SageMakerClient#
Auto-generated documentation for SageMaker type annotations stubs module mypy-boto3-sagemaker.
SageMakerClient#
Type annotations and code completion for boto3.client("sagemaker")
.
boto3 documentation
# SageMakerClient usage example
from boto3.session import Session
from mypy_boto3_sagemaker.client import SageMakerClient
def get_sagemaker_client() -> SageMakerClient:
return Session().client("sagemaker")
Exceptions#
boto3
client exceptions are generated in runtime.
This class provides code completion for boto3.client("sagemaker").exceptions
structure.
# Exceptions.exceptions usage example
client = boto3.client("sagemaker")
try:
do_something(client)
except (
client.exceptions.ClientError,
client.exceptions.ConflictException,
client.exceptions.ResourceInUse,
client.exceptions.ResourceLimitExceeded,
client.exceptions.ResourceNotFound,
) as e:
print(e)
# Exceptions.exceptions type checking example
from mypy_boto3_sagemaker.client import Exceptions
def handle_error(exc: Exceptions.ClientError) -> None:
...
Methods#
add_association#
Creates an association between the source and the destination.
Type annotations and code completion for boto3.client("sagemaker").add_association
method.
boto3 documentation
# add_association method definition
def add_association(
self,
*,
SourceArn: str,
DestinationArn: str,
AssociationType: AssociationEdgeTypeType = ..., # (1)
) -> AddAssociationResponseTypeDef: # (2)
...
# add_association method usage example with argument unpacking
kwargs: AddAssociationRequestRequestTypeDef = { # (1)
"SourceArn": ...,
"DestinationArn": ...,
}
parent.add_association(**kwargs)
add_tags#
Adds or overwrites one or more tags for the specified SageMaker resource.
Type annotations and code completion for boto3.client("sagemaker").add_tags
method.
boto3 documentation
# add_tags method definition
def add_tags(
self,
*,
ResourceArn: str,
Tags: Sequence[TagTypeDef], # (1)
) -> AddTagsOutputTypeDef: # (2)
...
- See TagTypeDef
- See AddTagsOutputTypeDef
# add_tags method usage example with argument unpacking
kwargs: AddTagsInputRequestTypeDef = { # (1)
"ResourceArn": ...,
"Tags": ...,
}
parent.add_tags(**kwargs)
associate_trial_component#
Associates a trial component with a trial.
Type annotations and code completion for boto3.client("sagemaker").associate_trial_component
method.
boto3 documentation
# associate_trial_component method definition
def associate_trial_component(
self,
*,
TrialComponentName: str,
TrialName: str,
) -> AssociateTrialComponentResponseTypeDef: # (1)
...
# associate_trial_component method usage example with argument unpacking
kwargs: AssociateTrialComponentRequestRequestTypeDef = { # (1)
"TrialComponentName": ...,
"TrialName": ...,
}
parent.associate_trial_component(**kwargs)
batch_describe_model_package#
This action batch describes a list of versioned model packages See also: AWS API Documentation.
Type annotations and code completion for boto3.client("sagemaker").batch_describe_model_package
method.
boto3 documentation
# batch_describe_model_package method definition
def batch_describe_model_package(
self,
*,
ModelPackageArnList: Sequence[str],
) -> BatchDescribeModelPackageOutputTypeDef: # (1)
...
# batch_describe_model_package method usage example with argument unpacking
kwargs: BatchDescribeModelPackageInputRequestTypeDef = { # (1)
"ModelPackageArnList": ...,
}
parent.batch_describe_model_package(**kwargs)
can_paginate#
Check if an operation can be paginated.
Type annotations and code completion for boto3.client("sagemaker").can_paginate
method.
boto3 documentation
# can_paginate method definition
def can_paginate(
self,
operation_name: str,
) -> bool:
...
close#
Closes underlying endpoint connections.
Type annotations and code completion for boto3.client("sagemaker").close
method.
boto3 documentation
# close method definition
def close(
self,
) -> None:
...
create_action#
Creates an action.
Type annotations and code completion for boto3.client("sagemaker").create_action
method.
boto3 documentation
# create_action method definition
def create_action(
self,
*,
ActionName: str,
Source: ActionSourceTypeDef, # (1)
ActionType: str,
Description: str = ...,
Status: ActionStatusType = ..., # (2)
Properties: Mapping[str, str] = ...,
MetadataProperties: MetadataPropertiesTypeDef = ..., # (3)
Tags: Sequence[TagTypeDef] = ..., # (4)
) -> CreateActionResponseTypeDef: # (5)
...
- See ActionSourceTypeDef
- See ActionStatusType
- See MetadataPropertiesTypeDef
- See TagTypeDef
- See CreateActionResponseTypeDef
# create_action method usage example with argument unpacking
kwargs: CreateActionRequestRequestTypeDef = { # (1)
"ActionName": ...,
"Source": ...,
"ActionType": ...,
}
parent.create_action(**kwargs)
create_algorithm#
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
Type annotations and code completion for boto3.client("sagemaker").create_algorithm
method.
boto3 documentation
# create_algorithm method definition
def create_algorithm(
self,
*,
AlgorithmName: str,
TrainingSpecification: TrainingSpecificationTypeDef, # (1)
AlgorithmDescription: str = ...,
InferenceSpecification: InferenceSpecificationTypeDef = ..., # (2)
ValidationSpecification: AlgorithmValidationSpecificationTypeDef = ..., # (3)
CertifyForMarketplace: bool = ...,
Tags: Sequence[TagTypeDef] = ..., # (4)
) -> CreateAlgorithmOutputTypeDef: # (5)
...
- See TrainingSpecificationTypeDef
- See InferenceSpecificationTypeDef
- See AlgorithmValidationSpecificationTypeDef
- See TagTypeDef
- See CreateAlgorithmOutputTypeDef
# create_algorithm method usage example with argument unpacking
kwargs: CreateAlgorithmInputRequestTypeDef = { # (1)
"AlgorithmName": ...,
"TrainingSpecification": ...,
}
parent.create_algorithm(**kwargs)
create_app#
Creates a running app for the specified UserProfile.
Type annotations and code completion for boto3.client("sagemaker").create_app
method.
boto3 documentation
# create_app method definition
def create_app(
self,
*,
DomainId: str,
AppType: AppTypeType, # (1)
AppName: str,
UserProfileName: str = ...,
Tags: Sequence[TagTypeDef] = ..., # (2)
ResourceSpec: ResourceSpecTypeDef = ..., # (3)
SpaceName: str = ...,
) -> CreateAppResponseTypeDef: # (4)
...
- See AppTypeType
- See TagTypeDef
- See ResourceSpecTypeDef
- See CreateAppResponseTypeDef
# create_app method usage example with argument unpacking
kwargs: CreateAppRequestRequestTypeDef = { # (1)
"DomainId": ...,
"AppType": ...,
"AppName": ...,
}
parent.create_app(**kwargs)
create_app_image_config#
Creates a configuration for running a SageMaker image as a KernelGateway app.
Type annotations and code completion for boto3.client("sagemaker").create_app_image_config
method.
boto3 documentation
# create_app_image_config method definition
def create_app_image_config(
self,
*,
AppImageConfigName: str,
Tags: Sequence[TagTypeDef] = ..., # (1)
KernelGatewayImageConfig: KernelGatewayImageConfigTypeDef = ..., # (2)
) -> CreateAppImageConfigResponseTypeDef: # (3)
...
# create_app_image_config method usage example with argument unpacking
kwargs: CreateAppImageConfigRequestRequestTypeDef = { # (1)
"AppImageConfigName": ...,
}
parent.create_app_image_config(**kwargs)
create_artifact#
Creates an artifact.
Type annotations and code completion for boto3.client("sagemaker").create_artifact
method.
boto3 documentation
# create_artifact method definition
def create_artifact(
self,
*,
Source: ArtifactSourceTypeDef, # (1)
ArtifactType: str,
ArtifactName: str = ...,
Properties: Mapping[str, str] = ...,
MetadataProperties: MetadataPropertiesTypeDef = ..., # (2)
Tags: Sequence[TagTypeDef] = ..., # (3)
) -> CreateArtifactResponseTypeDef: # (4)
...
- See ArtifactSourceTypeDef
- See MetadataPropertiesTypeDef
- See TagTypeDef
- See CreateArtifactResponseTypeDef
# create_artifact method usage example with argument unpacking
kwargs: CreateArtifactRequestRequestTypeDef = { # (1)
"Source": ...,
"ArtifactType": ...,
}
parent.create_artifact(**kwargs)
create_auto_ml_job#
Creates an Autopilot job.
Type annotations and code completion for boto3.client("sagemaker").create_auto_ml_job
method.
boto3 documentation
# create_auto_ml_job method definition
def create_auto_ml_job(
self,
*,
AutoMLJobName: str,
InputDataConfig: Sequence[AutoMLChannelTypeDef], # (1)
OutputDataConfig: AutoMLOutputDataConfigTypeDef, # (2)
RoleArn: str,
ProblemType: ProblemTypeType = ..., # (3)
AutoMLJobObjective: AutoMLJobObjectiveTypeDef = ..., # (4)
AutoMLJobConfig: AutoMLJobConfigTypeDef = ..., # (5)
GenerateCandidateDefinitionsOnly: bool = ...,
Tags: Sequence[TagTypeDef] = ..., # (6)
ModelDeployConfig: ModelDeployConfigTypeDef = ..., # (7)
) -> CreateAutoMLJobResponseTypeDef: # (8)
...
- See AutoMLChannelTypeDef
- See AutoMLOutputDataConfigTypeDef
- See ProblemTypeType
- See AutoMLJobObjectiveTypeDef
- See AutoMLJobConfigTypeDef
- See TagTypeDef
- See ModelDeployConfigTypeDef
- See CreateAutoMLJobResponseTypeDef
# create_auto_ml_job method usage example with argument unpacking
kwargs: CreateAutoMLJobRequestRequestTypeDef = { # (1)
"AutoMLJobName": ...,
"InputDataConfig": ...,
"OutputDataConfig": ...,
"RoleArn": ...,
}
parent.create_auto_ml_job(**kwargs)
create_auto_ml_job_v2#
Creates an Amazon SageMaker AutoML job that uses non-tabular data such as images or text for Computer Vision or Natural Language Processing problems.
Type annotations and code completion for boto3.client("sagemaker").create_auto_ml_job_v2
method.
boto3 documentation
# create_auto_ml_job_v2 method definition
def create_auto_ml_job_v2(
self,
*,
AutoMLJobName: str,
AutoMLJobInputDataConfig: Sequence[AutoMLJobChannelTypeDef], # (1)
OutputDataConfig: AutoMLOutputDataConfigTypeDef, # (2)
AutoMLProblemTypeConfig: AutoMLProblemTypeConfigTypeDef, # (3)
RoleArn: str,
Tags: Sequence[TagTypeDef] = ..., # (4)
SecurityConfig: AutoMLSecurityConfigTypeDef = ..., # (5)
AutoMLJobObjective: AutoMLJobObjectiveTypeDef = ..., # (6)
ModelDeployConfig: ModelDeployConfigTypeDef = ..., # (7)
DataSplitConfig: AutoMLDataSplitConfigTypeDef = ..., # (8)
) -> CreateAutoMLJobV2ResponseTypeDef: # (9)
...
- See AutoMLJobChannelTypeDef
- See AutoMLOutputDataConfigTypeDef
- See AutoMLProblemTypeConfigTypeDef
- See TagTypeDef
- See AutoMLSecurityConfigTypeDef
- See AutoMLJobObjectiveTypeDef
- See ModelDeployConfigTypeDef
- See AutoMLDataSplitConfigTypeDef
- See CreateAutoMLJobV2ResponseTypeDef
# create_auto_ml_job_v2 method usage example with argument unpacking
kwargs: CreateAutoMLJobV2RequestRequestTypeDef = { # (1)
"AutoMLJobName": ...,
"AutoMLJobInputDataConfig": ...,
"OutputDataConfig": ...,
"AutoMLProblemTypeConfig": ...,
"RoleArn": ...,
}
parent.create_auto_ml_job_v2(**kwargs)
create_code_repository#
Creates a Git repository as a resource in your SageMaker account.
Type annotations and code completion for boto3.client("sagemaker").create_code_repository
method.
boto3 documentation
# create_code_repository method definition
def create_code_repository(
self,
*,
CodeRepositoryName: str,
GitConfig: GitConfigTypeDef, # (1)
Tags: Sequence[TagTypeDef] = ..., # (2)
) -> CreateCodeRepositoryOutputTypeDef: # (3)
...
# create_code_repository method usage example with argument unpacking
kwargs: CreateCodeRepositoryInputRequestTypeDef = { # (1)
"CodeRepositoryName": ...,
"GitConfig": ...,
}
parent.create_code_repository(**kwargs)
create_compilation_job#
Starts a model compilation job.
Type annotations and code completion for boto3.client("sagemaker").create_compilation_job
method.
boto3 documentation
# create_compilation_job method definition
def create_compilation_job(
self,
*,
CompilationJobName: str,
RoleArn: str,
OutputConfig: OutputConfigTypeDef, # (1)
StoppingCondition: StoppingConditionTypeDef, # (2)
ModelPackageVersionArn: str = ...,
InputConfig: InputConfigTypeDef = ..., # (3)
VpcConfig: NeoVpcConfigTypeDef = ..., # (4)
Tags: Sequence[TagTypeDef] = ..., # (5)
) -> CreateCompilationJobResponseTypeDef: # (6)
...
- See OutputConfigTypeDef
- See StoppingConditionTypeDef
- See InputConfigTypeDef
- See NeoVpcConfigTypeDef
- See TagTypeDef
- See CreateCompilationJobResponseTypeDef
# create_compilation_job method usage example with argument unpacking
kwargs: CreateCompilationJobRequestRequestTypeDef = { # (1)
"CompilationJobName": ...,
"RoleArn": ...,
"OutputConfig": ...,
"StoppingCondition": ...,
}
parent.create_compilation_job(**kwargs)
create_context#
Creates a context.
Type annotations and code completion for boto3.client("sagemaker").create_context
method.
boto3 documentation
# create_context method definition
def create_context(
self,
*,
ContextName: str,
Source: ContextSourceTypeDef, # (1)
ContextType: str,
Description: str = ...,
Properties: Mapping[str, str] = ...,
Tags: Sequence[TagTypeDef] = ..., # (2)
) -> CreateContextResponseTypeDef: # (3)
...
# create_context method usage example with argument unpacking
kwargs: CreateContextRequestRequestTypeDef = { # (1)
"ContextName": ...,
"Source": ...,
"ContextType": ...,
}
parent.create_context(**kwargs)
create_data_quality_job_definition#
Creates a definition for a job that monitors data quality and drift.
Type annotations and code completion for boto3.client("sagemaker").create_data_quality_job_definition
method.
boto3 documentation
# create_data_quality_job_definition method definition
def create_data_quality_job_definition(
self,
*,
JobDefinitionName: str,
DataQualityAppSpecification: DataQualityAppSpecificationTypeDef, # (1)
DataQualityJobInput: DataQualityJobInputTypeDef, # (2)
DataQualityJobOutputConfig: MonitoringOutputConfigTypeDef, # (3)
JobResources: MonitoringResourcesTypeDef, # (4)
RoleArn: str,
DataQualityBaselineConfig: DataQualityBaselineConfigTypeDef = ..., # (5)
NetworkConfig: MonitoringNetworkConfigTypeDef = ..., # (6)
StoppingCondition: MonitoringStoppingConditionTypeDef = ..., # (7)
Tags: Sequence[TagTypeDef] = ..., # (8)
) -> CreateDataQualityJobDefinitionResponseTypeDef: # (9)
...
- See DataQualityAppSpecificationTypeDef
- See DataQualityJobInputTypeDef
- See MonitoringOutputConfigTypeDef
- See MonitoringResourcesTypeDef
- See DataQualityBaselineConfigTypeDef
- See MonitoringNetworkConfigTypeDef
- See MonitoringStoppingConditionTypeDef
- See TagTypeDef
- See CreateDataQualityJobDefinitionResponseTypeDef
# create_data_quality_job_definition method usage example with argument unpacking
kwargs: CreateDataQualityJobDefinitionRequestRequestTypeDef = { # (1)
"JobDefinitionName": ...,
"DataQualityAppSpecification": ...,
"DataQualityJobInput": ...,
"DataQualityJobOutputConfig": ...,
"JobResources": ...,
"RoleArn": ...,
}
parent.create_data_quality_job_definition(**kwargs)
create_device_fleet#
Creates a device fleet.
Type annotations and code completion for boto3.client("sagemaker").create_device_fleet
method.
boto3 documentation
# create_device_fleet method definition
def create_device_fleet(
self,
*,
DeviceFleetName: str,
OutputConfig: EdgeOutputConfigTypeDef, # (1)
RoleArn: str = ...,
Description: str = ...,
Tags: Sequence[TagTypeDef] = ..., # (2)
EnableIotRoleAlias: bool = ...,
) -> EmptyResponseMetadataTypeDef: # (3)
...
# create_device_fleet method usage example with argument unpacking
kwargs: CreateDeviceFleetRequestRequestTypeDef = { # (1)
"DeviceFleetName": ...,
"OutputConfig": ...,
}
parent.create_device_fleet(**kwargs)
create_domain#
Creates a Domain
used by Amazon SageMaker Studio.
Type annotations and code completion for boto3.client("sagemaker").create_domain
method.
boto3 documentation
# create_domain method definition
def create_domain(
self,
*,
DomainName: str,
AuthMode: AuthModeType, # (1)
DefaultUserSettings: UserSettingsTypeDef, # (2)
SubnetIds: Sequence[str],
VpcId: str,
Tags: Sequence[TagTypeDef] = ..., # (3)
AppNetworkAccessType: AppNetworkAccessTypeType = ..., # (4)
HomeEfsFileSystemKmsKeyId: str = ...,
KmsKeyId: str = ...,
AppSecurityGroupManagement: AppSecurityGroupManagementType = ..., # (5)
DomainSettings: DomainSettingsTypeDef = ..., # (6)
DefaultSpaceSettings: DefaultSpaceSettingsTypeDef = ..., # (7)
) -> CreateDomainResponseTypeDef: # (8)
...
- See AuthModeType
- See UserSettingsTypeDef
- See TagTypeDef
- See AppNetworkAccessTypeType
- See AppSecurityGroupManagementType
- See DomainSettingsTypeDef
- See DefaultSpaceSettingsTypeDef
- See CreateDomainResponseTypeDef
# create_domain method usage example with argument unpacking
kwargs: CreateDomainRequestRequestTypeDef = { # (1)
"DomainName": ...,
"AuthMode": ...,
"DefaultUserSettings": ...,
"SubnetIds": ...,
"VpcId": ...,
}
parent.create_domain(**kwargs)
create_edge_deployment_plan#
Creates an edge deployment plan, consisting of multiple stages.
Type annotations and code completion for boto3.client("sagemaker").create_edge_deployment_plan
method.
boto3 documentation
# create_edge_deployment_plan method definition
def create_edge_deployment_plan(
self,
*,
EdgeDeploymentPlanName: str,
ModelConfigs: Sequence[EdgeDeploymentModelConfigTypeDef], # (1)
DeviceFleetName: str,
Stages: Sequence[DeploymentStageTypeDef] = ..., # (2)
Tags: Sequence[TagTypeDef] = ..., # (3)
) -> CreateEdgeDeploymentPlanResponseTypeDef: # (4)
...
- See EdgeDeploymentModelConfigTypeDef
- See DeploymentStageTypeDef
- See TagTypeDef
- See CreateEdgeDeploymentPlanResponseTypeDef
# create_edge_deployment_plan method usage example with argument unpacking
kwargs: CreateEdgeDeploymentPlanRequestRequestTypeDef = { # (1)
"EdgeDeploymentPlanName": ...,
"ModelConfigs": ...,
"DeviceFleetName": ...,
}
parent.create_edge_deployment_plan(**kwargs)
create_edge_deployment_stage#
Creates a new stage in an existing edge deployment plan.
Type annotations and code completion for boto3.client("sagemaker").create_edge_deployment_stage
method.
boto3 documentation
# create_edge_deployment_stage method definition
def create_edge_deployment_stage(
self,
*,
EdgeDeploymentPlanName: str,
Stages: Sequence[DeploymentStageTypeDef], # (1)
) -> EmptyResponseMetadataTypeDef: # (2)
...
# create_edge_deployment_stage method usage example with argument unpacking
kwargs: CreateEdgeDeploymentStageRequestRequestTypeDef = { # (1)
"EdgeDeploymentPlanName": ...,
"Stages": ...,
}
parent.create_edge_deployment_stage(**kwargs)
create_edge_packaging_job#
Starts a SageMaker Edge Manager model packaging job.
Type annotations and code completion for boto3.client("sagemaker").create_edge_packaging_job
method.
boto3 documentation
# create_edge_packaging_job method definition
def create_edge_packaging_job(
self,
*,
EdgePackagingJobName: str,
CompilationJobName: str,
ModelName: str,
ModelVersion: str,
RoleArn: str,
OutputConfig: EdgeOutputConfigTypeDef, # (1)
ResourceKey: str = ...,
Tags: Sequence[TagTypeDef] = ..., # (2)
) -> EmptyResponseMetadataTypeDef: # (3)
...
# create_edge_packaging_job method usage example with argument unpacking
kwargs: CreateEdgePackagingJobRequestRequestTypeDef = { # (1)
"EdgePackagingJobName": ...,
"CompilationJobName": ...,
"ModelName": ...,
"ModelVersion": ...,
"RoleArn": ...,
"OutputConfig": ...,
}
parent.create_edge_packaging_job(**kwargs)
create_endpoint#
Creates an endpoint using the endpoint configuration specified in the request.
Type annotations and code completion for boto3.client("sagemaker").create_endpoint
method.
boto3 documentation
# create_endpoint method definition
def create_endpoint(
self,
*,
EndpointName: str,
EndpointConfigName: str,
DeploymentConfig: DeploymentConfigTypeDef = ..., # (1)
Tags: Sequence[TagTypeDef] = ..., # (2)
) -> CreateEndpointOutputTypeDef: # (3)
...
# create_endpoint method usage example with argument unpacking
kwargs: CreateEndpointInputRequestTypeDef = { # (1)
"EndpointName": ...,
"EndpointConfigName": ...,
}
parent.create_endpoint(**kwargs)
create_endpoint_config#
Creates an endpoint configuration that SageMaker hosting services uses to deploy models.
Type annotations and code completion for boto3.client("sagemaker").create_endpoint_config
method.
boto3 documentation
# create_endpoint_config method definition
def create_endpoint_config(
self,
*,
EndpointConfigName: str,
ProductionVariants: Sequence[ProductionVariantTypeDef], # (1)
DataCaptureConfig: DataCaptureConfigTypeDef = ..., # (2)
Tags: Sequence[TagTypeDef] = ..., # (3)
KmsKeyId: str = ...,
AsyncInferenceConfig: AsyncInferenceConfigTypeDef = ..., # (4)
ExplainerConfig: ExplainerConfigTypeDef = ..., # (5)
ShadowProductionVariants: Sequence[ProductionVariantTypeDef] = ..., # (1)
) -> CreateEndpointConfigOutputTypeDef: # (7)
...
- See ProductionVariantTypeDef
- See DataCaptureConfigTypeDef
- See TagTypeDef
- See AsyncInferenceConfigTypeDef
- See ExplainerConfigTypeDef
- See ProductionVariantTypeDef
- See CreateEndpointConfigOutputTypeDef
# create_endpoint_config method usage example with argument unpacking
kwargs: CreateEndpointConfigInputRequestTypeDef = { # (1)
"EndpointConfigName": ...,
"ProductionVariants": ...,
}
parent.create_endpoint_config(**kwargs)
create_experiment#
Creates a SageMaker experiment.
Type annotations and code completion for boto3.client("sagemaker").create_experiment
method.
boto3 documentation
# create_experiment method definition
def create_experiment(
self,
*,
ExperimentName: str,
DisplayName: str = ...,
Description: str = ...,
Tags: Sequence[TagTypeDef] = ..., # (1)
) -> CreateExperimentResponseTypeDef: # (2)
...
# create_experiment method usage example with argument unpacking
kwargs: CreateExperimentRequestRequestTypeDef = { # (1)
"ExperimentName": ...,
}
parent.create_experiment(**kwargs)
create_feature_group#
Create a new FeatureGroup
.
Type annotations and code completion for boto3.client("sagemaker").create_feature_group
method.
boto3 documentation
# create_feature_group method definition
def create_feature_group(
self,
*,
FeatureGroupName: str,
RecordIdentifierFeatureName: str,
EventTimeFeatureName: str,
FeatureDefinitions: Sequence[FeatureDefinitionTypeDef], # (1)
OnlineStoreConfig: OnlineStoreConfigTypeDef = ..., # (2)
OfflineStoreConfig: OfflineStoreConfigTypeDef = ..., # (3)
RoleArn: str = ...,
Description: str = ...,
Tags: Sequence[TagTypeDef] = ..., # (4)
) -> CreateFeatureGroupResponseTypeDef: # (5)
...
- See FeatureDefinitionTypeDef
- See OnlineStoreConfigTypeDef
- See OfflineStoreConfigTypeDef
- See TagTypeDef
- See CreateFeatureGroupResponseTypeDef
# create_feature_group method usage example with argument unpacking
kwargs: CreateFeatureGroupRequestRequestTypeDef = { # (1)
"FeatureGroupName": ...,
"RecordIdentifierFeatureName": ...,
"EventTimeFeatureName": ...,
"FeatureDefinitions": ...,
}
parent.create_feature_group(**kwargs)
create_flow_definition#
Creates a flow definition.
Type annotations and code completion for boto3.client("sagemaker").create_flow_definition
method.
boto3 documentation
# create_flow_definition method definition
def create_flow_definition(
self,
*,
FlowDefinitionName: str,
HumanLoopConfig: HumanLoopConfigTypeDef, # (1)
OutputConfig: FlowDefinitionOutputConfigTypeDef, # (2)
RoleArn: str,
HumanLoopRequestSource: HumanLoopRequestSourceTypeDef = ..., # (3)
HumanLoopActivationConfig: HumanLoopActivationConfigTypeDef = ..., # (4)
Tags: Sequence[TagTypeDef] = ..., # (5)
) -> CreateFlowDefinitionResponseTypeDef: # (6)
...
- See HumanLoopConfigTypeDef
- See FlowDefinitionOutputConfigTypeDef
- See HumanLoopRequestSourceTypeDef
- See HumanLoopActivationConfigTypeDef
- See TagTypeDef
- See CreateFlowDefinitionResponseTypeDef
# create_flow_definition method usage example with argument unpacking
kwargs: CreateFlowDefinitionRequestRequestTypeDef = { # (1)
"FlowDefinitionName": ...,
"HumanLoopConfig": ...,
"OutputConfig": ...,
"RoleArn": ...,
}
parent.create_flow_definition(**kwargs)
create_hub#
Create a hub.
Type annotations and code completion for boto3.client("sagemaker").create_hub
method.
boto3 documentation
# create_hub method definition
def create_hub(
self,
*,
HubName: str,
HubDescription: str,
HubDisplayName: str = ...,
HubSearchKeywords: Sequence[str] = ...,
S3StorageConfig: HubS3StorageConfigTypeDef = ..., # (1)
Tags: Sequence[TagTypeDef] = ..., # (2)
) -> CreateHubResponseTypeDef: # (3)
...
# create_hub method usage example with argument unpacking
kwargs: CreateHubRequestRequestTypeDef = { # (1)
"HubName": ...,
"HubDescription": ...,
}
parent.create_hub(**kwargs)
create_human_task_ui#
Defines the settings you will use for the human review workflow user interface.
Type annotations and code completion for boto3.client("sagemaker").create_human_task_ui
method.
boto3 documentation
# create_human_task_ui method definition
def create_human_task_ui(
self,
*,
HumanTaskUiName: str,
UiTemplate: UiTemplateTypeDef, # (1)
Tags: Sequence[TagTypeDef] = ..., # (2)
) -> CreateHumanTaskUiResponseTypeDef: # (3)
...
# create_human_task_ui method usage example with argument unpacking
kwargs: CreateHumanTaskUiRequestRequestTypeDef = { # (1)
"HumanTaskUiName": ...,
"UiTemplate": ...,
}
parent.create_human_task_ui(**kwargs)
create_hyper_parameter_tuning_job#
Starts a hyperparameter tuning job.
Type annotations and code completion for boto3.client("sagemaker").create_hyper_parameter_tuning_job
method.
boto3 documentation
# create_hyper_parameter_tuning_job method definition
def create_hyper_parameter_tuning_job(
self,
*,
HyperParameterTuningJobName: str,
HyperParameterTuningJobConfig: HyperParameterTuningJobConfigTypeDef, # (1)
TrainingJobDefinition: HyperParameterTrainingJobDefinitionTypeDef = ..., # (2)
TrainingJobDefinitions: Sequence[HyperParameterTrainingJobDefinitionTypeDef] = ..., # (3)
WarmStartConfig: HyperParameterTuningJobWarmStartConfigTypeDef = ..., # (4)
Tags: Sequence[TagTypeDef] = ..., # (5)
Autotune: AutotuneTypeDef = ..., # (6)
) -> CreateHyperParameterTuningJobResponseTypeDef: # (7)
...
- See HyperParameterTuningJobConfigTypeDef
- See HyperParameterTrainingJobDefinitionTypeDef
- See HyperParameterTrainingJobDefinitionTypeDef
- See HyperParameterTuningJobWarmStartConfigTypeDef
- See TagTypeDef
- See AutotuneTypeDef
- See CreateHyperParameterTuningJobResponseTypeDef
# create_hyper_parameter_tuning_job method usage example with argument unpacking
kwargs: CreateHyperParameterTuningJobRequestRequestTypeDef = { # (1)
"HyperParameterTuningJobName": ...,
"HyperParameterTuningJobConfig": ...,
}
parent.create_hyper_parameter_tuning_job(**kwargs)
create_image#
Creates a custom SageMaker image.
Type annotations and code completion for boto3.client("sagemaker").create_image
method.
boto3 documentation
# create_image method definition
def create_image(
self,
*,
ImageName: str,
RoleArn: str,
Description: str = ...,
DisplayName: str = ...,
Tags: Sequence[TagTypeDef] = ..., # (1)
) -> CreateImageResponseTypeDef: # (2)
...
- See TagTypeDef
- See CreateImageResponseTypeDef
# create_image method usage example with argument unpacking
kwargs: CreateImageRequestRequestTypeDef = { # (1)
"ImageName": ...,
"RoleArn": ...,
}
parent.create_image(**kwargs)
create_image_version#
Creates a version of the SageMaker image specified by ImageName
.
Type annotations and code completion for boto3.client("sagemaker").create_image_version
method.
boto3 documentation
# create_image_version method definition
def create_image_version(
self,
*,
BaseImage: str,
ClientToken: str,
ImageName: str,
Aliases: Sequence[str] = ...,
VendorGuidance: VendorGuidanceType = ..., # (1)
JobType: JobTypeType = ..., # (2)
MLFramework: str = ...,
ProgrammingLang: str = ...,
Processor: ProcessorType = ..., # (3)
Horovod: bool = ...,
ReleaseNotes: str = ...,
) -> CreateImageVersionResponseTypeDef: # (4)
...
- See VendorGuidanceType
- See JobTypeType
- See ProcessorType
- See CreateImageVersionResponseTypeDef
# create_image_version method usage example with argument unpacking
kwargs: CreateImageVersionRequestRequestTypeDef = { # (1)
"BaseImage": ...,
"ClientToken": ...,
"ImageName": ...,
}
parent.create_image_version(**kwargs)
create_inference_experiment#
Creates an inference experiment using the configurations specified in the request.
Type annotations and code completion for boto3.client("sagemaker").create_inference_experiment
method.
boto3 documentation
# create_inference_experiment method definition
def create_inference_experiment(
self,
*,
Name: str,
Type: InferenceExperimentTypeType, # (1)
RoleArn: str,
EndpointName: str,
ModelVariants: Sequence[ModelVariantConfigTypeDef], # (2)
ShadowModeConfig: ShadowModeConfigTypeDef, # (3)
Schedule: InferenceExperimentScheduleTypeDef = ..., # (4)
Description: str = ...,
DataStorageConfig: InferenceExperimentDataStorageConfigTypeDef = ..., # (5)
KmsKey: str = ...,
Tags: Sequence[TagTypeDef] = ..., # (6)
) -> CreateInferenceExperimentResponseTypeDef: # (7)
...
- See InferenceExperimentTypeType
- See ModelVariantConfigTypeDef
- See ShadowModeConfigTypeDef
- See InferenceExperimentScheduleTypeDef
- See InferenceExperimentDataStorageConfigTypeDef
- See TagTypeDef
- See CreateInferenceExperimentResponseTypeDef
# create_inference_experiment method usage example with argument unpacking
kwargs: CreateInferenceExperimentRequestRequestTypeDef = { # (1)
"Name": ...,
"Type": ...,
"RoleArn": ...,
"EndpointName": ...,
"ModelVariants": ...,
"ShadowModeConfig": ...,
}
parent.create_inference_experiment(**kwargs)
create_inference_recommendations_job#
Starts a recommendation job.
Type annotations and code completion for boto3.client("sagemaker").create_inference_recommendations_job
method.
boto3 documentation
# create_inference_recommendations_job method definition
def create_inference_recommendations_job(
self,
*,
JobName: str,
JobType: RecommendationJobTypeType, # (1)
RoleArn: str,
InputConfig: RecommendationJobInputConfigTypeDef, # (2)
JobDescription: str = ...,
StoppingConditions: RecommendationJobStoppingConditionsTypeDef = ..., # (3)
OutputConfig: RecommendationJobOutputConfigTypeDef = ..., # (4)
Tags: Sequence[TagTypeDef] = ..., # (5)
) -> CreateInferenceRecommendationsJobResponseTypeDef: # (6)
...
- See RecommendationJobTypeType
- See RecommendationJobInputConfigTypeDef
- See RecommendationJobStoppingConditionsTypeDef
- See RecommendationJobOutputConfigTypeDef
- See TagTypeDef
- See CreateInferenceRecommendationsJobResponseTypeDef
# create_inference_recommendations_job method usage example with argument unpacking
kwargs: CreateInferenceRecommendationsJobRequestRequestTypeDef = { # (1)
"JobName": ...,
"JobType": ...,
"RoleArn": ...,
"InputConfig": ...,
}
parent.create_inference_recommendations_job(**kwargs)
create_labeling_job#
Creates a job that uses workers to label the data objects in your input dataset.
Type annotations and code completion for boto3.client("sagemaker").create_labeling_job
method.
boto3 documentation
# create_labeling_job method definition
def create_labeling_job(
self,
*,
LabelingJobName: str,
LabelAttributeName: str,
InputConfig: LabelingJobInputConfigTypeDef, # (1)
OutputConfig: LabelingJobOutputConfigTypeDef, # (2)
RoleArn: str,
HumanTaskConfig: HumanTaskConfigTypeDef, # (3)
LabelCategoryConfigS3Uri: str = ...,
StoppingConditions: LabelingJobStoppingConditionsTypeDef = ..., # (4)
LabelingJobAlgorithmsConfig: LabelingJobAlgorithmsConfigTypeDef = ..., # (5)
Tags: Sequence[TagTypeDef] = ..., # (6)
) -> CreateLabelingJobResponseTypeDef: # (7)
...
- See LabelingJobInputConfigTypeDef
- See LabelingJobOutputConfigTypeDef
- See HumanTaskConfigTypeDef
- See LabelingJobStoppingConditionsTypeDef
- See LabelingJobAlgorithmsConfigTypeDef
- See TagTypeDef
- See CreateLabelingJobResponseTypeDef
# create_labeling_job method usage example with argument unpacking
kwargs: CreateLabelingJobRequestRequestTypeDef = { # (1)
"LabelingJobName": ...,
"LabelAttributeName": ...,
"InputConfig": ...,
"OutputConfig": ...,
"RoleArn": ...,
"HumanTaskConfig": ...,
}
parent.create_labeling_job(**kwargs)
create_model#
Creates a model in SageMaker.
Type annotations and code completion for boto3.client("sagemaker").create_model
method.
boto3 documentation
# create_model method definition
def create_model(
self,
*,
ModelName: str,
ExecutionRoleArn: str,
PrimaryContainer: ContainerDefinitionTypeDef = ..., # (1)
Containers: Sequence[ContainerDefinitionTypeDef] = ..., # (2)
InferenceExecutionConfig: InferenceExecutionConfigTypeDef = ..., # (3)
Tags: Sequence[TagTypeDef] = ..., # (4)
VpcConfig: VpcConfigTypeDef = ..., # (5)
EnableNetworkIsolation: bool = ...,
) -> CreateModelOutputTypeDef: # (6)
...
- See ContainerDefinitionTypeDef
- See ContainerDefinitionTypeDef
- See InferenceExecutionConfigTypeDef
- See TagTypeDef
- See VpcConfigTypeDef
- See CreateModelOutputTypeDef
# create_model method usage example with argument unpacking
kwargs: CreateModelInputRequestTypeDef = { # (1)
"ModelName": ...,
"ExecutionRoleArn": ...,
}
parent.create_model(**kwargs)
create_model_bias_job_definition#
Creates the definition for a model bias job.
Type annotations and code completion for boto3.client("sagemaker").create_model_bias_job_definition
method.
boto3 documentation
# create_model_bias_job_definition method definition
def create_model_bias_job_definition(
self,
*,
JobDefinitionName: str,
ModelBiasAppSpecification: ModelBiasAppSpecificationTypeDef, # (1)
ModelBiasJobInput: ModelBiasJobInputTypeDef, # (2)
ModelBiasJobOutputConfig: MonitoringOutputConfigTypeDef, # (3)
JobResources: MonitoringResourcesTypeDef, # (4)
RoleArn: str,
ModelBiasBaselineConfig: ModelBiasBaselineConfigTypeDef = ..., # (5)
NetworkConfig: MonitoringNetworkConfigTypeDef = ..., # (6)
StoppingCondition: MonitoringStoppingConditionTypeDef = ..., # (7)
Tags: Sequence[TagTypeDef] = ..., # (8)
) -> CreateModelBiasJobDefinitionResponseTypeDef: # (9)
...
- See ModelBiasAppSpecificationTypeDef
- See ModelBiasJobInputTypeDef
- See MonitoringOutputConfigTypeDef
- See MonitoringResourcesTypeDef
- See ModelBiasBaselineConfigTypeDef
- See MonitoringNetworkConfigTypeDef
- See MonitoringStoppingConditionTypeDef
- See TagTypeDef
- See CreateModelBiasJobDefinitionResponseTypeDef
# create_model_bias_job_definition method usage example with argument unpacking
kwargs: CreateModelBiasJobDefinitionRequestRequestTypeDef = { # (1)
"JobDefinitionName": ...,
"ModelBiasAppSpecification": ...,
"ModelBiasJobInput": ...,
"ModelBiasJobOutputConfig": ...,
"JobResources": ...,
"RoleArn": ...,
}
parent.create_model_bias_job_definition(**kwargs)
create_model_card#
Creates an Amazon SageMaker Model Card.
Type annotations and code completion for boto3.client("sagemaker").create_model_card
method.
boto3 documentation
# create_model_card method definition
def create_model_card(
self,
*,
ModelCardName: str,
Content: str,
ModelCardStatus: ModelCardStatusType, # (1)
SecurityConfig: ModelCardSecurityConfigTypeDef = ..., # (2)
Tags: Sequence[TagTypeDef] = ..., # (3)
) -> CreateModelCardResponseTypeDef: # (4)
...
- See ModelCardStatusType
- See ModelCardSecurityConfigTypeDef
- See TagTypeDef
- See CreateModelCardResponseTypeDef
# create_model_card method usage example with argument unpacking
kwargs: CreateModelCardRequestRequestTypeDef = { # (1)
"ModelCardName": ...,
"Content": ...,
"ModelCardStatus": ...,
}
parent.create_model_card(**kwargs)
create_model_card_export_job#
Creates an Amazon SageMaker Model Card export job.
Type annotations and code completion for boto3.client("sagemaker").create_model_card_export_job
method.