32xlarge'|'ml.c4.4xlarge'|'ml.g5.8xlarge'|'ml.c6i.xlarge'|'ml.c5.4xlarge'|'ml.g4dn.xlarge'|'ml.c7i.xlarge'|'ml.c6i.12xlarge'|'ml.g4dn.12xlarge'|'ml.c7i.12xlarge'|'ml.c6i.24xlarge'|'ml.g4dn.2xlarge'|'ml.c7i.24xlarge'|'ml.c7i.2xlarge'|'ml.c4.8xlarge'|'ml.c6i.2xlarge'|'ml.g4dn.4xlarge'|'ml.c7i.48xlarge'|'ml.c7i.4xlarge'|'ml.c6i.16xlarge'|'ml.c5.9xlarge'|'ml.g4dn.16xlarge'|'ml.c7i.16xlarge'|'ml.c6i.4xlarge'|'ml.c5.xlarge'|'ml.c4.xlarge'|'ml.g4dn.8xlarge'|'ml.c7i.8xlarge'|'ml.c7i.large'|'ml.g5.xlarge'|'ml.c6i.8xlarge'|'ml.c6i.large'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.m7i.2xlarge'|'ml.c5.18xlarge'|'ml.g5.48xlarge'|'ml.m6i.2xlarge'|'ml.g5.16xlarge'|'ml.m7i.4xlarge'|'ml.r6i.32xlarge'|'ml.m6i.4xlarge'|'ml.m5.xlarge'|'ml.m4.10xlarge'|'ml.r6i.xlarge'|'ml.m5.12xlarge'|'ml.m4.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.xlarge'|'ml.r6i.12xlarge'|'ml.m5.24xlarge'|'ml.r7i.12xlarge'|'ml.m7i.8xlarge'|'ml.m7i.large'|'ml.r6i.24xlarge'|'ml.r6i.2xlarge'|'ml.m4.2xlarge'|'ml.r7i.24xlarge'|'ml.r7i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.large'|'ml.m5.2xlarge'|'ml.p2.8xlarge'|'ml.r6i.4xlarge'|'ml.m6i.32xlarge'|'ml.m4.4xlarge'|'ml.p3.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge',
'instanceCount': 123
},
'outputConfiguration': {
'accept': 'string',
'members': [
{
'accountId': 'string'
},
]
},
'membershipIdentifier': 'string',
'dataSource': {
'mlInputChannelArn': 'string'
},
'containerExecutionParameters': {
'maxPayloadInMB': 123
},
'statusDetails': {
'statusCode': 'string',
'message': 'string'
},
'description': 'string',
'inferenceContainerImageDigest': 'string',
'environment': {
'string': 'string'
},
'kmsKeyArn': 'string',
'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'metricsStatusDetails': 'string',
'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'logsStatusDetails': 'string',
'tags': {
'string': 'string'
},
'mlModelInferencePayerAccountId': 'string'
}
Response Structure
(dict) --
createTime (datetime) --
The time at which the trained model inference job was created.
updateTime (datetime) --
The most recent time at which the trained model inference job was updated.
trainedModelInferenceJobArn (string) --
The Amazon Resource Name (ARN) of the trained model inference job.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that was used for the trained model inference job.
name (string) --
The name of the trained model inference job.
status (string) --
The status of the trained model inference job.
trainedModelArn (string) --
The Amazon Resource Name (ARN) for the trained model that was used for the trained model inference job.
trainedModelVersionIdentifier (string) --
The version identifier of the trained model used for this inference job. This identifies the specific version of the trained model that was used to generate the inference results.
resourceConfig (dict) --
The resource configuration information for the trained model inference job.
instanceType (string) --
The type of instance that is used to perform model inference.
instanceCount (integer) --
The number of instances to use.
outputConfiguration (dict) --
The output configuration information for the trained model inference job.
accept (string) --
The MIME type used to specify the output data.
members (list) --
Defines the members that can receive inference output.
(dict) --
Defines who will receive inference results.
accountId (string) --
The account ID of the member that can receive inference results.
membershipIdentifier (string) --
The membership ID of the membership that contains the trained model inference job.
dataSource (dict) --
The data source that was used for the trained model inference job.
mlInputChannelArn (string) --
The Amazon Resource Name (ARN) of the ML input channel for this model inference data source.
containerExecutionParameters (dict) --
The execution parameters for the model inference job container.
maxPayloadInMB (integer) --
The maximum size of the inference container payload, specified in MB.
statusDetails (dict) --
Details about the status of a resource.
statusCode (string) --
The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions.
message (string) --
The error message that was returned. The message is intended for human consumption and can change at any time. Use the statusCode for programmatic error handling.
description (string) --
The description of the trained model inference job.
inferenceContainerImageDigest (string) --
Information about the training container image.
environment (dict) --
The environment variables to set in the Docker container.
(string) --
(string) --
kmsKeyArn (string) --
The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data.
metricsStatus (string) --
The metrics status for the trained model inference job.
metricsStatusDetails (string) --
Details about the metrics status for the trained model inference job.
logsStatus (string) --
The logs status for the trained model inference job.
logsStatusDetails (string) --
Details about the logs status for the trained model inference job.
tags (dict) --
The optional metadata that you applied 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) --
mlModelInferencePayerAccountId (string) --
The account ID of the member that is responsible for paying for model inference costs.
{'collaborationMLInputChannelsList': {'payerConfiguration': {'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'}}}
Returns a list of the ML input channels in a collaboration.
See also: AWS API Documentation
Request Syntax
client.list_collaboration_ml_input_channels(
nextToken='string',
maxResults=123,
collaborationIdentifier='string'
)
string
The token value retrieved from a previous call to access the next page of results.
integer
The maximum number of results to return.
string
[REQUIRED]
The collaboration ID of the collaboration that contains the ML input channels that you want to list.
dict
Response Syntax
{
'nextToken': 'string',
'collaborationMLInputChannelsList': [
{
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'name': 'string',
'configuredModelAlgorithmAssociations': [
'string',
],
'mlInputChannelArn': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE',
'creatorAccountId': 'string',
'description': 'string',
'payerConfiguration': {
'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'
}
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token value used to access the next page of results.
collaborationMLInputChannelsList (list) --
The list of ML input channels that you wanted.
(dict) --
Provides summary information about an ML input channel in a collaboration.
createTime (datetime) --
The time at which the ML input channel was created.
updateTime (datetime) --
The most recent time at which the ML input channel was updated.
membershipIdentifier (string) --
The membership ID of the membership that contains the ML input channel.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the ML input channel.
name (string) --
The name of the ML input channel.
configuredModelAlgorithmAssociations (list) --
The associated configured model algorithms used to create the ML input channel.
(string) --
mlInputChannelArn (string) --
The Amazon Resource Name (ARN) of the ML input channel.
status (string) --
The status of the ML input channel.
creatorAccountId (string) --
The account ID of the member who created the ML input channel.
description (string) --
The description of the ML input channel.
payerConfiguration (dict) --
The payer configuration for the ML input channel.
computePayerAccountId (string) --
The account ID of the member that is responsible for paying compute costs.
syntheticDataPayerAccountId (string) --
The account ID of the member that is responsible for paying synthetic data generation costs.
{'collaborationTrainedModelInferenceJobs': {'mlModelInferencePayerAccountId': 'string'}}
Returns a list of trained model inference jobs in a specified collaboration.
See also: AWS API Documentation
Request Syntax
client.list_collaboration_trained_model_inference_jobs(
nextToken='string',
maxResults=123,
collaborationIdentifier='string',
trainedModelArn='string',
trainedModelVersionIdentifier='string'
)
string
The token value retrieved from a previous call to access the next page of results.
integer
The maximum size of the results that is returned per call.
string
[REQUIRED]
The collaboration ID of the collaboration that contains the trained model inference jobs that you are interested in.
string
The Amazon Resource Name (ARN) of the trained model that was used to create the trained model inference jobs that you are interested in.
string
The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned.
dict
Response Syntax
{
'nextToken': 'string',
'collaborationTrainedModelInferenceJobs': [
{
'trainedModelInferenceJobArn': 'string',
'configuredModelAlgorithmAssociationArn': 'string',
'membershipIdentifier': 'string',
'trainedModelArn': 'string',
'trainedModelVersionIdentifier': 'string',
'collaborationIdentifier': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE',
'outputConfiguration': {
'accept': 'string',
'members': [
{
'accountId': 'string'
},
]
},
'name': 'string',
'description': 'string',
'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'metricsStatusDetails': 'string',
'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'logsStatusDetails': 'string',
'mlModelInferencePayerAccountId': 'string',
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'creatorAccountId': 'string'
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token value used to access the next page of results.
collaborationTrainedModelInferenceJobs (list) --
The trained model inference jobs that you are interested in.
(dict) --
Provides summary information about a trained model inference job in a collaboration.
trainedModelInferenceJobArn (string) --
The Amazon Resource Name (ARN) of the trained model inference job.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that is used for the trained model inference job.
membershipIdentifier (string) --
The membership ID of the membership that contains the trained model inference job.
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model that is used for the trained model inference job.
trainedModelVersionIdentifier (string) --
The version identifier of the trained model that was used for inference in this job.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the trained model inference job.
status (string) --
The status of the trained model inference job.
outputConfiguration (dict) --
Returns output configuration information for the trained model inference job.
accept (string) --
The MIME type used to specify the output data.
members (list) --
Defines the members that can receive inference output.
(dict) --
Defines who will receive inference results.
accountId (string) --
The account ID of the member that can receive inference results.
name (string) --
The name of the trained model inference job.
description (string) --
The description of the trained model inference job.
metricsStatus (string) --
the trained model inference job metrics status.
metricsStatusDetails (string) --
Details about the metrics status for trained model inference job.
logsStatus (string) --
The trained model inference job logs status.
logsStatusDetails (string) --
Details about the logs status for the trained model inference job.
mlModelInferencePayerAccountId (string) --
The account ID of the member that is responsible for paying for model inference costs.
createTime (datetime) --
The time at which the trained model inference job was created.
updateTime (datetime) --
The most recent time at which the trained model inference job was updated.
creatorAccountId (string) --
The account ID that created the trained model inference job.
{'collaborationTrainedModels': {'mlModelTrainingPayerAccountId': 'string'}}
Returns a list of the trained models in a collaboration.
See also: AWS API Documentation
Request Syntax
client.list_collaboration_trained_models(
nextToken='string',
maxResults=123,
collaborationIdentifier='string'
)
string
The token value retrieved from a previous call to access the next page of results.
integer
The maximum size of the results that is returned per call.
string
[REQUIRED]
The collaboration ID of the collaboration that contains the trained models you are interested in.
dict
Response Syntax
{
'nextToken': 'string',
'collaborationTrainedModels': [
{
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'trainedModelArn': 'string',
'name': 'string',
'versionIdentifier': 'string',
'incrementalTrainingDataChannels': [
{
'channelName': 'string',
'versionIdentifier': 'string',
'modelName': 'string'
},
],
'description': 'string',
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED',
'configuredModelAlgorithmAssociationArn': 'string',
'creatorAccountId': 'string',
'mlModelTrainingPayerAccountId': 'string'
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token value used to access the next page of results.
collaborationTrainedModels (list) --
The trained models in the collaboration that you requested.
(dict) --
Provides summary information about a trained model in a collaboration.
createTime (datetime) --
The time at which the trained model was created.
updateTime (datetime) --
The most recent time at which the trained model was updated.
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model.
name (string) --
The name of the trained model.
versionIdentifier (string) --
The version identifier of this trained model version.
incrementalTrainingDataChannels (list) --
Information about the incremental training data channels used to create this version of the trained model.
(dict) --
Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training.
channelName (string) --
The name of the incremental training data channel that was used.
versionIdentifier (string) --
The version identifier of the trained model that was used for incremental training.
modelName (string) --
The name of the base trained model that was used for incremental training.
description (string) --
The description of the trained model.
membershipIdentifier (string) --
The membership ID of the member that created the trained model.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the trained model.
status (string) --
The status of the trained model.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model.
creatorAccountId (string) --
The account ID of the member that created the trained model.
mlModelTrainingPayerAccountId (string) --
The account ID of the member that is responsible for paying for model training costs.
{'mlInputChannelsList': {'payerConfiguration': {'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'}}}
Returns a list of ML input channels.
See also: AWS API Documentation
Request Syntax
client.list_ml_input_channels(
nextToken='string',
maxResults=123,
membershipIdentifier='string'
)
string
The token value retrieved from a previous call to access the next page of results.
integer
The maximum number of ML input channels to return.
string
[REQUIRED]
The membership ID of the membership that contains the ML input channels that you want to list.
dict
Response Syntax
{
'nextToken': 'string',
'mlInputChannelsList': [
{
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'name': 'string',
'configuredModelAlgorithmAssociations': [
'string',
],
'protectedQueryIdentifier': 'string',
'mlInputChannelArn': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE',
'description': 'string',
'payerConfiguration': {
'computePayerAccountId': 'string',
'syntheticDataPayerAccountId': 'string'
}
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token value used to access the next page of results.
mlInputChannelsList (list) --
The list of ML input channels that you wanted.
(dict) --
Provides summary information about the ML input channel.
createTime (datetime) --
The time at which the ML input channel was created.
updateTime (datetime) --
The most recent time at which the ML input channel was updated.
membershipIdentifier (string) --
The membership ID of the membership that contains the ML input channel.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the ML input channel.
name (string) --
The name of the ML input channel.
configuredModelAlgorithmAssociations (list) --
The associated configured model algorithms used to create the ML input channel.
(string) --
protectedQueryIdentifier (string) --
The ID of the protected query that was used to create the ML input channel.
mlInputChannelArn (string) --
The Amazon Resource Name (ARN) of the ML input channel.
status (string) --
The status of the ML input channel.
description (string) --
The description of the ML input channel.
payerConfiguration (dict) --
The payer configuration for the ML input channel.
computePayerAccountId (string) --
The account ID of the member that is responsible for paying compute costs.
syntheticDataPayerAccountId (string) --
The account ID of the member that is responsible for paying synthetic data generation costs.
{'trainedModelInferenceJobs': {'mlModelInferencePayerAccountId': 'string'}}
Returns a list of trained model inference jobs that match the request parameters.
See also: AWS API Documentation
Request Syntax
client.list_trained_model_inference_jobs(
nextToken='string',
maxResults=123,
membershipIdentifier='string',
trainedModelArn='string',
trainedModelVersionIdentifier='string'
)
string
The token value retrieved from a previous call to access the next page of results.
integer
The maximum size of the results that is returned per call.
string
[REQUIRED]
The membership
string
The Amazon Resource Name (ARN) of a trained model that was used to create the trained model inference jobs that you are interested in.
string
The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned.
dict
Response Syntax
{
'nextToken': 'string',
'trainedModelInferenceJobs': [
{
'trainedModelInferenceJobArn': 'string',
'configuredModelAlgorithmAssociationArn': 'string',
'membershipIdentifier': 'string',
'trainedModelArn': 'string',
'trainedModelVersionIdentifier': 'string',
'collaborationIdentifier': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE',
'outputConfiguration': {
'accept': 'string',
'members': [
{
'accountId': 'string'
},
]
},
'name': 'string',
'description': 'string',
'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'metricsStatusDetails': 'string',
'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED',
'logsStatusDetails': 'string',
'mlModelInferencePayerAccountId': 'string',
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1)
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token value used to access the next page of results.
trainedModelInferenceJobs (list) --
Returns the requested trained model inference jobs.
(dict) --
Provides information about the trained model inference job.
trainedModelInferenceJobArn (string) --
The Amazon Resource Name (ARN) of the trained model inference job.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that is used for the trained model inference job.
membershipIdentifier (string) --
The membership ID of the membership that contains the trained model inference job.
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model that is used for the trained model inference job.
trainedModelVersionIdentifier (string) --
The version identifier of the trained model that was used for inference in this job.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the trained model inference job.
status (string) --
The status of the trained model inference job.
outputConfiguration (dict) --
The output configuration information of the trained model job.
accept (string) --
The MIME type used to specify the output data.
members (list) --
Defines the members that can receive inference output.
(dict) --
Defines who will receive inference results.
accountId (string) --
The account ID of the member that can receive inference results.
name (string) --
The name of the trained model inference job.
description (string) --
The description of the trained model inference job.
metricsStatus (string) --
The metric status of the trained model inference job.
metricsStatusDetails (string) --
Details about the metrics status for the trained model inference job.
logsStatus (string) --
The log status of the trained model inference job.
logsStatusDetails (string) --
Details about the log status for the trained model inference job.
mlModelInferencePayerAccountId (string) --
The account ID of the member that is responsible for paying for model inference costs.
createTime (datetime) --
The time at which the trained model inference job was created.
updateTime (datetime) --
The most recent time at which the trained model inference job was updated.
{'trainedModels': {'mlModelTrainingPayerAccountId': 'string'}}
Returns a list of trained model versions for a specified trained model. This operation allows you to view all versions of a trained model, including information about their status and creation details. You can use this to track the evolution of your trained models and select specific versions for inference or further training.
See also: AWS API Documentation
Request Syntax
client.list_trained_model_versions(
nextToken='string',
maxResults=123,
membershipIdentifier='string',
trainedModelArn='string',
status='CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'
)
string
The pagination token from a previous ListTrainedModelVersions request. Use this token to retrieve the next page of results.
integer
The maximum number of trained model versions to return in a single page. The default value is 10, and the maximum value is 100.
string
[REQUIRED]
The membership identifier for the collaboration that contains the trained model.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the trained model for which to list versions.
string
Filter the results to only include trained model versions with the specified status. Valid values include CREATE_PENDING, CREATE_IN_PROGRESS, ACTIVE, CREATE_FAILED, and others.
dict
Response Syntax
{
'nextToken': 'string',
'trainedModels': [
{
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'trainedModelArn': 'string',
'versionIdentifier': 'string',
'incrementalTrainingDataChannels': [
{
'channelName': 'string',
'versionIdentifier': 'string',
'modelName': 'string'
},
],
'name': 'string',
'description': 'string',
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED',
'configuredModelAlgorithmAssociationArn': 'string',
'mlModelTrainingPayerAccountId': 'string'
},
]
}
Response Structure
(dict) --
nextToken (string) --
The pagination token to use in a subsequent ListTrainedModelVersions request to retrieve the next page of results. This value is null when there are no more results to return.
trainedModels (list) --
A list of trained model versions that match the specified criteria. Each entry contains summary information about a trained model version, including its version identifier, status, and creation details.
(dict) --
Summary information about the trained model.
createTime (datetime) --
The time at which the trained model was created.
updateTime (datetime) --
The most recent time at which the trained model was updated.
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model.
versionIdentifier (string) --
The version identifier of this trained model version.
incrementalTrainingDataChannels (list) --
Information about the incremental training data channels used to create this version of the trained model.
(dict) --
Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training.
channelName (string) --
The name of the incremental training data channel that was used.
versionIdentifier (string) --
The version identifier of the trained model that was used for incremental training.
modelName (string) --
The name of the base trained model that was used for incremental training.
name (string) --
The name of the trained model.
description (string) --
The description of the trained model.
membershipIdentifier (string) --
The membership ID of the member that created the trained model.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the trained model.
status (string) --
The status of the trained model.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model.
mlModelTrainingPayerAccountId (string) --
The account ID of the member that is responsible for paying for model training costs.
{'trainedModels': {'mlModelTrainingPayerAccountId': 'string'}}
Returns a list of trained models.
See also: AWS API Documentation
Request Syntax
client.list_trained_models(
nextToken='string',
maxResults=123,
membershipIdentifier='string'
)
string
The token value retrieved from a previous call to access the next page of results.
integer
The maximum size of the results that is returned per call.
string
[REQUIRED]
The membership ID of the member that created the trained models you are interested in.
dict
Response Syntax
{
'nextToken': 'string',
'trainedModels': [
{
'createTime': datetime(2015, 1, 1),
'updateTime': datetime(2015, 1, 1),
'trainedModelArn': 'string',
'versionIdentifier': 'string',
'incrementalTrainingDataChannels': [
{
'channelName': 'string',
'versionIdentifier': 'string',
'modelName': 'string'
},
],
'name': 'string',
'description': 'string',
'membershipIdentifier': 'string',
'collaborationIdentifier': 'string',
'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED',
'configuredModelAlgorithmAssociationArn': 'string',
'mlModelTrainingPayerAccountId': 'string'
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token value used to access the next page of results.
trainedModels (list) --
The list of trained models.
(dict) --
Summary information about the trained model.
createTime (datetime) --
The time at which the trained model was created.
updateTime (datetime) --
The most recent time at which the trained model was updated.
trainedModelArn (string) --
The Amazon Resource Name (ARN) of the trained model.
versionIdentifier (string) --
The version identifier of this trained model version.
incrementalTrainingDataChannels (list) --
Information about the incremental training data channels used to create this version of the trained model.
(dict) --
Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training.
channelName (string) --
The name of the incremental training data channel that was used.
versionIdentifier (string) --
The version identifier of the trained model that was used for incremental training.
modelName (string) --
The name of the base trained model that was used for incremental training.
name (string) --
The name of the trained model.
description (string) --
The description of the trained model.
membershipIdentifier (string) --
The membership ID of the member that created the trained model.
collaborationIdentifier (string) --
The collaboration ID of the collaboration that contains the trained model.
status (string) --
The status of the trained model.
configuredModelAlgorithmAssociationArn (string) --
The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model.
mlModelTrainingPayerAccountId (string) --
The account ID of the member that is responsible for paying for model training costs.
{'mlModelInferencePayerAccountId': 'string'}
Defines the information necessary to begin a trained model inference job.
See also: AWS API Documentation
Request Syntax
client.start_trained_model_inference_job(
membershipIdentifier='string',
name='string',
trainedModelArn='string',
trainedModelVersionIdentifier='string',
configuredModelAlgorithmAssociationArn='string',
resourceConfig={
'instanceType': 'ml.r7i.48xlarge'|'ml.r6i.16xlarge'|'ml.m6i.xlarge'|'ml.m5.4xlarge'|'ml.p2.xlarge'|'ml.m4.16xlarge'|'ml.r7i.16xlarge'|'ml.m7i.xlarge'|'ml.m6i.12xlarge'|'ml.r7i.8xlarge'|'ml.r7i.large'|'ml.m7i.12xlarge'|'ml.m6i.24xlarge'|'ml.m7i.24xlarge'|'ml.r6i.8xlarge'|'ml.r6i.large'|'ml.g5.2xlarge'|'ml.m5.large'|'ml.m7i.48xlarge'|'ml.m6i.16xlarge'|'ml.p2.16xlarge'|'ml.g5.4xlarge'|'ml.m7i.16xlarge'|'ml.c4.2xlarge'|'ml.c5.2xlarge'|'ml.c6i.32xlarge'|'ml.c4.4xlarge'|'ml.g5.8xlarge'|'ml.c6i.xlarge'|'ml.c5.4xlarge'|'ml.g4dn.xlarge'|'ml.c7i.xlarge'|'ml.c6i.12xlarge'|'ml.g4dn.12xlarge'|'ml.c7i.12xlarge'|'ml.c6i.24xlarge'|'ml.g4dn.2xlarge'|'ml.c7i.24xlarge'|'ml.c7i.2xlarge'|'ml.c4.8xlarge'|'ml.c6i.2xlarge'|'ml.g4dn.4xlarge'|'ml.c7i.48xlarge'|'ml.c7i.4xlarge'|'ml.c6i.16xlarge'|'ml.c5.9xlarge'|'ml.g4dn.16xlarge'|'ml.c7i.16xlarge'|'ml.c6i.4xlarge'|'ml.c5.xlarge'|'ml.c4.xlarge'|'ml.g4dn.8xlarge'|'ml.c7i.8xlarge'|'ml.c7i.large'|'ml.g5.xlarge'|'ml.c6i.8xlarge'|'ml.c6i.large'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.m7i.2xlarge'|'ml.c5.18xlarge'|'ml.g5.48xlarge'|'ml.m6i.2xlarge'|'ml.g5.16xlarge'|'ml.m7i.4xlarge'|'ml.r6i.32xlarge'|'ml.m6i.4xlarge'|'ml.m5.xlarge'|'ml.m4.10xlarge'|'ml.r6i.xlarge'|'ml.m5.12xlarge'|'ml.m4.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.xlarge'|'ml.r6i.12xlarge'|'ml.m5.24xlarge'|'ml.r7i.12xlarge'|'ml.m7i.8xlarge'|'ml.m7i.large'|'ml.r6i.24xlarge'|'ml.r6i.2xlarge'|'ml.m4.2xlarge'|'ml.r7i.24xlarge'|'ml.r7i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.large'|'ml.m5.2xlarge'|'ml.p2.8xlarge'|'ml.r6i.4xlarge'|'ml.m6i.32xlarge'|'ml.m4.4xlarge'|'ml.p3.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge',
'instanceCount': 123
},
outputConfiguration={
'accept': 'string',
'members': [
{
'accountId': 'string'
},
]
},
dataSource={
'mlInputChannelArn': 'string'
},
description='string',
containerExecutionParameters={
'maxPayloadInMB': 123
},
environment={
'string': 'string'
},
kmsKeyArn='string',
tags={
'string': 'string'
},
mlModelInferencePayerAccountId='string'
)
string
[REQUIRED]
The membership ID of the membership that contains the trained model inference job.
string
[REQUIRED]
The name of the trained model inference job.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the trained model that is used for this trained model inference job.
string
The version identifier of the trained model to use for inference. This specifies which version of the trained model should be used to generate predictions on the input data.
string
The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model inference job.
dict
[REQUIRED]
Defines the resource configuration for the trained model inference job.
instanceType (string) -- [REQUIRED]
The type of instance that is used to perform model inference.
instanceCount (integer) --
The number of instances to use.
dict
[REQUIRED]
Defines the output configuration information for the trained model inference job.
accept (string) --
The MIME type used to specify the output data.
members (list) -- [REQUIRED]
Defines the members that can receive inference output.
(dict) --
Defines who will receive inference results.
accountId (string) -- [REQUIRED]
The account ID of the member that can receive inference results.
dict
[REQUIRED]
Defines the data source that is used for the trained model inference job.
mlInputChannelArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the ML input channel for this model inference data source.
string
The description of the trained model inference job.
dict
The execution parameters for the container.
maxPayloadInMB (integer) --
The maximum size of the inference container payload, specified in MB.
dict
The environment variables to set in the Docker container.
(string) --
(string) --
string
The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data.
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) --
string
The account ID of the member that is responsible for paying for model inference costs.
dict
Response Syntax
{
'trainedModelInferenceJobArn': 'string'
}
Response Structure
(dict) --
trainedModelInferenceJobArn (string) --
The Amazon Resource Name (ARN) of the trained model inference job.