2023/03/23 - Amazon SageMaker Service - 2 new15 updated api methods
Changes Amazon SageMaker Autopilot adds two new APIs - CreateAutoMLJobV2 and DescribeAutoMLJobV2. Amazon SageMaker Notebook Instances now supports the ml.geospatial.interactive instance type.
Creates an Amazon SageMaker AutoML job that uses non-tabular data such as images or text for Computer Vision or Natural Language Processing problems.
Find the resulting model after you run an AutoML job V2 by calling .
To create an AutoMLJob using tabular data, see .
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'
}
},
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'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
ModelDeployConfig={
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
DataSplitConfig={
'ValidationFraction': ...
}
)
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 InputDataConfig supported by CreateAutoMLJob. The supported formats depend on the problem type:
ImageClassification: S3Prefix, ManifestFile, AugmentedManifestFile
TextClassification: S3Prefix
(dict) --
A channel is a named input source that training algorithms can consume. This channel is used for the non tabular training data of an AutoML job using the V2 API. For tabular training data, see . For more information, see .
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
ContentType (string) --
The content type of the data from the input source. The following are the allowed content types for different problems:
ImageClassification: image/png, image/jpeg, image/*
TextClassification: text/csv;header=present
CompressionType (string) --
The allowed compression types depend on the input format. We allow the compression type Gzip for S3Prefix inputs 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.
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 (KMS) encryption key ID.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 output path. Must be 128 characters or less.
dict
[REQUIRED]
Defines the configuration settings of one of the supported problem types.
ImageClassificationJobConfig (dict) --
Settings used to configure an AutoML job using the V2 API 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 V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
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 used by the action.
For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
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 using the V2 API 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 V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
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 used by the action.
For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
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.
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. For , only Accuracy is supported.
MetricName (string) -- [REQUIRED]
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
Accuracy
The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.
AUC
The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.
The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives, but more false negatives.
AUC is the area under this ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.
BalancedAccuracy
BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.
F1
The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class, and false when they do not.
Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision measures the quality of the prediction when it predicts the positive class.
Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances. Recall measures how completely a model predicts the actual class members in a dataset.
F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
F1macro
The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
MAE
The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they're averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.
MSE
The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive. The better a model is at predicting the actual values, the smaller the MSE value is
Precision
Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.
PrecisionMacro
The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model's ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.
R2
R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.
Recall
Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.
Recall is important when testing for cancer because it's used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yield a perfect recall score.
RecallMacro
The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model's ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yields a perfect recall score.
RMSE
Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it's averaged over all values. It is used in regression analysis to understand model prediction error. It's an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
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.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
dict
This structure specifies how to split the data into train and validation datasets.
If you are using the V1 API (for example CreateAutoMLJob) or the V2 API for Natural Language Processing problems (for example CreateAutoMLJobV2 with a TextClassificationJobConfig problem type), the validation and training datasets must contain the same headers. Also, for V1 API jobs, the validation dataset must be less than 2 GB in size.
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
Response Syntax
{
'AutoMLJobArn': 'string'
}
Response Structure
(dict) --
AutoMLJobArn (string) --
The unique ARN assigned to the AutoMLJob when it is created.
Returns information about an Amazon SageMaker AutoML V2 job.
See also: AWS API Documentation
Request Syntax
client.describe_auto_ml_job_v2(
AutoMLJobName='string'
)
string
[REQUIRED]
Requests information about an AutoML V2 job 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'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
'AutoMLProblemTypeConfig': {
'ImageClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
}
},
'TextClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ContentColumn': 'string',
'TargetLabelColumn': 'string'
}
},
'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'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
'Value': ...,
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
'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'
},
'CandidateMetrics': [
{
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
'Value': ...,
'Set': 'Train'|'Validation'|'Test',
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'
},
]
},
'InferenceContainerDefinitions': {
'string': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
]
}
},
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'GeneratingExplainabilityReport'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'TrainingModels',
'ModelDeployConfig': {
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
'ModelDeployResult': {
'EndpointName': 'string'
},
'DataSplitConfig': {
'ValidationFraction': ...
},
'SecurityConfig': {
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
}
}
Response Structure
(dict) --
AutoMLJobName (string) --
Returns the name of the AutoML V2 job.
AutoMLJobArn (string) --
Returns the Amazon Resource Name (ARN) of the AutoML V2 job.
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 the non tabular training data of an AutoML job using the V2 API. For tabular training data, see . For more information, see .
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
ContentType (string) --
The content type of the data from the input source. The following are the allowed content types for different problems:
ImageClassification: image/png, image/jpeg, image/*
TextClassification: text/csv;header=present
CompressionType (string) --
The allowed compression types depend on the input format. We allow the compression type Gzip for S3Prefix inputs 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.
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 (KMS) encryption key ID.
S3OutputPath (string) --
The Amazon S3 output path. Must be 128 characters or less.
RoleArn (string) --
The ARN of the Identity and Access Management 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. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
Accuracy
The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.
AUC
The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.
The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives, but more false negatives.
AUC is the area under this ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.
BalancedAccuracy
BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.
F1
The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class, and false when they do not.
Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision measures the quality of the prediction when it predicts the positive class.
Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances. Recall measures how completely a model predicts the actual class members in a dataset.
F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
F1macro
The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
MAE
The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they're averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.
MSE
The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive. The better a model is at predicting the actual values, the smaller the MSE value is
Precision
Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.
PrecisionMacro
The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model's ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.
R2
R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.
Recall
Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.
Recall is important when testing for cancer because it's used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yield a perfect recall score.
RecallMacro
The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model's ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yields a perfect recall score.
RMSE
Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it's averaged over all values. It is used in regression analysis to understand model prediction error. It's an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
AutoMLProblemTypeConfig (dict) --
Returns the configuration settings of the problem type set for the AutoML V2 job.
ImageClassificationJobConfig (dict) --
Settings used to configure an AutoML job using the V2 API 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 V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
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 used by the action.
For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
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 using the V2 API 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 V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
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 used by the action.
For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
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.
CreationTime (datetime) --
Returns the creation time of the AutoML V2 job.
EndTime (datetime) --
Returns the end time of the AutoML V2 job.
LastModifiedTime (datetime) --
Returns the job's last modified time.
FailureReason (string) --
Returns the reason for the failure of the AutoML V2 job, when applicable.
PartialFailureReasons (list) --
Returns a list of reasons for partial failures within an AutoML V2 job.
(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 .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
(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.
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.
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 V2 API only (for example, for jobs created by calling CreateAutoMLJobV2).
(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 .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
(string) --
(string) --
AutoMLJobStatus (string) --
Returns the status of the AutoML V2 job.
AutoMLJobSecondaryStatus (string) --
Returns the secondary status of the AutoML V2 job.
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.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
ModelDeployResult (dict) --
Provides information about endpoint for the model deployment.
EndpointName (string) --
The name of the endpoint to which the model has been deployed.
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) --
{'ResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
See also: AWS API Documentation
Request Syntax
client.create_app(
DomainId='string',
UserProfileName='string',
AppType='JupyterServer'|'KernelGateway'|'TensorBoard'|'RStudioServerPro'|'RSessionGateway',
AppName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ResourceSpec={
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
SpaceName='string'
)
string
[REQUIRED]
The domain ID.
string
The user profile name. If this value is not set, then SpaceName must be set.
string
[REQUIRED]
The type of app.
string
[REQUIRED]
The name of the app.
list
Each tag consists of a key and an optional value. 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 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.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
string
The name of the space. If this value is not set, then UserProfileName must be set.
dict
Response Syntax
{
'AppArn': 'string'
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
{'InputDataConfig': {'DataSource': {'S3DataSource': {'S3DataType': {'AugmentedManifestFile'}}}}}
Creates an Autopilot job.
Find the best-performing model after you run an Autopilot job by calling .
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
See also: AWS API Documentation
Request Syntax
client.create_auto_ml_job(
AutoMLJobName='string',
InputDataConfig=[
{
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'CompressionType': 'None'|'Gzip',
'TargetAttributeName': 'string',
'ContentType': 'string',
'ChannelType': 'training'|'validation'
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
ProblemType='BinaryClassification'|'MulticlassClassification'|'Regression',
AutoMLJobObjective={
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
AutoMLJobConfig={
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'SecurityConfig': {
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'DataSplitConfig': {
'ValidationFraction': ...
},
'CandidateGenerationConfig': {
'FeatureSpecificationS3Uri': 'string',
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
]
},
]
},
'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING'
},
RoleArn='string',
GenerateCandidateDefinitionsOnly=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ModelDeployConfig={
'AutoGenerateEndpointName': True|False,
'EndpointName': '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 that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.
(dict) --
A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see .
DataSource (dict) -- [REQUIRED]
The data source for an AutoML channel.
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.
CompressionType (string) --
You can use Gzip or None. The default value is None.
TargetAttributeName (string) -- [REQUIRED]
The name of the target variable in supervised learning, usually represented by 'y'.
ContentType (string) --
The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
ChannelType (string) --
The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
dict
[REQUIRED]
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
KmsKeyId (string) --
The Key Management Service (KMS) encryption key ID.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 output path. Must be 128 characters or less.
string
Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.
dict
Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. For , only Accuracy is supported.
MetricName (string) -- [REQUIRED]
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
Accuracy
The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.
AUC
The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.
The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives, but more false negatives.
AUC is the area under this ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.
BalancedAccuracy
BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.
F1
The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class, and false when they do not.
Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision measures the quality of the prediction when it predicts the positive class.
Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances. Recall measures how completely a model predicts the actual class members in a dataset.
F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
F1macro
The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
MAE
The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they're averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.
MSE
The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive. The better a model is at predicting the actual values, the smaller the MSE value is
Precision
Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.
PrecisionMacro
The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model's ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.
R2
R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.
Recall
Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.
Recall is important when testing for cancer because it's used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yield a perfect recall score.
RecallMacro
The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model's ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yields a perfect recall score.
RMSE
Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it's averaged over all values. It is used in regression analysis to understand model prediction error. It's an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
dict
A collection of settings used to configure an AutoML job.
CompletionCriteria (dict) --
How long an AutoML 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 V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
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 used by the action.
For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
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.
SecurityConfig (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) --
DataSplitConfig (dict) --
The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
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.
CandidateGenerationConfig (dict) --
The configuration for generating a candidate for an AutoML job (optional).
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. 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" ... } }
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.
AlgorithmsConfig (list) --
Stores the configuration information for the selection of algorithms used to train the model candidates.
The list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode.
AlgorithmsConfig should not be set in AUTO training mode.
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, AutoMLCandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, AutoMLCandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see .
For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
(dict) --
The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.
AutoMLAlgorithms (list) -- [REQUIRED]
The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
(string) --
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.
string
[REQUIRED]
The ARN of the role that is used to access the data.
boolean
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, 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
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.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
dict
Response Syntax
{
'AutoMLJobArn': 'string'
}
Response Structure
(dict) --
AutoMLJobArn (string) --
The unique ARN assigned to the AutoML job when it is created.
{'DefaultSpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}},
'DefaultUserSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'RSessionAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}},
'DomainSettings': {'RStudioServerProDomainSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) 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 SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. 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 Studio 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 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.
For more information, see Connect 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',
'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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
}
}
},
SubnetIds=[
'string',
],
VpcId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
AppNetworkAccessType='PublicInternetOnly'|'VpcOnly',
HomeEfsFileSystemKmsKeyId='string',
KmsKeyId='string',
AppSecurityGroupManagement='Service'|'Customer',
DomainSettings={
'SecurityGroupIds': [
'string',
],
'RStudioServerProDomainSettings': {
'DomainExecutionRoleArn': 'string',
'RStudioConnectUrl': 'string',
'RStudioPackageManagerUrl': 'string',
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED'
},
DefaultSpaceSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'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 Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly.
Amazon SageMaker adds a security group to allow NFS traffic from 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 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
InstanceType (string) --
The instance type that the image version runs on.
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.
InstanceType (string) --
The instance type that the image version runs on.
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 Canvas app.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas app.
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 app. 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.
list
[REQUIRED]
The VPC subnets that Studio uses for communication.
(string) --
string
[REQUIRED]
The ID of the Amazon Virtual Private Cloud (VPC) that Studio 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 Studio traffic is through the specified VPC and subnets
string
Use KmsKeyId.
string
SageMaker uses 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, 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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
dict
The default settings used to create a space.
ExecutionRole (string) --
The execution role for the space.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(string) --
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.
{'SpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
Creates a space used for real time collaboration in a Domain.
See also: AWS API Documentation
Request Syntax
client.create_space(
DomainId='string',
SpaceName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
SpaceSettings={
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
}
}
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
The name of the space.
list
Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the Search API.
(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 space settings.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(string) --
dict
Response Syntax
{
'SpaceArn': 'string'
}
Response Structure
(dict) --
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
{'UserSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'RSessionAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
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 Amazon SageMaker Studio. 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 (EFS) 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',
'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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
}
}
}
)
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 Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly.
Amazon SageMaker adds a security group to allow NFS traffic from 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 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
InstanceType (string) --
The instance type that the image version runs on.
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.
InstanceType (string) --
The instance type that the image version runs on.
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 Canvas app.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas app.
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 app. 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.
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
{'ResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}
Describes the app.
See also: AWS API Documentation
Request Syntax
client.describe_app(
DomainId='string',
UserProfileName='string',
AppType='JupyterServer'|'KernelGateway'|'TensorBoard'|'RStudioServerPro'|'RSessionGateway',
AppName='string',
SpaceName='string'
)
string
[REQUIRED]
The domain ID.
string
The user profile name. If this value is not set, then SpaceName must be set.
string
[REQUIRED]
The type of app.
string
[REQUIRED]
The name of the app.
string
The name of the space.
dict
Response Syntax
{
'AppArn': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'TensorBoard'|'RStudioServerPro'|'RSessionGateway',
'AppName': 'string',
'DomainId': 'string',
'UserProfileName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'LastHealthCheckTimestamp': datetime(2015, 1, 1),
'LastUserActivityTimestamp': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'SpaceName': 'string'
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
Status (string) --
The status.
LastHealthCheckTimestamp (datetime) --
The timestamp of the last health check.
LastUserActivityTimestamp (datetime) --
The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp.
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
ResourceSpec (dict) --
The 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.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
SpaceName (string) --
The name of the space. If this value is not set, then UserProfileName must be set.
{'AutoMLJobSecondaryStatus': {'TrainingModels'},
'BestCandidate': {'InferenceContainerDefinitions': {'CPU | GPU': [{'Environment': {'string': 'string'},
'Image': 'string',
'ModelDataUrl': 'string'}]}},
'InputDataConfig': {'DataSource': {'S3DataSource': {'S3DataType': {'AugmentedManifestFile'}}}}}
Returns information about an Amazon SageMaker AutoML job.
See also: AWS API Documentation
Request Syntax
client.describe_auto_ml_job(
AutoMLJobName='string'
)
string
[REQUIRED]
Requests information about an AutoML job using its unique name.
dict
Response Syntax
{
'AutoMLJobName': 'string',
'AutoMLJobArn': 'string',
'InputDataConfig': [
{
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'CompressionType': 'None'|'Gzip',
'TargetAttributeName': 'string',
'ContentType': 'string',
'ChannelType': 'training'|'validation'
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'RoleArn': 'string',
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'AutoMLJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'SecurityConfig': {
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'DataSplitConfig': {
'ValidationFraction': ...
},
'CandidateGenerationConfig': {
'FeatureSpecificationS3Uri': 'string',
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
]
},
]
},
'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING'
},
'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'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
'Value': ...,
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
'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'
},
'CandidateMetrics': [
{
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
'Value': ...,
'Set': 'Train'|'Validation'|'Test',
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'
},
]
},
'InferenceContainerDefinitions': {
'string': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
]
}
},
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'GeneratingExplainabilityReport'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'TrainingModels',
'GenerateCandidateDefinitionsOnly': True|False,
'AutoMLJobArtifacts': {
'CandidateDefinitionNotebookLocation': 'string',
'DataExplorationNotebookLocation': 'string'
},
'ResolvedAttributes': {
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
}
},
'ModelDeployConfig': {
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
'ModelDeployResult': {
'EndpointName': 'string'
}
}
Response Structure
(dict) --
AutoMLJobName (string) --
Returns the name of the AutoML job.
AutoMLJobArn (string) --
Returns the ARN of the AutoML job.
InputDataConfig (list) --
Returns the input data configuration for the AutoML job.
(dict) --
A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see .
DataSource (dict) --
The data source for an AutoML channel.
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.
CompressionType (string) --
You can use Gzip or None. The default value is None.
TargetAttributeName (string) --
The name of the target variable in supervised learning, usually represented by 'y'.
ContentType (string) --
The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
ChannelType (string) --
The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
OutputDataConfig (dict) --
Returns the job's output data config.
KmsKeyId (string) --
The Key Management Service (KMS) encryption key ID.
S3OutputPath (string) --
The Amazon S3 output path. Must be 128 characters or less.
RoleArn (string) --
The Amazon Resource Name (ARN) of the Identity and Access Management (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. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
Accuracy
The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.
AUC
The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.
The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives, but more false negatives.
AUC is the area under this ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.
BalancedAccuracy
BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.
F1
The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class, and false when they do not.
Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision measures the quality of the prediction when it predicts the positive class.
Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances. Recall measures how completely a model predicts the actual class members in a dataset.
F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
F1macro
The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
MAE
The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they're averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.
MSE
The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive. The better a model is at predicting the actual values, the smaller the MSE value is
Precision
Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.
PrecisionMacro
The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model's ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.
R2
R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.
Recall
Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.
Recall is important when testing for cancer because it's used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yield a perfect recall score.
RecallMacro
The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model's ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yields a perfect recall score.
RMSE
Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it's averaged over all values. It is used in regression analysis to understand model prediction error. It's an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
ProblemType (string) --
Returns the job's problem type.
AutoMLJobConfig (dict) --
Returns the configuration for the AutoML job.
CompletionCriteria (dict) --
How long an AutoML 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 V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
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 used by the action.
For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
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.
SecurityConfig (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) --
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) --
DataSplitConfig (dict) --
The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
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.
CandidateGenerationConfig (dict) --
The configuration for generating a candidate for an AutoML job (optional).
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. 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" ... } }
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.
AlgorithmsConfig (list) --
Stores the configuration information for the selection of algorithms used to train the model candidates.
The list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode.
AlgorithmsConfig should not be set in AUTO training mode.
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, AutoMLCandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, AutoMLCandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see .
For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
(dict) --
The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.
AutoMLAlgorithms (list) --
The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
(string) --
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.
CreationTime (datetime) --
Returns the creation time of the AutoML job.
EndTime (datetime) --
Returns the end time of the AutoML job.
LastModifiedTime (datetime) --
Returns the job's last modified time.
FailureReason (string) --
Returns the failure reason for an AutoML job, when applicable.
PartialFailureReasons (list) --
Returns a list of reasons for partial failures within an AutoML job.
(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) --
The best model candidate selected by SageMaker Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.
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 .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
(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.
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.
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 V2 API only (for example, for jobs created by calling CreateAutoMLJobV2).
(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 .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
(string) --
(string) --
AutoMLJobStatus (string) --
Returns the status of the AutoML job.
AutoMLJobSecondaryStatus (string) --
Returns the secondary status of the AutoML job.
GenerateCandidateDefinitionsOnly (boolean) --
Indicates whether the output for an AutoML job generates candidate definitions only.
AutoMLJobArtifacts (dict) --
Returns information on the job's artifacts found in AutoMLJobArtifacts.
CandidateDefinitionNotebookLocation (string) --
The URL of the notebook location.
DataExplorationNotebookLocation (string) --
The URL of the notebook location.
ResolvedAttributes (dict) --
Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are auto-inferred. If you do provide them, the values used are the ones you provide.
AutoMLJobObjective (dict) --
Specifies a metric to minimize or maximize as the objective of a job. V2 API jobs (for example jobs created by calling CreateAutoMLJobV2), support Accuracy only.
MetricName (string) --
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
Accuracy
The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.
AUC
The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.
The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives, but more false negatives.
AUC is the area under this ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.
BalancedAccuracy
BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.
F1
The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class, and false when they do not.
Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision measures the quality of the prediction when it predicts the positive class.
Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances. Recall measures how completely a model predicts the actual class members in a dataset.
F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
F1macro
The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
MAE
The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they're averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.
MSE
The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive. The better a model is at predicting the actual values, the smaller the MSE value is
Precision
Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.
PrecisionMacro
The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model's ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.
R2
R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.
Recall
Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.
Recall is important when testing for cancer because it's used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yield a perfect recall score.
RecallMacro
The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model's ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yields a perfect recall score.
RMSE
Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it's averaged over all values. It is used in regression analysis to understand model prediction error. It's an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
ProblemType (string) --
The 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 V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
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 used by the action.
For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
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.
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.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
ModelDeployResult (dict) --
Provides information about endpoint for the model deployment.
EndpointName (string) --
The name of the endpoint to which the model has been deployed.
{'DefaultSpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}},
'DefaultUserSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'RSessionAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}},
'DomainSettings': {'RStudioServerProDomainSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
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',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'AuthMode': 'SSO'|'IAM',
'DefaultUserSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
}
}
},
'AppNetworkAccessType': 'PublicInternetOnly'|'VpcOnly',
'HomeEfsFileSystemKmsKeyId': 'string',
'SubnetIds': [
'string',
],
'Url': 'string',
'VpcId': 'string',
'KmsKeyId': 'string',
'DomainSettings': {
'SecurityGroupIds': [
'string',
],
'RStudioServerProDomainSettings': {
'DomainExecutionRoleArn': 'string',
'RStudioConnectUrl': 'string',
'RStudioPackageManagerUrl': 'string',
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED'
},
'AppSecurityGroupManagement': 'Service'|'Customer',
'SecurityGroupIdForDomainBoundary': 'string',
'DefaultSpaceSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'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 (EFS) managed by this Domain.
SingleSignOnManagedApplicationInstanceId (string) --
The IAM Identity Center managed application instance ID.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
LastModifiedTime (datetime) --
The last modified time.
FailureReason (string) --
The failure reason.
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 Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly.
Amazon SageMaker adds a security group to allow NFS traffic from 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 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
InstanceType (string) --
The instance type that the image version runs on.
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.
InstanceType (string) --
The instance type that the image version runs on.
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 Canvas app.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas app.
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 app. 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.
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 Studio traffic is through the specified VPC and subnets
HomeEfsFileSystemKmsKeyId (string) --
Use KmsKeyId.
SubnetIds (list) --
The VPC subnets that Studio uses for communication.
(string) --
Url (string) --
The domain's URL.
VpcId (string) --
The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
KmsKeyId (string) --
The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
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.
SecurityGroupIdForDomainBoundary (string) --
The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.
DefaultSpaceSettings (dict) --
The default settings used to create a space.
ExecutionRole (string) --
The execution role for the space.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(string) --
{'SpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
Describes the space.
See also: AWS API Documentation
Request Syntax
client.describe_space(
DomainId='string',
SpaceName='string'
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
The name of the space.
dict
Response Syntax
{
'DomainId': 'string',
'SpaceArn': 'string',
'SpaceName': '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',
'SpaceSettings': {
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
}
}
}
Response Structure
(dict) --
DomainId (string) --
The ID of the associated Domain.
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
SpaceName (string) --
The name of the space.
HomeEfsFileSystemUid (string) --
The ID of the space'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.
SpaceSettings (dict) --
A collection of space settings.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(string) --
{'UserSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'RSessionAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
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',
'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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
}
}
}
}
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 (EFS) 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 Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly.
Amazon SageMaker adds a security group to allow NFS traffic from 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 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
InstanceType (string) --
The instance type that the image version runs on.
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.
InstanceType (string) --
The instance type that the image version runs on.
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 Canvas app.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas app.
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 app. 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.
{'AutoMLJobSummaries': {'AutoMLJobSecondaryStatus': {'TrainingModels'}}}
Request a list of jobs.
See also: AWS API Documentation
Request Syntax
client.list_auto_ml_jobs(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
SortOrder='Ascending'|'Descending',
SortBy='Name'|'CreationTime'|'Status',
MaxResults=123,
NextToken='string'
)
datetime
Request a list of jobs, using a filter for time.
datetime
Request a list of jobs, using a filter for time.
datetime
Request a list of jobs, using a filter for time.
datetime
Request a list of jobs, using a filter for time.
string
Request a list of jobs, using a search filter for name.
string
Request a list of jobs, using a filter for status.
string
The sort order for the results. The default is Descending.
string
The parameter by which to sort the results. The default is Name.
integer
Request a list of jobs up to a specified limit.
string
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
dict
Response Syntax
{
'AutoMLJobSummaries': [
{
'AutoMLJobName': 'string',
'AutoMLJobArn': 'string',
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'GeneratingExplainabilityReport'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'TrainingModels',
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'PartialFailureReasons': [
{
'PartialFailureMessage': 'string'
},
]
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
AutoMLJobSummaries (list) --
Returns a summary list of jobs.
(dict) --
Provides a summary about an AutoML job.
AutoMLJobName (string) --
The name of the AutoML job you are requesting.
AutoMLJobArn (string) --
The ARN of the AutoML job.
AutoMLJobStatus (string) --
The status of the AutoML job.
AutoMLJobSecondaryStatus (string) --
The secondary status of the AutoML job.
CreationTime (datetime) --
When the AutoML job was created.
EndTime (datetime) --
The end time of an AutoML job.
LastModifiedTime (datetime) --
When the AutoML job was last modified.
FailureReason (string) --
The failure reason of an AutoML job.
PartialFailureReasons (list) --
The list of reasons for partial failures within an AutoML job.
(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.
NextToken (string) --
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
{'Candidates': {'InferenceContainerDefinitions': {'CPU | GPU': [{'Environment': {'string': 'string'},
'Image': 'string',
'ModelDataUrl': 'string'}]}}}
List the candidates created for the job.
See also: AWS API Documentation
Request Syntax
client.list_candidates_for_auto_ml_job(
AutoMLJobName='string',
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
CandidateNameEquals='string',
SortOrder='Ascending'|'Descending',
SortBy='CreationTime'|'Status'|'FinalObjectiveMetricValue',
MaxResults=123,
NextToken='string'
)
string
[REQUIRED]
List the candidates created for the job by providing the job's name.
string
List the candidates for the job and filter by status.
string
List the candidates for the job and filter by candidate name.
string
The sort order for the results. The default is Ascending.
string
The parameter by which to sort the results. The default is Descending.
integer
List the job's candidates up to a specified limit.
string
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
dict
Response Syntax
{
'Candidates': [
{
'CandidateName': 'string',
'FinalAutoMLJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
'Value': ...,
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
},
'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'
},
'CandidateMetrics': [
{
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
'Value': ...,
'Set': 'Train'|'Validation'|'Test',
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'
},
]
},
'InferenceContainerDefinitions': {
'string': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
]
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Candidates (list) --
Summaries about the AutoMLCandidates.
(dict) --
Information about a candidate produced by an AutoML training job, 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 .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
(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.
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.
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 V2 API only (for example, for jobs created by calling CreateAutoMLJobV2).
(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 .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
(string) --
(string) --
NextToken (string) --
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
{'DefaultSpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}},
'DefaultUserSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'RSessionAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}},
'DomainSettingsForUpdate': {'RStudioServerProDomainSettingsForUpdate': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
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',
'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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
}
}
},
DomainSettingsForUpdate={
'RStudioServerProDomainSettingsForUpdate': {
'DomainExecutionRoleArn': 'string',
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'RStudioConnectUrl': 'string',
'RStudioPackageManagerUrl': 'string'
},
'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED',
'SecurityGroupIds': [
'string',
]
},
DefaultSpaceSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
}
},
AppSecurityGroupManagement='Service'|'Customer'
)
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 Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly.
Amazon SageMaker adds a security group to allow NFS traffic from 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 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
InstanceType (string) --
The instance type that the image version runs on.
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.
InstanceType (string) --
The instance type that the image version runs on.
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 Canvas app.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas app.
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 app. 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.
dict
A collection of DomainSettings configuration values to update.
RStudioServerProDomainSettingsForUpdate (dict) --
A collection of RStudioServerPro Domain-level app settings to update.
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.
InstanceType (string) --
The instance type that the image version runs on.
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) --
dict
The default settings used to create a space within the Domain.
ExecutionRole (string) --
The execution role for the space.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(string) --
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.
dict
Response Syntax
{
'DomainArn': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the domain.
{'SpaceSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
Updates the settings of a space.
See also: AWS API Documentation
Request Syntax
client.update_space(
DomainId='string',
SpaceName='string',
SpaceSettings={
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
}
}
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
The name of the space.
dict
A collection of space settings.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(string) --
dict
Response Syntax
{
'SpaceArn': 'string'
}
Response Structure
(dict) --
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
{'UserSettings': {'JupyterServerAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'RSessionAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'InstanceType': {'ml.geospatial.interactive'}}}}}
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',
'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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': '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.geospatial.interactive',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'CanvasAppSettings': {
'TimeSeriesForecastingSettings': {
'Status': 'ENABLED'|'DISABLED',
'AmazonForecastRoleArn': 'string'
}
}
}
)
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 Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly.
Amazon SageMaker adds a security group to allow NFS traffic from 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 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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
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.
InstanceType (string) --
The instance type that the image version runs on.
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.
(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.
InstanceType (string) --
The instance type that the image version runs on.
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.
InstanceType (string) --
The instance type that the image version runs on.
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 Canvas app.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas app.
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 app. 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.
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).