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SageMakerClient#

Index > SageMaker > 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)
    ...
  1. See AssociationEdgeTypeType
  2. See AddAssociationResponseTypeDef 
# add_association method usage example with argument unpacking

kwargs: AddAssociationRequestRequestTypeDef = {  # (1)
    "SourceArn": ...,
    "DestinationArn": ...,
}

parent.add_association(**kwargs)
  1. See AddAssociationRequestRequestTypeDef

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)
    ...
  1. See TagTypeDef
  2. See AddTagsOutputTypeDef 
# add_tags method usage example with argument unpacking

kwargs: AddTagsInputRequestTypeDef = {  # (1)
    "ResourceArn": ...,
    "Tags": ...,
}

parent.add_tags(**kwargs)
  1. See AddTagsInputRequestTypeDef

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)
    ...
  1. See AssociateTrialComponentResponseTypeDef 
# associate_trial_component method usage example with argument unpacking

kwargs: AssociateTrialComponentRequestRequestTypeDef = {  # (1)
    "TrialComponentName": ...,
    "TrialName": ...,
}

parent.associate_trial_component(**kwargs)
  1. See AssociateTrialComponentRequestRequestTypeDef

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)
    ...
  1. See BatchDescribeModelPackageOutputTypeDef 
# batch_describe_model_package method usage example with argument unpacking

kwargs: BatchDescribeModelPackageInputRequestTypeDef = {  # (1)
    "ModelPackageArnList": ...,
}

parent.batch_describe_model_package(**kwargs)
  1. See BatchDescribeModelPackageInputRequestTypeDef

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)
    ...
  1. See ActionSourceTypeDef
  2. See ActionStatusType
  3. See MetadataPropertiesTypeDef
  4. See TagTypeDef
  5. See CreateActionResponseTypeDef 
# create_action method usage example with argument unpacking

kwargs: CreateActionRequestRequestTypeDef = {  # (1)
    "ActionName": ...,
    "Source": ...,
    "ActionType": ...,
}

parent.create_action(**kwargs)
  1. See CreateActionRequestRequestTypeDef

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)
    ...
  1. See TrainingSpecificationTypeDef
  2. See InferenceSpecificationTypeDef
  3. See AlgorithmValidationSpecificationTypeDef
  4. See TagTypeDef
  5. See CreateAlgorithmOutputTypeDef 
# create_algorithm method usage example with argument unpacking

kwargs: CreateAlgorithmInputRequestTypeDef = {  # (1)
    "AlgorithmName": ...,
    "TrainingSpecification": ...,
}

parent.create_algorithm(**kwargs)
  1. See CreateAlgorithmInputRequestTypeDef

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)
    ...
  1. See AppTypeType
  2. See TagTypeDef
  3. See ResourceSpecTypeDef
  4. See CreateAppResponseTypeDef 
# create_app method usage example with argument unpacking

kwargs: CreateAppRequestRequestTypeDef = {  # (1)
    "DomainId": ...,
    "AppType": ...,
    "AppName": ...,
}

parent.create_app(**kwargs)
  1. See CreateAppRequestRequestTypeDef

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)
    ...
  1. See TagTypeDef
  2. See KernelGatewayImageConfigTypeDef
  3. See CreateAppImageConfigResponseTypeDef 
# create_app_image_config method usage example with argument unpacking

kwargs: CreateAppImageConfigRequestRequestTypeDef = {  # (1)
    "AppImageConfigName": ...,
}

parent.create_app_image_config(**kwargs)
  1. See CreateAppImageConfigRequestRequestTypeDef

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)
    ...
  1. See ArtifactSourceTypeDef
  2. See MetadataPropertiesTypeDef
  3. See TagTypeDef
  4. See CreateArtifactResponseTypeDef 
# create_artifact method usage example with argument unpacking

kwargs: CreateArtifactRequestRequestTypeDef = {  # (1)
    "Source": ...,
    "ArtifactType": ...,
}

parent.create_artifact(**kwargs)
  1. See CreateArtifactRequestRequestTypeDef

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)
    ...
  1. See AutoMLChannelTypeDef
  2. See AutoMLOutputDataConfigTypeDef
  3. See ProblemTypeType
  4. See AutoMLJobObjectiveTypeDef
  5. See AutoMLJobConfigTypeDef
  6. See TagTypeDef
  7. See ModelDeployConfigTypeDef
  8. 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)
  1. See CreateAutoMLJobRequestRequestTypeDef

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)
    ...
  1. See AutoMLJobChannelTypeDef
  2. See AutoMLOutputDataConfigTypeDef
  3. See AutoMLProblemTypeConfigTypeDef
  4. See TagTypeDef
  5. See AutoMLSecurityConfigTypeDef
  6. See AutoMLJobObjectiveTypeDef
  7. See ModelDeployConfigTypeDef
  8. See AutoMLDataSplitConfigTypeDef
  9. 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)
  1. See CreateAutoMLJobV2RequestRequestTypeDef

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)
    ...
  1. See GitConfigTypeDef
  2. See TagTypeDef
  3. See CreateCodeRepositoryOutputTypeDef 
# create_code_repository method usage example with argument unpacking

kwargs: CreateCodeRepositoryInputRequestTypeDef = {  # (1)
    "CodeRepositoryName": ...,
    "GitConfig": ...,
}

parent.create_code_repository(**kwargs)
  1. See CreateCodeRepositoryInputRequestTypeDef

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)
    ...
  1. See OutputConfigTypeDef
  2. See StoppingConditionTypeDef
  3. See InputConfigTypeDef
  4. See NeoVpcConfigTypeDef
  5. See TagTypeDef
  6. See CreateCompilationJobResponseTypeDef 
# create_compilation_job method usage example with argument unpacking

kwargs: CreateCompilationJobRequestRequestTypeDef = {  # (1)
    "CompilationJobName": ...,
    "RoleArn": ...,
    "OutputConfig": ...,
    "StoppingCondition": ...,
}

parent.create_compilation_job(**kwargs)
  1. See CreateCompilationJobRequestRequestTypeDef

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)
    ...
  1. See ContextSourceTypeDef
  2. See TagTypeDef
  3. See CreateContextResponseTypeDef 
# create_context method usage example with argument unpacking

kwargs: CreateContextRequestRequestTypeDef = {  # (1)
    "ContextName": ...,
    "Source": ...,
    "ContextType": ...,
}

parent.create_context(**kwargs)
  1. See CreateContextRequestRequestTypeDef

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)
    ...
  1. See DataQualityAppSpecificationTypeDef
  2. See DataQualityJobInputTypeDef
  3. See MonitoringOutputConfigTypeDef
  4. See MonitoringResourcesTypeDef
  5. See DataQualityBaselineConfigTypeDef
  6. See MonitoringNetworkConfigTypeDef
  7. See MonitoringStoppingConditionTypeDef
  8. See TagTypeDef
  9. 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)
  1. See CreateDataQualityJobDefinitionRequestRequestTypeDef

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)
    ...
  1. See EdgeOutputConfigTypeDef
  2. See TagTypeDef
  3. See EmptyResponseMetadataTypeDef 
# create_device_fleet method usage example with argument unpacking

kwargs: CreateDeviceFleetRequestRequestTypeDef = {  # (1)
    "DeviceFleetName": ...,
    "OutputConfig": ...,
}

parent.create_device_fleet(**kwargs)
  1. See CreateDeviceFleetRequestRequestTypeDef

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)
    ...
  1. See AuthModeType
  2. See UserSettingsTypeDef
  3. See TagTypeDef
  4. See AppNetworkAccessTypeType
  5. See AppSecurityGroupManagementType
  6. See DomainSettingsTypeDef
  7. See DefaultSpaceSettingsTypeDef
  8. See CreateDomainResponseTypeDef 
# create_domain method usage example with argument unpacking

kwargs: CreateDomainRequestRequestTypeDef = {  # (1)
    "DomainName": ...,
    "AuthMode": ...,
    "DefaultUserSettings": ...,
    "SubnetIds": ...,
    "VpcId": ...,
}

parent.create_domain(**kwargs)
  1. See CreateDomainRequestRequestTypeDef

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)
    ...
  1. See EdgeDeploymentModelConfigTypeDef
  2. See DeploymentStageTypeDef
  3. See TagTypeDef
  4. See CreateEdgeDeploymentPlanResponseTypeDef 
# create_edge_deployment_plan method usage example with argument unpacking

kwargs: CreateEdgeDeploymentPlanRequestRequestTypeDef = {  # (1)
    "EdgeDeploymentPlanName": ...,
    "ModelConfigs": ...,
    "DeviceFleetName": ...,
}

parent.create_edge_deployment_plan(**kwargs)
  1. See CreateEdgeDeploymentPlanRequestRequestTypeDef

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)
    ...
  1. See DeploymentStageTypeDef
  2. See EmptyResponseMetadataTypeDef 
# create_edge_deployment_stage method usage example with argument unpacking

kwargs: CreateEdgeDeploymentStageRequestRequestTypeDef = {  # (1)
    "EdgeDeploymentPlanName": ...,
    "Stages": ...,
}

parent.create_edge_deployment_stage(**kwargs)
  1. See CreateEdgeDeploymentStageRequestRequestTypeDef

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)
    ...
  1. See EdgeOutputConfigTypeDef
  2. See TagTypeDef
  3. See EmptyResponseMetadataTypeDef 
# 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)
  1. See CreateEdgePackagingJobRequestRequestTypeDef

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)
    ...
  1. See DeploymentConfigTypeDef
  2. See TagTypeDef
  3. See CreateEndpointOutputTypeDef 
# create_endpoint method usage example with argument unpacking

kwargs: CreateEndpointInputRequestTypeDef = {  # (1)
    "EndpointName": ...,
    "EndpointConfigName": ...,
}

parent.create_endpoint(**kwargs)
  1. See CreateEndpointInputRequestTypeDef

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)
    ...
  1. See ProductionVariantTypeDef
  2. See DataCaptureConfigTypeDef
  3. See TagTypeDef
  4. See AsyncInferenceConfigTypeDef
  5. See ExplainerConfigTypeDef
  6. See ProductionVariantTypeDef
  7. See CreateEndpointConfigOutputTypeDef 
# create_endpoint_config method usage example with argument unpacking

kwargs: CreateEndpointConfigInputRequestTypeDef = {  # (1)
    "EndpointConfigName": ...,
    "ProductionVariants": ...,
}

parent.create_endpoint_config(**kwargs)
  1. See CreateEndpointConfigInputRequestTypeDef

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)
    ...
  1. See TagTypeDef
  2. See CreateExperimentResponseTypeDef 
# create_experiment method usage example with argument unpacking

kwargs: CreateExperimentRequestRequestTypeDef = {  # (1)
    "ExperimentName": ...,
}

parent.create_experiment(**kwargs)
  1. See CreateExperimentRequestRequestTypeDef

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)
    ...
  1. See FeatureDefinitionTypeDef
  2. See OnlineStoreConfigTypeDef
  3. See OfflineStoreConfigTypeDef
  4. See TagTypeDef
  5. See CreateFeatureGroupResponseTypeDef 
# create_feature_group method usage example with argument unpacking

kwargs: CreateFeatureGroupRequestRequestTypeDef = {  # (1)
    "FeatureGroupName": ...,
    "RecordIdentifierFeatureName": ...,
    "EventTimeFeatureName": ...,
    "FeatureDefinitions": ...,
}

parent.create_feature_group(**kwargs)
  1. See CreateFeatureGroupRequestRequestTypeDef

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)
    ...
  1. See HumanLoopConfigTypeDef
  2. See FlowDefinitionOutputConfigTypeDef
  3. See HumanLoopRequestSourceTypeDef
  4. See HumanLoopActivationConfigTypeDef
  5. See TagTypeDef
  6. See CreateFlowDefinitionResponseTypeDef 
# create_flow_definition method usage example with argument unpacking

kwargs: CreateFlowDefinitionRequestRequestTypeDef = {  # (1)
    "FlowDefinitionName": ...,
    "HumanLoopConfig": ...,
    "OutputConfig": ...,
    "RoleArn": ...,
}

parent.create_flow_definition(**kwargs)
  1. See CreateFlowDefinitionRequestRequestTypeDef

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)
    ...
  1. See HubS3StorageConfigTypeDef
  2. See TagTypeDef
  3. See CreateHubResponseTypeDef 
# create_hub method usage example with argument unpacking

kwargs: CreateHubRequestRequestTypeDef = {  # (1)
    "HubName": ...,
    "HubDescription": ...,
}

parent.create_hub(**kwargs)
  1. See CreateHubRequestRequestTypeDef

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)
    ...
  1. See UiTemplateTypeDef
  2. See TagTypeDef
  3. See CreateHumanTaskUiResponseTypeDef 
# create_human_task_ui method usage example with argument unpacking

kwargs: CreateHumanTaskUiRequestRequestTypeDef = {  # (1)
    "HumanTaskUiName": ...,
    "UiTemplate": ...,
}

parent.create_human_task_ui(**kwargs)
  1. See CreateHumanTaskUiRequestRequestTypeDef

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)
    ...
  1. See HyperParameterTuningJobConfigTypeDef
  2. See HyperParameterTrainingJobDefinitionTypeDef
  3. See HyperParameterTrainingJobDefinitionTypeDef
  4. See HyperParameterTuningJobWarmStartConfigTypeDef
  5. See TagTypeDef
  6. See AutotuneTypeDef
  7. 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)
  1. See CreateHyperParameterTuningJobRequestRequestTypeDef

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)
    ...
  1. See TagTypeDef
  2. See CreateImageResponseTypeDef 
# create_image method usage example with argument unpacking

kwargs: CreateImageRequestRequestTypeDef = {  # (1)
    "ImageName": ...,
    "RoleArn": ...,
}

parent.create_image(**kwargs)
  1. See CreateImageRequestRequestTypeDef

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)
    ...
  1. See VendorGuidanceType
  2. See JobTypeType
  3. See ProcessorType
  4. See CreateImageVersionResponseTypeDef 
# create_image_version method usage example with argument unpacking

kwargs: CreateImageVersionRequestRequestTypeDef = {  # (1)
    "BaseImage": ...,
    "ClientToken": ...,
    "ImageName": ...,
}

parent.create_image_version(**kwargs)
  1. See CreateImageVersionRequestRequestTypeDef

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)
    ...
  1. See InferenceExperimentTypeType
  2. See ModelVariantConfigTypeDef
  3. See ShadowModeConfigTypeDef
  4. See InferenceExperimentScheduleTypeDef
  5. See InferenceExperimentDataStorageConfigTypeDef
  6. See TagTypeDef
  7. 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)
  1. See CreateInferenceExperimentRequestRequestTypeDef

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)
    ...
  1. See RecommendationJobTypeType
  2. See RecommendationJobInputConfigTypeDef
  3. See RecommendationJobStoppingConditionsTypeDef
  4. See RecommendationJobOutputConfigTypeDef
  5. See TagTypeDef
  6. 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)
  1. See CreateInferenceRecommendationsJobRequestRequestTypeDef

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)
    ...
  1. See LabelingJobInputConfigTypeDef
  2. See LabelingJobOutputConfigTypeDef
  3. See HumanTaskConfigTypeDef
  4. See LabelingJobStoppingConditionsTypeDef
  5. See LabelingJobAlgorithmsConfigTypeDef
  6. See TagTypeDef
  7. 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)
  1. See CreateLabelingJobRequestRequestTypeDef

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)
    ...
  1. See ContainerDefinitionTypeDef
  2. See ContainerDefinitionTypeDef
  3. See InferenceExecutionConfigTypeDef
  4. See TagTypeDef
  5. See VpcConfigTypeDef
  6. See CreateModelOutputTypeDef 
# create_model method usage example with argument unpacking

kwargs: CreateModelInputRequestTypeDef = {  # (1)
    "ModelName": ...,
    "ExecutionRoleArn": ...,
}

parent.create_model(**kwargs)
  1. See CreateModelInputRequestTypeDef

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)
    ...
  1. See ModelBiasAppSpecificationTypeDef
  2. See ModelBiasJobInputTypeDef
  3. See MonitoringOutputConfigTypeDef
  4. See MonitoringResourcesTypeDef
  5. See ModelBiasBaselineConfigTypeDef
  6. See MonitoringNetworkConfigTypeDef
  7. See MonitoringStoppingConditionTypeDef
  8. See TagTypeDef
  9. 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)
  1. See CreateModelBiasJobDefinitionRequestRequestTypeDef

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)
    ...
  1. See ModelCardStatusType
  2. See ModelCardSecurityConfigTypeDef
  3. See TagTypeDef
  4. See CreateModelCardResponseTypeDef 
# create_model_card method usage example with argument unpacking

kwargs: CreateModelCardRequestRequestTypeDef = {  # (1)
    "ModelCardName": ...,
    "Content": ...,
    "ModelCardStatus": ...,
}

parent.create_model_card(**kwargs)
  1. See CreateModelCardRequestRequestTypeDef

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.