2024/08/12 - Amazon SageMaker Service - 8 updated api methods
Changes Releasing large data support as part of CreateAutoMLJobV2 in SageMaker Autopilot and CreateDomain API for SageMaker Canvas.
{'AutoMLComputeConfig': {'EmrServerlessComputeConfig': {'ExecutionRoleARN': 'string'}}}
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.
AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.
Note
CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.
CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob , as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
For the list of available problem types supported by CreateAutoMLJobV2 , see AutoMLProblemTypeConfig.
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
See also: AWS API Documentation
Request Syntax
client.create_auto_ml_job_v2(
AutoMLJobName='string',
AutoMLJobInputDataConfig=[
{
'ChannelType': 'training'|'validation',
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
}
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
AutoMLProblemTypeConfig={
'ImageClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
}
},
'TextClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ContentColumn': 'string',
'TargetLabelColumn': 'string'
},
'TimeSeriesForecastingJobConfig': {
'FeatureSpecificationS3Uri': 'string',
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ForecastFrequency': 'string',
'ForecastHorizon': 123,
'ForecastQuantiles': [
'string',
],
'Transformations': {
'Filling': {
'string': {
'string': 'string'
}
},
'Aggregation': {
'string': 'sum'|'avg'|'first'|'min'|'max'
}
},
'TimeSeriesConfig': {
'TargetAttributeName': 'string',
'TimestampAttributeName': 'string',
'ItemIdentifierAttributeName': 'string',
'GroupingAttributeNames': [
'string',
]
},
'HolidayConfig': [
{
'CountryCode': 'string'
},
],
'CandidateGenerationConfig': {
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai'|'cnn-qr'|'deepar'|'prophet'|'npts'|'arima'|'ets',
]
},
]
}
},
'TabularJobConfig': {
'CandidateGenerationConfig': {
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai'|'cnn-qr'|'deepar'|'prophet'|'npts'|'arima'|'ets',
]
},
]
},
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'FeatureSpecificationS3Uri': 'string',
'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
'GenerateCandidateDefinitionsOnly': True|False,
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'TargetAttributeName': 'string',
'SampleWeightAttributeName': 'string'
},
'TextGenerationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'BaseModelName': 'string',
'TextGenerationHyperParameters': {
'string': 'string'
},
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
SecurityConfig={
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
AutoMLJobObjective={
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
ModelDeployConfig={
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
DataSplitConfig={
'ValidationFraction': ...
},
AutoMLComputeConfig={
'EmrServerlessComputeConfig': {
'ExecutionRoleARN': 'string'
}
}
)
string
[REQUIRED]
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
list
[REQUIRED]
An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:
For tabular problem types: S3Prefix , ManifestFile .
For image classification: S3Prefix , ManifestFile , AugmentedManifestFile .
For text classification: S3Prefix .
For time-series forecasting: S3Prefix .
For text generation (LLMs fine-tuning): S3Prefix .
(dict) --
A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).
ChannelType (string) --
The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType
Note
The type of channel defaults to training for the time-series forecasting problem type.
ContentType (string) --
The content type of the data from the input source. The following are the allowed content types for different problems:
For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For image classification: image/png , image/jpeg , or image/* . The default value is image/* .
For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
CompressionType (string) --
The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .
DataSource (dict) --
The data source for an AutoML channel (Required).
S3DataSource (dict) -- [REQUIRED]
The Amazon S3 location of the input data.
S3DataType (string) -- [REQUIRED]
The data type.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
S3Uri (string) -- [REQUIRED]
The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
dict
[REQUIRED]
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
KmsKeyId (string) --
The Key Management Service encryption key ID.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 output path. Must be 512 characters or less.
dict
[REQUIRED]
Defines the configuration settings of one of the supported problem types.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: ImageClassificationJobConfig, TextClassificationJobConfig, TimeSeriesForecastingJobConfig, TabularJobConfig, TextGenerationJobConfig.
ImageClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the image classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
TextClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ContentColumn (string) -- [REQUIRED]
The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
TargetLabelColumn (string) -- [REQUIRED]
The name of the column used to provide the class labels. It should not be same as the content column.
TimeSeriesForecastingJobConfig (dict) --
Settings used to configure an AutoML job V2 for the time-series forecasting problem type.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .
You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types: numeric , categorical , text , and datetime .
Note
These column keys must not include any column set in TimeSeriesConfig .
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ForecastFrequency (string) -- [REQUIRED]
The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .
The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
ForecastHorizon (integer) -- [REQUIRED]
The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
ForecastQuantiles (list) --
The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.
(string) --
Transformations (dict) --
The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
Filling (dict) --
A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.
The supported filling methods and their corresponding options are:
frontfill : none (Supported only for target column)
middlefill : zero , value , median , mean , min , max
backfill : zero , value , median , mean , min , max
futurefill : zero , value , median , mean , min , max
To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .
(string) --
(dict) --
(string) --
(string) --
Aggregation (dict) --
A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
The supported aggregation methods are sum (default), avg , first , min , max .
Note
Aggregation is only supported for the target column.
(string) --
(string) --
TimeSeriesConfig (dict) -- [REQUIRED]
The collection of components that defines the time-series.
TargetAttributeName (string) -- [REQUIRED]
The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
TimestampAttributeName (string) -- [REQUIRED]
The name of the column indicating a point in time at which the target value of a given item is recorded.
ItemIdentifierAttributeName (string) -- [REQUIRED]
The name of the column that represents the set of item identifiers for which you want to predict the target value.
GroupingAttributeNames (list) --
A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
(string) --
HolidayConfig (list) --
The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
(dict) --
Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.
CountryCode (string) --
The country code for the holiday calendar.
For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.
CandidateGenerationConfig (dict) --
Stores the configuration information for how model candidates are generated using an AutoML job V2.
AlgorithmsConfig (list) --
Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig stores the customized selection of algorithms to train on your data.
For the tabular problem type TabularJobConfig ,the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode.
AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO .
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
For the time-series forecasting problem type TimeSeriesForecastingJobConfig ,choose your algorithms from the list provided in AlgorithmConfig. For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
(dict) --
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithms (list) -- [REQUIRED]
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
For the tabular problem type TabularJobConfig :
Note
Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode ( ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
For the time-series forecasting problem type TimeSeriesForecastingJobConfig :
Choose your algorithms from this list.
"cnn-qr"
"deepar"
"prophet"
"arima"
"npts"
"ets"
(string) --
TabularJobConfig (dict) --
Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
CandidateGenerationConfig (dict) --
The configuration information of how model candidates are generated.
AlgorithmsConfig (list) --
Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig stores the customized selection of algorithms to train on your data.
For the tabular problem type TabularJobConfig ,the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode.
AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO .
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
For the time-series forecasting problem type TimeSeriesForecastingJobConfig ,choose your algorithms from the list provided in AlgorithmConfig. For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
(dict) --
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithms (list) -- [REQUIRED]
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
For the tabular problem type TabularJobConfig :
Note
Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode ( ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
For the time-series forecasting problem type TimeSeriesForecastingJobConfig :
Choose your algorithms from this list.
"cnn-qr"
"deepar"
"prophet"
"arima"
"npts"
"ets"
(string) --
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note
These column keys may not include the target column.
In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .
If only FeatureDataTypes is provided, the column keys ( col1 , col2 ,..) should be a subset of the column names in the input data.
If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .
The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
Mode (string) --
The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
GenerateCandidateDefinitionsOnly (boolean) --
Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
ProblemType (string) --
The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.
Note
You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
TargetAttributeName (string) -- [REQUIRED]
The name of the target variable in supervised learning, usually represented by 'y'.
SampleWeightAttributeName (string) --
If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
TextGenerationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.
Note
The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.
CompletionCriteria (dict) --
How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
BaseModelName (string) --
The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct .
TextGenerationHyperParameters (dict) --
The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.
"epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".
"batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".
"learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".
"learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".
Here is an example where all four hyperparameters are configured.
{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }
(string) --
(string) --
ModelAccessConfig (dict) --
The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig .
If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.
If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.
AcceptEula (boolean) -- [REQUIRED]
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
string
[REQUIRED]
The ARN of the role that is used to access the data.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
The security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId (string) --
The key used to encrypt stored data.
EnableInterContainerTrafficEncryption (boolean) --
Whether to use traffic encryption between the container layers.
VpcConfig (dict) --
The VPC configuration.
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
(string) --
dict
Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.
Note
For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig ( TabularJobConfig.ProblemType ), or none at all.
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
MetricName (string) -- [REQUIRED]
The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
For tabular problem types:
List of available metrics:
Regression: MAE , MSE , R2 , RMSE
Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , Precision , Recall
Multiclass classification: Accuracy , BalancedAccuracy , F1macro , PrecisionMacro , RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
Default objective metrics:
Regression: MSE .
Binary classification: F1 .
Multiclass classification: Accuracy .
For image or text classification problem types:
List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification.
Default objective metrics: Accuracy
For time-series forecasting problem types:
List of available metrics: RMSE , wQL , Average wQL , MASE , MAPE , WAPE For a description of each metric, see Autopilot metrics for time-series forecasting.
Default objective metrics: AverageWeightedQuantileLoss
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
dict
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
AutoGenerateEndpointName (boolean) --
Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .
Note
If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note
Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.
dict
This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob , the validation dataset must be less than 2 GB in size.
Note
This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.
ValidationFraction (float) --
The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
dict
Specifies the compute configuration for the AutoML job V2.
EmrServerlessComputeConfig (dict) --
The configuration for using EMR Serverless to run the AutoML job V2.
To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request.
By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.
EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.
ExecutionRoleARN (string) -- [REQUIRED]
The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see "How to configure AutoML to initiate a remote job on EMR Serverless for large datasets" in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.
dict
Response Syntax
{
'AutoMLJobArn': 'string'
}
Response Structure
(dict) --
AutoMLJobArn (string) --
The unique ARN assigned to the AutoMLJob when it is created.
{'DefaultUserSettings': {'CanvasAppSettings': {'EmrServerlessSettings': {'ExecutionRoleArn': 'string',
'Status': 'ENABLED '
'| '
'DISABLED'}},
'StudioWebPortalSettings': {'HiddenMlTools': {'InferenceOptimization'}}}}
Creates a Domain . A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:
PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.
VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
Warning
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.
For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
See also: AWS API Documentation
Request Syntax
client.create_domain(
DomainName='string',
AuthMode='SSO'|'IAM',
DefaultUserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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],
'RStudioServerProDomainSettings': {
'DomainExecutionRoleArn': 'string',
'RStudioConnectUrl': 'string',
'RStudioPackageManagerUrl': 'string',
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
}
},
'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED',
'DockerSettings': {
'EnableDockerAccess': 'ENABLED'|'DISABLED',
'VpcOnlyTrustedAccounts': [
'string',
]
},
'AmazonQSettings': {
'Status': 'ENABLED'|'DISABLED',
'QProfileArn': 'string'
}
},
SubnetIds=[
'string',
],
VpcId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
AppNetworkAccessType='PublicInternetOnly'|'VpcOnly',
HomeEfsFileSystemKmsKeyId='string',
KmsKeyId='string',
AppSecurityGroupManagement='Service'|'Customer',
DefaultSpaceSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
}
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
}
},
]
}
)
string
[REQUIRED]
A name for the domain.
string
[REQUIRED]
The mode of authentication that members use to access the domain.
dict
[REQUIRED]
The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.
SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings , the values specified in CreateUserProfile take precedence over those specified in CreateDomain .
ExecutionRole (string) --
The execution role for the user.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
S3OutputPath (string) --
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .
app:JupyterServer: : Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
dict
A collection of Domain settings.
SecurityGroupIds (list) --
The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
(string) --
RStudioServerProDomainSettings (dict) --
A collection of settings that configure the RStudioServerPro Domain-level app.
DomainExecutionRoleArn (string) -- [REQUIRED]
The ARN of the execution role for the RStudioServerPro Domain-level app.
RStudioConnectUrl (string) --
A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl (string) --
A URL pointing to an RStudio Package Manager server.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.
DockerSettings (dict) --
A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess (string) --
Indicates whether the domain can access Docker.
VpcOnlyTrustedAccounts (list) --
The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
(string) --
AmazonQSettings (dict) --
A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO .
Status (string) --
Whether Amazon Q has been enabled within the domain.
QProfileArn (string) --
The ARN of the Amazon Q profile used within the domain.
list
[REQUIRED]
The VPC subnets that the domain uses for communication.
(string) --
string
[REQUIRED]
The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
list
Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
Tags that you specify for the Domain are also added to all Apps that the Domain launches.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
string
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly .
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access
VpcOnly - All traffic is through the specified VPC and subnets
string
Use KmsKeyId .
string
SageMaker uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
string
The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service .
dict
The default settings used to create a space.
ExecutionRole (string) --
The ARN of the execution role for the space.
SecurityGroups (list) --
The security group IDs for the Amazon VPC that the space uses for communication.
(string) --
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The default storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
dict
Response Syntax
{
'DomainArn': 'string',
'Url': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the created domain.
Url (string) --
The URL to the created domain.
{'UserSettings': {'CanvasAppSettings': {'EmrServerlessSettings': {'ExecutionRoleArn': 'string',
'Status': 'ENABLED '
'| '
'DISABLED'}},
'StudioWebPortalSettings': {'HiddenMlTools': {'InferenceOptimization'}}}}
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.
See also: AWS API Documentation
Request Syntax
client.create_user_profile(
DomainId='string',
UserProfileName='string',
SingleSignOnUserIdentifier='string',
SingleSignOnUserValue='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
UserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
},
'ModelRegisterSettings': {
'Status': 'ENABLED'|'DISABLED',
'CrossAccountModelRegisterRoleArn': 'string'
},
'WorkspaceSettings': {
'S3ArtifactPath': 'string',
'S3KmsKeyId': 'string'
},
'IdentityProviderOAuthSettings': [
{
'DataSourceName': 'SalesforceGenie'|'Snowflake',
'Status': 'ENABLED'|'DISABLED',
'SecretArn': 'string'
},
],
'DirectDeploySettings': {
'Status': 'ENABLED'|'DISABLED'
},
'KendraSettings': {
'Status': 'ENABLED'|'DISABLED'
},
'GenerativeAiSettings': {
'AmazonBedrockRoleArn': 'string'
},
'EmrServerlessSettings': {
'ExecutionRoleArn': 'string',
'Status': 'ENABLED'|'DISABLED'
}
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
}
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
]
}
}
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
A name for the UserProfile. This value is not case sensitive.
string
A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is IAM Identity Center, this field is required. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
string
The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
list
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
S3OutputPath (string) --
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .
app:JupyterServer: : Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
{'AutoMLComputeConfig': {'EmrServerlessComputeConfig': {'ExecutionRoleARN': 'string'}}}
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
See also: AWS API Documentation
Request Syntax
client.describe_auto_ml_job_v2(
AutoMLJobName='string'
)
string
[REQUIRED]
Requests information about an AutoML job V2 using its unique name.
dict
Response Syntax
{
'AutoMLJobName': 'string',
'AutoMLJobArn': 'string',
'AutoMLJobInputDataConfig': [
{
'ChannelType': 'training'|'validation',
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'RoleArn': 'string',
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
'AutoMLProblemTypeConfig': {
'ImageClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
}
},
'TextClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ContentColumn': 'string',
'TargetLabelColumn': 'string'
},
'TimeSeriesForecastingJobConfig': {
'FeatureSpecificationS3Uri': 'string',
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ForecastFrequency': 'string',
'ForecastHorizon': 123,
'ForecastQuantiles': [
'string',
],
'Transformations': {
'Filling': {
'string': {
'string': 'string'
}
},
'Aggregation': {
'string': 'sum'|'avg'|'first'|'min'|'max'
}
},
'TimeSeriesConfig': {
'TargetAttributeName': 'string',
'TimestampAttributeName': 'string',
'ItemIdentifierAttributeName': 'string',
'GroupingAttributeNames': [
'string',
]
},
'HolidayConfig': [
{
'CountryCode': 'string'
},
],
'CandidateGenerationConfig': {
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai'|'cnn-qr'|'deepar'|'prophet'|'npts'|'arima'|'ets',
]
},
]
}
},
'TabularJobConfig': {
'CandidateGenerationConfig': {
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai'|'cnn-qr'|'deepar'|'prophet'|'npts'|'arima'|'ets',
]
},
]
},
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'FeatureSpecificationS3Uri': 'string',
'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
'GenerateCandidateDefinitionsOnly': True|False,
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'TargetAttributeName': 'string',
'SampleWeightAttributeName': 'string'
},
'TextGenerationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'BaseModelName': 'string',
'TextGenerationHyperParameters': {
'string': 'string'
},
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
'AutoMLProblemTypeConfigName': 'ImageClassification'|'TextClassification'|'TimeSeriesForecasting'|'Tabular'|'TextGeneration',
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'PartialFailureReasons': [
{
'PartialFailureMessage': 'string'
},
],
'BestCandidate': {
'CandidateName': 'string',
'FinalAutoMLJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
'Value': ...,
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
'CandidateSteps': [
{
'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
'CandidateStepArn': 'string',
'CandidateStepName': 'string'
},
],
'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'InferenceContainers': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
],
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'CandidateProperties': {
'CandidateArtifactLocations': {
'Explainability': 'string',
'ModelInsights': 'string',
'BacktestResults': 'string'
},
'CandidateMetrics': [
{
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
'Value': ...,
'Set': 'Train'|'Validation'|'Test',
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss'
},
]
},
'InferenceContainerDefinitions': {
'string': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
]
}
},
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'GeneratingExplainabilityReport'|'TrainingModels'|'PreTraining',
'AutoMLJobArtifacts': {
'CandidateDefinitionNotebookLocation': 'string',
'DataExplorationNotebookLocation': 'string'
},
'ResolvedAttributes': {
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'AutoMLProblemTypeResolvedAttributes': {
'TabularResolvedAttributes': {
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression'
},
'TextGenerationResolvedAttributes': {
'BaseModelName': 'string'
}
}
},
'ModelDeployConfig': {
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
'ModelDeployResult': {
'EndpointName': 'string'
},
'DataSplitConfig': {
'ValidationFraction': ...
},
'SecurityConfig': {
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'AutoMLComputeConfig': {
'EmrServerlessComputeConfig': {
'ExecutionRoleARN': 'string'
}
}
}
Response Structure
(dict) --
AutoMLJobName (string) --
Returns the name of the AutoML job V2.
AutoMLJobArn (string) --
Returns the Amazon Resource Name (ARN) of the AutoML job V2.
AutoMLJobInputDataConfig (list) --
Returns an array of channel objects describing the input data and their location.
(dict) --
A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).
ChannelType (string) --
The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType
Note
The type of channel defaults to training for the time-series forecasting problem type.
ContentType (string) --
The content type of the data from the input source. The following are the allowed content types for different problems:
For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For image classification: image/png , image/jpeg , or image/* . The default value is image/* .
For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
CompressionType (string) --
The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .
DataSource (dict) --
The data source for an AutoML channel (Required).
S3DataSource (dict) --
The Amazon S3 location of the input data.
S3DataType (string) --
The data type.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
S3Uri (string) --
The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
OutputDataConfig (dict) --
Returns the job's output data config.
KmsKeyId (string) --
The Key Management Service encryption key ID.
S3OutputPath (string) --
The Amazon S3 output path. Must be 512 characters or less.
RoleArn (string) --
The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
AutoMLJobObjective (dict) --
Returns the job's objective.
MetricName (string) --
The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
For tabular problem types:
List of available metrics:
Regression: MAE , MSE , R2 , RMSE
Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , Precision , Recall
Multiclass classification: Accuracy , BalancedAccuracy , F1macro , PrecisionMacro , RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
Default objective metrics:
Regression: MSE .
Binary classification: F1 .
Multiclass classification: Accuracy .
For image or text classification problem types:
List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification.
Default objective metrics: Accuracy
For time-series forecasting problem types:
List of available metrics: RMSE , wQL , Average wQL , MASE , MAPE , WAPE For a description of each metric, see Autopilot metrics for time-series forecasting.
Default objective metrics: AverageWeightedQuantileLoss
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
AutoMLProblemTypeConfig (dict) --
Returns the configuration settings of the problem type set for the AutoML job V2.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: ImageClassificationJobConfig, TextClassificationJobConfig, TimeSeriesForecastingJobConfig, TabularJobConfig, TextGenerationJobConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
ImageClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the image classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
TextClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ContentColumn (string) --
The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
TargetLabelColumn (string) --
The name of the column used to provide the class labels. It should not be same as the content column.
TimeSeriesForecastingJobConfig (dict) --
Settings used to configure an AutoML job V2 for the time-series forecasting problem type.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .
You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types: numeric , categorical , text , and datetime .
Note
These column keys must not include any column set in TimeSeriesConfig .
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ForecastFrequency (string) --
The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .
The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
ForecastHorizon (integer) --
The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
ForecastQuantiles (list) --
The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.
(string) --
Transformations (dict) --
The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
Filling (dict) --
A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.
The supported filling methods and their corresponding options are:
frontfill : none (Supported only for target column)
middlefill : zero , value , median , mean , min , max
backfill : zero , value , median , mean , min , max
futurefill : zero , value , median , mean , min , max
To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .
(string) --
(dict) --
(string) --
(string) --
Aggregation (dict) --
A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
The supported aggregation methods are sum (default), avg , first , min , max .
Note
Aggregation is only supported for the target column.
(string) --
(string) --
TimeSeriesConfig (dict) --
The collection of components that defines the time-series.
TargetAttributeName (string) --
The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
TimestampAttributeName (string) --
The name of the column indicating a point in time at which the target value of a given item is recorded.
ItemIdentifierAttributeName (string) --
The name of the column that represents the set of item identifiers for which you want to predict the target value.
GroupingAttributeNames (list) --
A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
(string) --
HolidayConfig (list) --
The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
(dict) --
Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.
CountryCode (string) --
The country code for the holiday calendar.
For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.
CandidateGenerationConfig (dict) --
Stores the configuration information for how model candidates are generated using an AutoML job V2.
AlgorithmsConfig (list) --
Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig stores the customized selection of algorithms to train on your data.
For the tabular problem type TabularJobConfig ,the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode.
AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO .
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
For the time-series forecasting problem type TimeSeriesForecastingJobConfig ,choose your algorithms from the list provided in AlgorithmConfig. For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
(dict) --
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithms (list) --
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
For the tabular problem type TabularJobConfig :
Note
Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode ( ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
For the time-series forecasting problem type TimeSeriesForecastingJobConfig :
Choose your algorithms from this list.
"cnn-qr"
"deepar"
"prophet"
"arima"
"npts"
"ets"
(string) --
TabularJobConfig (dict) --
Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
CandidateGenerationConfig (dict) --
The configuration information of how model candidates are generated.
AlgorithmsConfig (list) --
Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig stores the customized selection of algorithms to train on your data.
For the tabular problem type TabularJobConfig ,the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode.
AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO .
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
For the time-series forecasting problem type TimeSeriesForecastingJobConfig ,choose your algorithms from the list provided in AlgorithmConfig. For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.
(dict) --
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithms (list) --
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
For the tabular problem type TabularJobConfig :
Note
Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode ( ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
For the time-series forecasting problem type TimeSeriesForecastingJobConfig :
Choose your algorithms from this list.
"cnn-qr"
"deepar"
"prophet"
"arima"
"npts"
"ets"
(string) --
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note
These column keys may not include the target column.
In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .
If only FeatureDataTypes is provided, the column keys ( col1 , col2 ,..) should be a subset of the column names in the input data.
If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .
The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
Mode (string) --
The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
GenerateCandidateDefinitionsOnly (boolean) --
Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
ProblemType (string) --
The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.
Note
You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
TargetAttributeName (string) --
The name of the target variable in supervised learning, usually represented by 'y'.
SampleWeightAttributeName (string) --
If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
TextGenerationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.
Note
The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.
CompletionCriteria (dict) --
How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
BaseModelName (string) --
The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct .
TextGenerationHyperParameters (dict) --
The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.
"epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".
"batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".
"learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".
"learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".
Here is an example where all four hyperparameters are configured.
{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }
(string) --
(string) --
ModelAccessConfig (dict) --
The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig .
If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.
If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AutoMLProblemTypeConfigName (string) --
Returns the name of the problem type configuration set for the AutoML job V2.
CreationTime (datetime) --
Returns the creation time of the AutoML job V2.
EndTime (datetime) --
Returns the end time of the AutoML job V2.
LastModifiedTime (datetime) --
Returns the job's last modified time.
FailureReason (string) --
Returns the reason for the failure of the AutoML job V2, when applicable.
PartialFailureReasons (list) --
Returns a list of reasons for partial failures within an AutoML job V2.
(dict) --
The reason for a partial failure of an AutoML job.
PartialFailureMessage (string) --
The message containing the reason for a partial failure of an AutoML job.
BestCandidate (dict) --
Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.
CandidateName (string) --
The name of the candidate.
FinalAutoMLJobObjectiveMetric (dict) --
The best candidate result from an AutoML training job.
Type (string) --
The type of metric with the best result.
MetricName (string) --
The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
Value (float) --
The value of the metric with the best result.
StandardMetricName (string) --
The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.
ObjectiveStatus (string) --
The objective's status.
CandidateSteps (list) --
Information about the candidate's steps.
(dict) --
Information about the steps for a candidate and what step it is working on.
CandidateStepType (string) --
Whether the candidate is at the transform, training, or processing step.
CandidateStepArn (string) --
The ARN for the candidate's step.
CandidateStepName (string) --
The name for the candidate's step.
CandidateStatus (string) --
The candidate's status.
InferenceContainers (list) --
Information about the recommended inference container definitions.
(dict) --
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition.
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl (string) --
The location of the model artifacts. For more information, see ContainerDefinition.
Environment (dict) --
The environment variables to set in the container. For more information, see ContainerDefinition.
(string) --
(string) --
CreationTime (datetime) --
The creation time.
EndTime (datetime) --
The end time.
LastModifiedTime (datetime) --
The last modified time.
FailureReason (string) --
The failure reason.
CandidateProperties (dict) --
The properties of an AutoML candidate job.
CandidateArtifactLocations (dict) --
The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
Explainability (string) --
The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
ModelInsights (string) --
The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
BacktestResults (string) --
The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
CandidateMetrics (list) --
Information about the candidate metrics for an AutoML job.
(dict) --
Information about the metric for a candidate produced by an AutoML job.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Set (string) --
The dataset split from which the AutoML job produced the metric.
StandardMetricName (string) --
The name of the standard metric.
Note
For definitions of the standard metrics, see Autopilot candidate metrics.
InferenceContainerDefinitions (dict) --
The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2 ) related to image or text classification problem types only.
(string) --
Processing unit for an inference container. Currently Autopilot only supports CPU or GPU .
(list) --
Information about the recommended inference container definitions.
(dict) --
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition.
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl (string) --
The location of the model artifacts. For more information, see ContainerDefinition.
Environment (dict) --
The environment variables to set in the container. For more information, see ContainerDefinition.
(string) --
(string) --
AutoMLJobStatus (string) --
Returns the status of the AutoML job V2.
AutoMLJobSecondaryStatus (string) --
Returns the secondary status of the AutoML job V2.
AutoMLJobArtifacts (dict) --
The artifacts that are generated during an AutoML job.
CandidateDefinitionNotebookLocation (string) --
The URL of the notebook location.
DataExplorationNotebookLocation (string) --
The URL of the notebook location.
ResolvedAttributes (dict) --
Returns the resolved attributes used by the AutoML job V2.
AutoMLJobObjective (dict) --
Specifies a metric to minimize or maximize as the objective of an AutoML job.
MetricName (string) --
The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
For tabular problem types:
List of available metrics:
Regression: MAE , MSE , R2 , RMSE
Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , Precision , Recall
Multiclass classification: Accuracy , BalancedAccuracy , F1macro , PrecisionMacro , RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
Default objective metrics:
Regression: MSE .
Binary classification: F1 .
Multiclass classification: Accuracy .
For image or text classification problem types:
List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification.
Default objective metrics: Accuracy
For time-series forecasting problem types:
List of available metrics: RMSE , wQL , Average wQL , MASE , MAPE , WAPE For a description of each metric, see Autopilot metrics for time-series forecasting.
Default objective metrics: AverageWeightedQuantileLoss
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
AutoMLProblemTypeResolvedAttributes (dict) --
Defines the resolved attributes specific to a problem type.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: TabularResolvedAttributes, TextGenerationResolvedAttributes. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
TabularResolvedAttributes (dict) --
The resolved attributes for the tabular problem type.
ProblemType (string) --
The type of supervised learning problem available for the model candidates of the AutoML job V2 (Binary Classification, Multiclass Classification, Regression). For more information, see SageMaker Autopilot problem types.
TextGenerationResolvedAttributes (dict) --
The resolved attributes for the text generation problem type.
BaseModelName (string) --
The name of the base model to fine-tune.
ModelDeployConfig (dict) --
Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
AutoGenerateEndpointName (boolean) --
Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .
Note
If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note
Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.
ModelDeployResult (dict) --
Provides information about endpoint for the model deployment.
EndpointName (string) --
The name of the endpoint to which the model has been deployed.
Note
If model deployment fails, this field is omitted from the response.
DataSplitConfig (dict) --
Returns the configuration settings of how the data are split into train and validation datasets.
ValidationFraction (float) --
The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
SecurityConfig (dict) --
Returns the security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId (string) --
The key used to encrypt stored data.
EnableInterContainerTrafficEncryption (boolean) --
Whether to use traffic encryption between the container layers.
VpcConfig (dict) --
The VPC configuration.
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
(string) --
AutoMLComputeConfig (dict) --
The compute configuration used for the AutoML job V2.
EmrServerlessComputeConfig (dict) --
The configuration for using EMR Serverless to run the AutoML job V2.
To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request.
By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.
EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.
ExecutionRoleARN (string) --
The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see "How to configure AutoML to initiate a remote job on EMR Serverless for large datasets" in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.
{'DefaultUserSettings': {'CanvasAppSettings': {'EmrServerlessSettings': {'ExecutionRoleArn': 'string',
'Status': 'ENABLED '
'| '
'DISABLED'}},
'StudioWebPortalSettings': {'HiddenMlTools': {'InferenceOptimization'}}}}
The description of the domain.
See also: AWS API Documentation
Request Syntax
client.describe_domain(
DomainId='string'
)
string
[REQUIRED]
The domain ID.
dict
Response Syntax
{
'DomainArn': 'string',
'DomainId': 'string',
'DomainName': 'string',
'HomeEfsFileSystemId': 'string',
'SingleSignOnManagedApplicationInstanceId': 'string',
'SingleSignOnApplicationArn': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'SecurityGroupIdForDomainBoundary': 'string',
'AuthMode': 'SSO'|'IAM',
'DefaultUserSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
}
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
}
},
]
}
}
Response Structure
(dict) --
DomainArn (string) --
The domain's Amazon Resource Name (ARN).
DomainId (string) --
The domain ID.
DomainName (string) --
The domain name.
HomeEfsFileSystemId (string) --
The ID of the Amazon Elastic File System managed by this Domain.
SingleSignOnManagedApplicationInstanceId (string) --
The IAM Identity Center managed application instance ID.
SingleSignOnApplicationArn (string) --
The ARN of the application managed by SageMaker in IAM Identity Center. This value is only returned for domains created after October 1, 2023.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
LastModifiedTime (datetime) --
The last modified time.
FailureReason (string) --
The failure reason.
SecurityGroupIdForDomainBoundary (string) --
The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.
AuthMode (string) --
The domain's authentication mode.
DefaultUserSettings (dict) --
Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.
ExecutionRole (string) --
The execution role for the user.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
S3OutputPath (string) --
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) --
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) --
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .
app:JupyterServer: : Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) --
The POSIX user ID.
Gid (integer) --
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: EFSFileSystemConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) --
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
DomainSettings (dict) --
A collection of Domain settings.
SecurityGroupIds (list) --
The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
(string) --
RStudioServerProDomainSettings (dict) --
A collection of settings that configure the RStudioServerPro Domain-level app.
DomainExecutionRoleArn (string) --
The ARN of the execution role for the RStudioServerPro Domain-level app.
RStudioConnectUrl (string) --
A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl (string) --
A URL pointing to an RStudio Package Manager server.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.
DockerSettings (dict) --
A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess (string) --
Indicates whether the domain can access Docker.
VpcOnlyTrustedAccounts (list) --
The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
(string) --
AmazonQSettings (dict) --
A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO .
Status (string) --
Whether Amazon Q has been enabled within the domain.
QProfileArn (string) --
The ARN of the Amazon Q profile used within the domain.
AppNetworkAccessType (string) --
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly .
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access
VpcOnly - All traffic is through the specified VPC and subnets
HomeEfsFileSystemKmsKeyId (string) --
Use KmsKeyId .
SubnetIds (list) --
The VPC subnets that the domain uses for communication.
(string) --
Url (string) --
The domain's URL.
VpcId (string) --
The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
KmsKeyId (string) --
The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.
AppSecurityGroupManagement (string) --
The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
DefaultSpaceSettings (dict) --
The default settings used to create a space.
ExecutionRole (string) --
The ARN of the execution role for the space.
SecurityGroups (list) --
The security group IDs for the Amazon VPC that the space uses for communication.
(string) --
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The default storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) --
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) --
The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) --
The POSIX user ID.
Gid (integer) --
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: EFSFileSystemConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) --
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
{'UserSettings': {'CanvasAppSettings': {'EmrServerlessSettings': {'ExecutionRoleArn': 'string',
'Status': 'ENABLED '
'| '
'DISABLED'}},
'StudioWebPortalSettings': {'HiddenMlTools': {'InferenceOptimization'}}}}
Describes a user profile. For more information, see CreateUserProfile .
See also: AWS API Documentation
Request Syntax
client.describe_user_profile(
DomainId='string',
UserProfileName='string'
)
string
[REQUIRED]
The domain ID.
string
[REQUIRED]
The user profile name. This value is not case sensitive.
dict
Response Syntax
{
'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string',
'HomeEfsFileSystemUid': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'SingleSignOnUserIdentifier': 'string',
'SingleSignOnUserValue': 'string',
'UserSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
}
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
]
}
}
}
Response Structure
(dict) --
DomainId (string) --
The ID of the domain that contains the profile.
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
UserProfileName (string) --
The user profile name.
HomeEfsFileSystemUid (string) --
The ID of the user's profile in the Amazon Elastic File System volume.
Status (string) --
The status.
LastModifiedTime (datetime) --
The last modified time.
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
SingleSignOnUserIdentifier (string) --
The IAM Identity Center user identifier.
SingleSignOnUserValue (string) --
The IAM Identity Center user value.
UserSettings (dict) --
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
S3OutputPath (string) --
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) --
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) --
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .
app:JupyterServer: : Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) --
The POSIX user ID.
Gid (integer) --
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: EFSFileSystemConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) --
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
{'DefaultUserSettings': {'CanvasAppSettings': {'EmrServerlessSettings': {'ExecutionRoleArn': 'string',
'Status': 'ENABLED '
'| '
'DISABLED'}},
'StudioWebPortalSettings': {'HiddenMlTools': {'InferenceOptimization'}}}}
Updates the default settings for new user profiles in the domain.
See also: AWS API Documentation
Request Syntax
client.update_domain(
DomainId='string',
DefaultUserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
}
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
}
},
]
},
SubnetIds=[
'string',
],
AppNetworkAccessType='PublicInternetOnly'|'VpcOnly'
)
string
[REQUIRED]
The ID of the domain to be updated.
dict
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
S3OutputPath (string) --
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .
app:JupyterServer: : Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
dict
A collection of DomainSettings configuration values to update.
RStudioServerProDomainSettingsForUpdate (dict) --
A collection of RStudioServerPro Domain-level app settings to update. A single RStudioServerPro application is created for a domain.
DomainExecutionRoleArn (string) -- [REQUIRED]
The execution role for the RStudioServerPro Domain-level app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioConnectUrl (string) --
A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl (string) --
A URL pointing to an RStudio Package Manager server.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key. This configuration can only be modified if there are no apps in the InService or Pending state.
SecurityGroupIds (list) --
The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
(string) --
DockerSettings (dict) --
A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess (string) --
Indicates whether the domain can access Docker.
VpcOnlyTrustedAccounts (list) --
The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
(string) --
AmazonQSettings (dict) --
A collection of settings that configure the Amazon Q experience within the domain.
Status (string) --
Whether Amazon Q has been enabled within the domain.
QProfileArn (string) --
The ARN of the Amazon Q profile used within the domain.
string
The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service .
dict
The default settings used to create a space within the domain.
ExecutionRole (string) --
The ARN of the execution role for the space.
SecurityGroups (list) --
The security group IDs for the Amazon VPC that the space uses for communication.
(string) --
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The default storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
list
The VPC subnets that Studio uses for communication.
If removing subnets, ensure there are no apps in the InService , Pending , or Deleting state.
(string) --
string
Specifies the VPC used for non-EFS traffic.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access.
VpcOnly - All Studio traffic is through the specified VPC and subnets.
This configuration can only be modified if there are no apps in the InService , Pending , or Deleting state. The configuration cannot be updated if DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is already set or DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided as part of the same request.
dict
Response Syntax
{
'DomainArn': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the domain.
{'UserSettings': {'CanvasAppSettings': {'EmrServerlessSettings': {'ExecutionRoleArn': 'string',
'Status': 'ENABLED '
'| '
'DISABLED'}},
'StudioWebPortalSettings': {'HiddenMlTools': {'InferenceOptimization'}}}}
Updates a user profile.
See also: AWS API Documentation
Request Syntax
client.update_user_profile(
DomainId='string',
UserProfileName='string',
UserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
},
'ModelRegisterSettings': {
'Status': 'ENABLED'|'DISABLED',
'CrossAccountModelRegisterRoleArn': 'string'
},
'WorkspaceSettings': {
'S3ArtifactPath': 'string',
'S3KmsKeyId': 'string'
},
'IdentityProviderOAuthSettings': [
{
'DataSourceName': 'SalesforceGenie'|'Snowflake',
'Status': 'ENABLED'|'DISABLED',
'SecretArn': 'string'
},
],
'DirectDeploySettings': {
'Status': 'ENABLED'|'DISABLED'
},
'KendraSettings': {
'Status': 'ENABLED'|'DISABLED'
},
'GenerativeAiSettings': {
'AmazonBedrockRoleArn': 'string'
},
'EmrServerlessSettings': {
'ExecutionRoleArn': 'string',
'Status': 'ENABLED'|'DISABLED'
}
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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.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.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'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.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'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.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
}
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
]
}
}
)
string
[REQUIRED]
The domain ID.
string
[REQUIRED]
The user profile name.
dict
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
S3OutputPath (string) --
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
SpaceStorageSettings (dict) --
The storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio:: : Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED .
app:JupyterServer: : Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED , the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).