sagemaker-2017-07-24
library
Classes
-
ActionSource
-
A structure describing the source of an action.
-
ActionSummary
-
Lists the properties of an action. An action represents an action or
activity. Some examples are a workflow step and a model deployment.
Generally, an action involves at least one input artifact or output
artifact.
-
AddAssociationResponse
-
-
AddTagsOutput
-
-
AgentVersion
-
Edge Manager agent version.
-
Alarm
-
This API is not supported.
-
AlgorithmSpecification
-
Specifies the training algorithm to use in a CreateTrainingJob
request.
-
AlgorithmStatusDetails
-
Specifies the validation and image scan statuses of the algorithm.
-
AlgorithmStatusItem
-
Represents the overall status of an algorithm.
-
AlgorithmSummary
-
Provides summary information about an algorithm.
-
AlgorithmValidationProfile
-
Defines a training job and a batch transform job that Amazon SageMaker runs
to validate your algorithm.
-
AlgorithmValidationSpecification
-
Specifies configurations for one or more training jobs that Amazon SageMaker
runs to test the algorithm.
-
AnnotationConsolidationConfig
-
Configures how labels are consolidated across human workers and processes
output data.
-
AppDetails
-
Details about an Amazon SageMaker app.
-
AppImageConfigDetails
-
The configuration for running a SageMaker image as a KernelGateway app.
-
AppSpecification
-
Configuration to run a processing job in a specified container image.
-
ArtifactSource
-
A structure describing the source of an artifact.
-
ArtifactSourceType
-
The ID and ID type of an artifact source.
-
ArtifactSummary
-
Lists a summary of the properties of an artifact. An artifact represents a
URI addressable object or data. Some examples are a dataset and a model.
-
AssociateTrialComponentResponse
-
-
AssociationSummary
-
Lists a summary of the properties of an association. An association is an
entity that links other lineage or experiment entities. An example would be
an association between a training job and a model.
-
AthenaDatasetDefinition
-
Configuration for Athena Dataset Definition input.
-
AutoMLCandidate
-
An Autopilot job returns recommendations, or candidates. Each candidate has
futher details about the steps involed, and the status.
-
AutoMLCandidateStep
-
Information about the steps for a Candidate, and what step it is working on.
-
AutoMLChannel
-
Similar to Channel. A channel is a named input source that training
algorithms can consume. Refer to Channel for detailed descriptions.
-
AutoMLContainerDefinition
-
A list of container definitions that describe the different containers that
make up one AutoML candidate. Refer to ContainerDefinition for more details.
-
AutoMLDataSource
-
The data source for the Autopilot job.
-
AutoMLJobArtifacts
-
Artifacts that are generation during a job.
-
AutoMLJobCompletionCriteria
-
How long a job is allowed to run, or how many candidates a job is allowed to
generate.
-
AutoMLJobConfig
-
A collection of settings used for a job.
-
AutoMLJobObjective
-
Specifies a metric to minimize or maximize as the objective of a job.
-
AutoMLJobSummary
-
Provides a summary about a job.
-
AutoMLOutputDataConfig
-
The output data configuration.
-
AutoMLS3DataSource
-
The Amazon S3 data source.
-
AutoMLSecurityConfig
-
Security options.
-
AutoRollbackConfig
-
Currently, the
AutoRollbackConfig
API is not supported.
-
AwsClientCredentials
-
AWS credentials.
-
Bias
-
Contains bias metrics for a model.
-
BlueGreenUpdatePolicy
-
Currently, the
BlueGreenUpdatePolicy
API is not supported.
-
CacheHitResult
-
Details on the cache hit of a pipeline execution step.
-
CapacitySize
-
Currently, the
CapacitySize
API is not supported.
-
-
CaptureOption
-
-
CategoricalParameterRange
-
A list of categorical hyperparameters to tune.
-
CategoricalParameterRangeSpecification
-
Defines the possible values for a categorical hyperparameter.
-
Channel
-
A channel is a named input source that training algorithms can consume.
-
ChannelSpecification
-
Defines a named input source, called a channel, to be used by an algorithm.
-
CheckpointConfig
-
Contains information about the output location for managed spot training
checkpoint data.
-
CodeRepositorySummary
-
Specifies summary information about a Git repository.
-
CognitoConfig
-
Use this parameter to configure your Amazon Cognito workforce. A single
Cognito workforce is created using and corresponds to a single
Amazon Cognito user pool.
-
CognitoMemberDefinition
-
Identifies a Amazon Cognito user group. A user group can be used in on or
more work teams.
-
CollectionConfiguration
-
Configuration information for the Debugger output tensor collections.
-
CompilationJobSummary
-
A summary of a model compilation job.
-
ConditionStepMetadata
-
Metadata for a Condition step.
-
ContainerDefinition
-
Describes the container, as part of model definition.
-
ContextSource
-
A structure describing the source of a context.
-
ContextSummary
-
Lists a summary of the properties of a context. A context provides a logical
grouping of other entities.
-
ContinuousParameterRange
-
A list of continuous hyperparameters to tune.
-
ContinuousParameterRangeSpecification
-
Defines the possible values for a continuous hyperparameter.
-
CreateActionResponse
-
-
CreateAlgorithmOutput
-
-
CreateAppImageConfigResponse
-
-
CreateAppResponse
-
-
CreateArtifactResponse
-
-
CreateAutoMLJobResponse
-
-
CreateCodeRepositoryOutput
-
-
CreateCompilationJobResponse
-
-
CreateContextResponse
-
-
CreateDataQualityJobDefinitionResponse
-
-
CreateDomainResponse
-
-
CreateEndpointConfigOutput
-
-
CreateEndpointOutput
-
-
CreateExperimentResponse
-
-
CreateFeatureGroupResponse
-
-
CreateFlowDefinitionResponse
-
-
CreateHumanTaskUiResponse
-
-
CreateHyperParameterTuningJobResponse
-
-
CreateImageResponse
-
-
CreateImageVersionResponse
-
-
CreateLabelingJobResponse
-
-
CreateModelBiasJobDefinitionResponse
-
-
CreateModelExplainabilityJobDefinitionResponse
-
-
CreateModelOutput
-
-
CreateModelPackageGroupOutput
-
-
CreateModelPackageOutput
-
-
CreateModelQualityJobDefinitionResponse
-
-
CreateMonitoringScheduleResponse
-
-
CreateNotebookInstanceLifecycleConfigOutput
-
-
CreateNotebookInstanceOutput
-
-
CreatePipelineResponse
-
-
CreatePresignedDomainUrlResponse
-
-
CreatePresignedNotebookInstanceUrlOutput
-
-
CreateProcessingJobResponse
-
-
CreateProjectOutput
-
-
CreateTrainingJobResponse
-
-
CreateTransformJobResponse
-
-
CreateTrialComponentResponse
-
-
CreateTrialResponse
-
-
CreateUserProfileResponse
-
-
CreateWorkforceResponse
-
-
CreateWorkteamResponse
-
-
CustomImage
-
A custom SageMaker image. For more information, see Bring
your own SageMaker image.
-
DataCaptureConfig
-
-
DataCaptureConfigSummary
-
-
DataCatalogConfig
-
The meta data of the Glue table which serves as data catalog for the
OfflineStore
.
-
DataProcessing
-
The data structure used to specify the data to be used for inference in a
batch transform job and to associate the data that is relevant to the
prediction results in the output. The input filter provided allows you to
exclude input data that is not needed for inference in a batch transform
job. The output filter provided allows you to include input data relevant to
interpreting the predictions in the output from the job. For more
information, see Associate
Prediction Results with their Corresponding Input Records.
-
DataQualityAppSpecification
-
Information about the container that a data quality monitoring job runs.
-
DataQualityBaselineConfig
-
Configuration for monitoring constraints and monitoring statistics. These
baseline resources are compared against the results of the current job from
the series of jobs scheduled to collect data periodically.
-
DataQualityJobInput
-
The input for the data quality monitoring job. Currently endpoints are
supported for input.
-
DatasetDefinition
-
Configuration for Dataset Definition inputs. The Dataset Definition input
must specify exactly one of either
AthenaDatasetDefinition
or
RedshiftDatasetDefinition
types.
-
DataSource
-
Describes the location of the channel data.
-
DebugHookConfig
-
Configuration information for the Debugger hook parameters, metric and
tensor collections, and storage paths. To learn more about how to configure
the
DebugHookConfig
parameter, see Use
the SageMaker and Debugger Configuration API Operations to Create, Update,
and Debug Your Training Job.
-
DebugRuleConfiguration
-
Configuration information for SageMaker Debugger rules for debugging. To
learn more about how to configure the
DebugRuleConfiguration
parameter, see Use
the SageMaker and Debugger Configuration API Operations to Create, Update,
and Debug Your Training Job.
-
DebugRuleEvaluationStatus
-
Information about the status of the rule evaluation.
-
DeleteActionResponse
-
-
DeleteArtifactResponse
-
-
DeleteAssociationResponse
-
-
DeleteContextResponse
-
-
DeleteExperimentResponse
-
-
DeleteFlowDefinitionResponse
-
-
DeleteHumanTaskUiResponse
-
-
DeleteImageResponse
-
-
DeleteImageVersionResponse
-
-
DeletePipelineResponse
-
-
DeleteTagsOutput
-
-
DeleteTrialComponentResponse
-
-
DeleteTrialResponse
-
-
DeleteWorkforceResponse
-
-
DeleteWorkteamResponse
-
-
DeployedImage
-
Gets the Amazon EC2 Container Registry path of the docker image of the model
that is hosted in this ProductionVariant.
-
DeploymentConfig
-
Currently, the
DeploymentConfig
API is not supported.
-
DescribeActionResponse
-
-
DescribeAlgorithmOutput
-
-
DescribeAppImageConfigResponse
-
-
DescribeAppResponse
-
-
DescribeArtifactResponse
-
-
DescribeAutoMLJobResponse
-
-
DescribeCodeRepositoryOutput
-
-
DescribeCompilationJobResponse
-
-
DescribeContextResponse
-
-
DescribeDataQualityJobDefinitionResponse
-
-
DescribeDeviceFleetResponse
-
-
DescribeDeviceResponse
-
-
DescribeDomainResponse
-
-
DescribeEdgePackagingJobResponse
-
-
DescribeEndpointConfigOutput
-
-
DescribeEndpointOutput
-
-
DescribeExperimentResponse
-
-
DescribeFeatureGroupResponse
-
-
DescribeFlowDefinitionResponse
-
-
DescribeHumanTaskUiResponse
-
-
DescribeHyperParameterTuningJobResponse
-
-
DescribeImageResponse
-
-
DescribeImageVersionResponse
-
-
DescribeLabelingJobResponse
-
-
DescribeModelBiasJobDefinitionResponse
-
-
DescribeModelExplainabilityJobDefinitionResponse
-
-
DescribeModelOutput
-
-
DescribeModelPackageGroupOutput
-
-
DescribeModelPackageOutput
-
-
DescribeModelQualityJobDefinitionResponse
-
-
DescribeMonitoringScheduleResponse
-
-
DescribeNotebookInstanceLifecycleConfigOutput
-
-
DescribeNotebookInstanceOutput
-
-
DescribePipelineDefinitionForExecutionResponse
-
-
DescribePipelineExecutionResponse
-
-
DescribePipelineResponse
-
-
DescribeProcessingJobResponse
-
-
DescribeProjectOutput
-
-
DescribeSubscribedWorkteamResponse
-
-
DescribeTrainingJobResponse
-
-
DescribeTransformJobResponse
-
-
DescribeTrialComponentResponse
-
-
DescribeTrialResponse
-
-
DescribeUserProfileResponse
-
-
DescribeWorkforceResponse
-
-
DescribeWorkteamResponse
-
-
DesiredWeightAndCapacity
-
Specifies weight and capacity values for a production variant.
-
Device
-
Information of a particular device.
-
DeviceFleetSummary
-
Summary of the device fleet.
-
DeviceStats
-
Status of devices.
-
DeviceSummary
-
Summary of the device.
-
DisableSagemakerServicecatalogPortfolioOutput
-
-
DisassociateTrialComponentResponse
-
-
DomainDetails
-
The domain's details.
-
EdgeModel
-
The model on the edge device.
-
EdgeModelStat
-
Status of edge devices with this model.
-
EdgeModelSummary
-
Summary of model on edge device.
-
EdgeOutputConfig
-
The output configuration.
-
EdgePackagingJobSummary
-
Summary of edge packaging job.
-
EnableSagemakerServicecatalogPortfolioOutput
-
-
Endpoint
-
A hosted endpoint for real-time inference.
-
EndpointConfigSummary
-
Provides summary information for an endpoint configuration.
-
EndpointInput
-
Input object for the endpoint
-
EndpointSummary
-
Provides summary information for an endpoint.
-
Experiment
-
The properties of an experiment as returned by the Search API.
-
ExperimentConfig
-
Associates a SageMaker job as a trial component with an experiment and
trial. Specified when you call the following APIs:
-
ExperimentSource
-
The source of the experiment.
-
ExperimentSummary
-
A summary of the properties of an experiment. To get the complete set of
properties, call the DescribeExperiment API and provide the
ExperimentName
.
-
Explainability
-
Contains explainability metrics for a model.
-
FeatureDefinition
-
A list of features. You must include
FeatureName
and
FeatureType
. Valid feature FeatureType
s are
Integral
, Fractional
and String
.
-
FeatureGroup
-
Amazon SageMaker Feature Store stores features in a collection called
Feature Group. A Feature Group can be visualized as a table which has rows,
with a unique identifier for each row where each column in the table is a
feature. In principle, a Feature Group is composed of features and values
per features.
-
FeatureGroupSummary
-
The name, Arn,
CreationTime
, FeatureGroup
values,
LastUpdatedTime
and EnableOnlineStorage
status of
a FeatureGroup
.
-
FileSystemConfig
-
The Amazon Elastic File System (EFS) storage configuration for a SageMaker
image.
-
FileSystemDataSource
-
Specifies a file system data source for a channel.
-
Filter
-
A conditional statement for a search expression that includes a resource
property, a Boolean operator, and a value. Resources that match the
statement are returned in the results from the Search API.
-
FinalAutoMLJobObjectiveMetric
-
The best candidate result from an AutoML training job.
-
FinalHyperParameterTuningJobObjectiveMetric
-
Shows the final value for the objective metric for a training job that was
launched by a hyperparameter tuning job. You define the objective metric in
the
HyperParameterTuningJobObjective
parameter of
HyperParameterTuningJobConfig.
-
FlowDefinitionOutputConfig
-
Contains information about where human output will be stored.
-
FlowDefinitionSummary
-
Contains summary information about the flow definition.
-
GetDeviceFleetReportResponse
-
-
GetModelPackageGroupPolicyOutput
-
-
GetSagemakerServicecatalogPortfolioStatusOutput
-
-
GetSearchSuggestionsResponse
-
-
GitConfig
-
Specifies configuration details for a Git repository in your AWS account.
-
GitConfigForUpdate
-
Specifies configuration details for a Git repository when the repository is
updated.
-
HumanLoopActivationConditionsConfig
-
Defines under what conditions SageMaker creates a human loop. Used within .
See for the required format of activation conditions.
-
HumanLoopActivationConfig
-
Provides information about how and under what conditions SageMaker creates a
human loop. If
HumanLoopActivationConfig
is not given, then all
requests go to humans.
-
HumanLoopConfig
-
Describes the work to be performed by human workers.
-
HumanLoopRequestSource
-
Container for configuring the source of human task requests.
-
HumanTaskConfig
-
Information required for human workers to complete a labeling task.
-
HumanTaskUiSummary
-
Container for human task user interface information.
-
HyperParameterAlgorithmSpecification
-
Specifies which training algorithm to use for training jobs that a
hyperparameter tuning job launches and the metrics to monitor.
-
HyperParameterSpecification
-
Defines a hyperparameter to be used by an algorithm.
-
HyperParameterTrainingJobDefinition
-
Defines the training jobs launched by a hyperparameter tuning job.
-
HyperParameterTrainingJobSummary
-
Specifies summary information about a training job.
-
HyperParameterTuningJobConfig
-
Configures a hyperparameter tuning job.
-
HyperParameterTuningJobObjective
-
Defines the objective metric for a hyperparameter tuning job. Hyperparameter
tuning uses the value of this metric to evaluate the training jobs it
launches, and returns the training job that results in either the highest or
lowest value for this metric, depending on the value you specify for the
Type
parameter.
-
HyperParameterTuningJobSummary
-
Provides summary information about a hyperparameter tuning job.
-
HyperParameterTuningJobWarmStartConfig
-
Specifies the configuration for a hyperparameter tuning job that uses one or
more previous hyperparameter tuning jobs as a starting point. The results of
previous tuning jobs are used to inform which combinations of
hyperparameters to search over in the new tuning job.
-
Image
-
A SageMaker image. A SageMaker image represents a set of container images
that are derived from a common base container image. Each of these container
images is represented by a SageMaker
ImageVersion
.
-
ImageConfig
-
Specifies whether the model container is in Amazon ECR or a private Docker
registry accessible from your Amazon Virtual Private Cloud (VPC).
-
ImageVersion
-
A version of a SageMaker
Image
. A version represents an
existing container image.
-
InferenceSpecification
-
Defines how to perform inference generation after a training job is run.
-
InputConfig
-
Contains information about the location of input model artifacts, the name
and shape of the expected data inputs, and the framework in which the model
was trained.
-
IntegerParameterRange
-
For a hyperparameter of the integer type, specifies the range that a
hyperparameter tuning job searches.
-
IntegerParameterRangeSpecification
-
Defines the possible values for an integer hyperparameter.
-
JupyterServerAppSettings
-
The JupyterServer app settings.
-
KernelGatewayAppSettings
-
The KernelGateway app settings.
-
KernelGatewayImageConfig
-
The configuration for the file system and kernels in a SageMaker image
running as a KernelGateway app.
-
KernelSpec
-
The specification of a Jupyter kernel.
-
LabelCounters
-
Provides a breakdown of the number of objects labeled.
-
LabelCountersForWorkteam
-
Provides counts for human-labeled tasks in the labeling job.
-
LabelingJobAlgorithmsConfig
-
Provides configuration information for auto-labeling of your data objects. A
LabelingJobAlgorithmsConfig
object must be supplied in order to
use auto-labeling.
-
LabelingJobDataAttributes
-
Attributes of the data specified by the customer. Use these to describe the
data to be labeled.
-
LabelingJobDataSource
-
Provides information about the location of input data.
-
LabelingJobForWorkteamSummary
-
Provides summary information for a work team.
-
LabelingJobInputConfig
-
Input configuration information for a labeling job.
-
LabelingJobOutput
-
Specifies the location of the output produced by the labeling job.
-
LabelingJobOutputConfig
-
Output configuration information for a labeling job.
-
LabelingJobResourceConfig
-
Provides configuration information for labeling jobs.
-
LabelingJobS3DataSource
-
The Amazon S3 location of the input data objects.
-
LabelingJobSnsDataSource
-
An Amazon SNS data source used for streaming labeling jobs.
-
LabelingJobStoppingConditions
-
A set of conditions for stopping a labeling job. If any of the conditions
are met, the job is automatically stopped. You can use these conditions to
control the cost of data labeling.
-
LabelingJobSummary
-
Provides summary information about a labeling job.
-
ListActionsResponse
-
-
ListAlgorithmsOutput
-
-
ListAppImageConfigsResponse
-
-
ListAppsResponse
-
-
ListArtifactsResponse
-
-
ListAssociationsResponse
-
-
ListAutoMLJobsResponse
-
-
ListCandidatesForAutoMLJobResponse
-
-
ListCodeRepositoriesOutput
-
-
ListCompilationJobsResponse
-
-
ListContextsResponse
-
-
ListDataQualityJobDefinitionsResponse
-
-
ListDeviceFleetsResponse
-
-
ListDevicesResponse
-
-
ListDomainsResponse
-
-
ListEdgePackagingJobsResponse
-
-
ListEndpointConfigsOutput
-
-
ListEndpointsOutput
-
-
ListExperimentsResponse
-
-
ListFeatureGroupsResponse
-
-
ListFlowDefinitionsResponse
-
-
ListHumanTaskUisResponse
-
-
ListHyperParameterTuningJobsResponse
-
-
ListImagesResponse
-
-
ListImageVersionsResponse
-
-
ListLabelingJobsForWorkteamResponse
-
-
ListLabelingJobsResponse
-
-
ListModelBiasJobDefinitionsResponse
-
-
ListModelExplainabilityJobDefinitionsResponse
-
-
ListModelPackageGroupsOutput
-
-
ListModelPackagesOutput
-
-
ListModelQualityJobDefinitionsResponse
-
-
ListModelsOutput
-
-
ListMonitoringExecutionsResponse
-
-
ListMonitoringSchedulesResponse
-
-
ListNotebookInstanceLifecycleConfigsOutput
-
-
ListNotebookInstancesOutput
-
-
ListPipelineExecutionsResponse
-
-
ListPipelineExecutionStepsResponse
-
-
ListPipelineParametersForExecutionResponse
-
-
ListPipelinesResponse
-
-
ListProcessingJobsResponse
-
-
ListProjectsOutput
-
-
ListSubscribedWorkteamsResponse
-
-
ListTagsOutput
-
-
ListTrainingJobsForHyperParameterTuningJobResponse
-
-
ListTrainingJobsResponse
-
-
ListTransformJobsResponse
-
-
ListTrialComponentsResponse
-
-
ListTrialsResponse
-
-
ListUserProfilesResponse
-
-
ListWorkforcesResponse
-
-
ListWorkteamsResponse
-
-
MemberDefinition
-
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a
work team.
-
MetadataProperties
-
Metadata properties of the tracking entity, trial, or trial component.
-
MetricData
-
The name, value, and date and time of a metric that was emitted to Amazon
CloudWatch.
-
MetricDefinition
-
Specifies a metric that the training algorithm writes to
stderr
or stdout
. Amazon SageMakerhyperparameter tuning captures all
defined metrics. You specify one metric that a hyperparameter tuning job
uses as its objective metric to choose the best training job.
-
MetricsSource
-
-
ModelArtifacts
-
Provides information about the location that is configured for storing model
artifacts.
-
ModelBiasAppSpecification
-
Docker container image configuration object for the model bias job.
-
ModelBiasBaselineConfig
-
The configuration for a baseline model bias job.
-
ModelBiasJobInput
-
Inputs for the model bias job.
-
ModelClientConfig
-
Configures the timeout and maximum number of retries for processing a
transform job invocation.
-
ModelDataQuality
-
Data quality constraints and statistics for a model.
-
ModelDigests
-
Provides information to verify the integrity of stored model artifacts.
-
ModelExplainabilityAppSpecification
-
Docker container image configuration object for the model explainability
job.
-
ModelExplainabilityBaselineConfig
-
The configuration for a baseline model explainability job.
-
ModelExplainabilityJobInput
-
Inputs for the model explainability job.
-
ModelMetrics
-
Contains metrics captured from a model.
-
ModelPackage
-
A versioned model that can be deployed for SageMaker inference.
-
ModelPackageContainerDefinition
-
Describes the Docker container for the model package.
-
ModelPackageGroup
-
A group of versioned models in the model registry.
-
ModelPackageGroupSummary
-
Summary information about a model group.
-
ModelPackageStatusDetails
-
Specifies the validation and image scan statuses of the model package.
-
ModelPackageStatusItem
-
Represents the overall status of a model package.
-
ModelPackageSummary
-
Provides summary information about a model package.
-
ModelPackageValidationProfile
-
Contains data, such as the inputs and targeted instance types that are used
in the process of validating the model package.
-
ModelPackageValidationSpecification
-
Specifies batch transform jobs that Amazon SageMaker runs to validate your
model package.
-
ModelQuality
-
Model quality statistics and constraints.
-
ModelQualityAppSpecification
-
Container image configuration object for the monitoring job.
-
ModelQualityBaselineConfig
-
Configuration for monitoring constraints and monitoring statistics. These
baseline resources are compared against the results of the current job from
the series of jobs scheduled to collect data periodically.
-
ModelQualityJobInput
-
The input for the model quality monitoring job. Currently endponts are
supported for input for model quality monitoring jobs.
-
ModelStepMetadata
-
Metadata for Model steps.
-
ModelSummary
-
Provides summary information about a model.
-
MonitoringAppSpecification
-
Container image configuration object for the monitoring job.
-
MonitoringBaselineConfig
-
Configuration for monitoring constraints and monitoring statistics. These
baseline resources are compared against the results of the current job from
the series of jobs scheduled to collect data periodically.
-
MonitoringClusterConfig
-
Configuration for the cluster used to run model monitoring jobs.
-
MonitoringConstraintsResource
-
The constraints resource for a monitoring job.
-
MonitoringExecutionSummary
-
Summary of information about the last monitoring job to run.
-
MonitoringGroundTruthS3Input
-
The ground truth labels for the dataset used for the monitoring job.
-
MonitoringInput
-
The inputs for a monitoring job.
-
MonitoringJobDefinition
-
Defines the monitoring job.
-
MonitoringJobDefinitionSummary
-
Summary information about a monitoring job.
-
MonitoringNetworkConfig
-
The networking configuration for the monitoring job.
-
MonitoringOutput
-
The output object for a monitoring job.
-
MonitoringOutputConfig
-
The output configuration for monitoring jobs.
-
MonitoringResources
-
Identifies the resources to deploy for a monitoring job.
-
MonitoringS3Output
-
Information about where and how you want to store the results of a
monitoring job.
-
MonitoringSchedule
-
A schedule for a model monitoring job. For information about model monitor,
see Amazon
SageMaker Model Monitor.
-
MonitoringScheduleConfig
-
Configures the monitoring schedule and defines the monitoring job.
-
MonitoringScheduleSummary
-
Summarizes the monitoring schedule.
-
MonitoringStatisticsResource
-
The statistics resource for a monitoring job.
-
MonitoringStoppingCondition
-
A time limit for how long the monitoring job is allowed to run before
stopping.
-
NestedFilters
-
A list of nested Filter objects. A resource must satisfy the
conditions of all filters to be included in the results returned from the
Search API.
-
NetworkConfig
-
Networking options for a job, such as network traffic encryption between
containers, whether to allow inbound and outbound network calls to and from
containers, and the VPC subnets and security groups to use for VPC-enabled
jobs.
-
NotebookInstanceLifecycleConfigSummary
-
Provides a summary of a notebook instance lifecycle configuration.
-
NotebookInstanceLifecycleHook
-
Contains the notebook instance lifecycle configuration script.
-
NotebookInstanceSummary
-
Provides summary information for an Amazon SageMaker notebook instance.
-
NotificationConfiguration
-
Configures SNS notifications of available or expiring work items for work
teams.
-
ObjectiveStatusCounters
-
Specifies the number of training jobs that this hyperparameter tuning job
launched, categorized by the status of their objective metric. The objective
metric status shows whether the final objective metric for the training job
has been evaluated by the tuning job and used in the hyperparameter tuning
process.
-
OfflineStoreConfig
-
The configuration of an
OfflineStore
.
-
OfflineStoreStatus
-
The status of
OfflineStore
.
-
OidcConfig
-
Use this parameter to configure your OIDC Identity Provider (IdP).
-
OidcConfigForResponse
-
Your OIDC IdP workforce configuration.
-
OidcMemberDefinition
-
A list of user groups that exist in your OIDC Identity Provider (IdP). One
to ten groups can be used to create a single private work team. When you add
a user group to the list of
Groups
, you can add that user group
to one or more private work teams. If you add a user group to a private work
team, all workers in that user group are added to the work team.
-
OnlineStoreConfig
-
Use this to specify the AWS Key Management Service (KMS) Key ID, or
KMSKeyId
, for at rest data encryption. You can turn
OnlineStore
on or off by specifying the
EnableOnlineStore
flag at General Assembly; the default value
is False
.
-
OnlineStoreSecurityConfig
-
The security configuration for
OnlineStore
.
-
OutputConfig
-
Contains information about the output location for the compiled model and
the target device that the model runs on.
TargetDevice
and
TargetPlatform
are mutually exclusive, so you need to choose
one between the two to specify your target device or platform. If you cannot
find your device you want to use from the TargetDevice
list,
use TargetPlatform
to describe the platform of your edge device
and CompilerOptions
if there are specific settings that are
required or recommended to use for particular TargetPlatform.
-
OutputDataConfig
-
Provides information about how to store model training results (model
artifacts).
-
Parameter
-
Assigns a value to a named Pipeline parameter.
-
ParameterRange
-
Defines the possible values for categorical, continuous, and integer
hyperparameters to be used by an algorithm.
-
ParameterRanges
-
Specifies ranges of integer, continuous, and categorical hyperparameters
that a hyperparameter tuning job searches. The hyperparameter tuning job
launches training jobs with hyperparameter values within these ranges to
find the combination of values that result in the training job with the best
performance as measured by the objective metric of the hyperparameter tuning
job.
-
Parent
-
The trial that a trial component is associated with and the experiment the
trial is part of. A component might not be associated with a trial. A
component can be associated with multiple trials.
-
ParentHyperParameterTuningJob
-
A previously completed or stopped hyperparameter tuning job to be used as a
starting point for a new hyperparameter tuning job.
-
Pipeline
-
A SageMaker Model Building Pipeline instance.
-
PipelineExecution
-
An execution of a pipeline.
-
PipelineExecutionStep
-
An execution of a step in a pipeline.
-
PipelineExecutionStepMetadata
-
Metadata for a step execution.
-
PipelineExecutionSummary
-
A pipeline execution summary.
-
PipelineSummary
-
A summary of a pipeline.
-
ProcessingClusterConfig
-
Configuration for the cluster used to run a processing job.
-
ProcessingFeatureStoreOutput
-
Configuration for processing job outputs in Amazon SageMaker Feature Store.
-
ProcessingInput
-
The inputs for a processing job. The processing input must specify exactly
one of either
S3Input
or DatasetDefinition
types.
-
ProcessingJob
-
An Amazon SageMaker processing job that is used to analyze data and evaluate
models. For more information, see Process
Data and Evaluate Models.
-
ProcessingJobStepMetadata
-
Metadata for a processing job step.
-
ProcessingJobSummary
-
Summary of information about a processing job.
-
ProcessingOutput
-
Describes the results of a processing job. The processing output must
specify exactly one of either
S3Output
or
FeatureStoreOutput
types.
-
ProcessingOutputConfig
-
The output configuration for the processing job.
-
ProcessingResources
-
Identifies the resources, ML compute instances, and ML storage volumes to
deploy for a processing job. In distributed training, you specify more than
one instance.
-
ProcessingS3Input
-
Configuration for processing job inputs in Amazon S3.
-
ProcessingS3Output
-
Configuration for processing job outputs in Amazon S3.
-
ProcessingStoppingCondition
-
Specifies a time limit for how long the processing job is allowed to run.
-
ProductionVariant
-
Identifies a model that you want to host and the resources to deploy for
hosting it. If you are deploying multiple models, tell Amazon SageMaker how
to distribute traffic among the models by specifying variant weights.
-
ProductionVariantSummary
-
Describes weight and capacities for a production variant associated with an
endpoint. If you sent a request to the
UpdateEndpointWeightsAndCapacities
API and the endpoint status
is Updating
, you get different desired and current values.
-
ProfilerConfig
-
Configuration information for Debugger system monitoring, framework
profiling, and storage paths.
-
ProfilerConfigForUpdate
-
Configuration information for updating the Debugger profile parameters,
system and framework metrics configurations, and storage paths.
-
ProfilerRuleConfiguration
-
Configuration information for profiling rules.
-
ProfilerRuleEvaluationStatus
-
Information about the status of the rule evaluation.
-
ProjectSummary
-
Information about a project.
-
PropertyNameQuery
-
Part of the
SuggestionQuery
type. Specifies a hint for
retrieving property names that begin with the specified text.
-
PropertyNameSuggestion
-
A property name returned from a
GetSearchSuggestions
call that
specifies a value in the PropertyNameQuery
field.
-
ProvisioningParameter
-
A key value pair used when you provision a project as a service catalog
product. For information, see What
is AWS Service Catalog.
-
PublicWorkforceTaskPrice
-
Defines the amount of money paid to an Amazon Mechanical Turk worker for
each task performed.
-
PutModelPackageGroupPolicyOutput
-
-
RedshiftDatasetDefinition
-
Configuration for Redshift Dataset Definition input.
-
RegisterModelStepMetadata
-
Metadata for a register model job step.
-
RenderableTask
-
Contains input values for a task.
-
RenderingError
-
A description of an error that occurred while rendering the template.
-
RenderUiTemplateResponse
-
-
ResolvedAttributes
-
The resolved attributes.
-
ResourceConfig
-
Describes the resources, including ML compute instances and ML storage
volumes, to use for model training.
-
ResourceLimits
-
Specifies the maximum number of training jobs and parallel training jobs
that a hyperparameter tuning job can launch.
-
ResourceSpec
-
Specifies the ARN's of a SageMaker image and SageMaker image version, and
the instance type that the version runs on.
-
RetentionPolicy
-
The retention policy for data stored on an Amazon Elastic File System (EFS)
volume.
-
S3DataSource
-
Describes the S3 data source.
-
S3StorageConfig
-
The Amazon Simple Storage (Amazon S3) location and and security
configuration for
OfflineStore
.
-
SageMaker
-
Provides APIs for creating and managing Amazon SageMaker resources.
-
ScheduleConfig
-
Configuration details about the monitoring schedule.
-
SearchExpression
-
A multi-expression that searches for the specified resource or resources in
a search. All resource objects that satisfy the expression's condition are
included in the search results. You must specify at least one subexpression,
filter, or nested filter. A
SearchExpression
can contain up to
twenty elements.
-
SearchRecord
-
A single resource returned as part of the Search API response.
-
SearchResponse
-
-
SecondaryStatusTransition
-
An array element of
DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides
additional details about a status that the training job has transitioned
through. A training job can be in one of several states, for example,
starting, downloading, training, or uploading. Within each state, there are
a number of intermediate states. For example, within the starting state,
Amazon SageMaker could be starting the training job or launching the ML
instances. These transitional states are referred to as the job's secondary
status.
-
ServiceCatalogProvisionedProductDetails
-
Details of a provisioned service catalog product. For information about
service catalog, see What
is AWS Service Catalog.
-
ServiceCatalogProvisioningDetails
-
Details that you specify to provision a service catalog product. For
information about service catalog, see .What
is AWS Service Catalog.
-
SharingSettings
-
Specifies options when sharing an Amazon SageMaker Studio notebook. These
settings are specified as part of
DefaultUserSettings
when the
CreateDomain API is called, and as part of UserSettings
when the CreateUserProfile API is called.
-
ShuffleConfig
-
A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
for S3DataType
, the results of the S3 key
prefix matches are shuffled. If you use ManifestFile
, the order
of the S3 object references in the ManifestFile
is shuffled. If
you use AugmentedManifestFile
, the order of the JSON lines in
the AugmentedManifestFile
is shuffled. The shuffling order is
determined using the Seed
value.
-
SourceAlgorithm
-
Specifies an algorithm that was used to create the model package. The
algorithm must be either an algorithm resource in your Amazon SageMaker
account or an algorithm in AWS Marketplace that you are subscribed to.
-
SourceAlgorithmSpecification
-
A list of algorithms that were used to create a model package.
-
SourceIpConfig
-
A list of IP address ranges (CIDRs).
Used to create an allow list of IP addresses for a private workforce.
Workers will only be able to login to their worker portal from an IP address
within this range. By default, a workforce isn't restricted to specific IP
addresses.
-
StartPipelineExecutionResponse
-
-
StoppingCondition
-
Specifies a limit to how long a model training or compilation job can run.
It also specifies how long you are willing to wait for a managed spot
training job to complete. When the job reaches the time limit, Amazon
SageMaker ends the training or compilation job. Use this API to cap model
training costs.
-
StopPipelineExecutionResponse
-
-
SubscribedWorkteam
-
Describes a work team of a vendor that does the a labelling job.
-
SuggestionQuery
-
Specified in the GetSearchSuggestions request. Limits the property
names that are included in the response.
-
Tag
-
Describes a tag.
-
TargetPlatform
-
Contains information about a target platform that you want your model to run
on, such as OS, architecture, and accelerators. It is an alternative of
TargetDevice
.
-
TensorBoardAppSettings
-
The TensorBoard app settings.
-
TensorBoardOutputConfig
-
Configuration of storage locations for the Debugger TensorBoard output data.
-
TrafficRoutingConfig
-
Currently, the
TrafficRoutingConfig
API is not supported.
-
TrainingJob
-
Contains information about a training job.
-
TrainingJobDefinition
-
Defines the input needed to run a training job using the algorithm.
-
TrainingJobStatusCounters
-
The numbers of training jobs launched by a hyperparameter tuning job,
categorized by status.
-
TrainingJobStepMetadata
-
Metadata for a training job step.
-
TrainingJobSummary
-
Provides summary information about a training job.
-
TrainingSpecification
-
Defines how the algorithm is used for a training job.
-
TransformDataSource
-
Describes the location of the channel data.
-
TransformInput
-
Describes the input source of a transform job and the way the transform job
consumes it.
-
TransformJob
-
A batch transform job. For information about SageMaker batch transform, see
Use
Batch Transform.
-
TransformJobDefinition
-
Defines the input needed to run a transform job using the inference
specification specified in the algorithm.
-
TransformJobStepMetadata
-
Metadata for a transform job step.
-
TransformJobSummary
-
Provides a summary of a transform job. Multiple
TransformJobSummary
objects are returned as a list after in
response to a ListTransformJobs call.
-
TransformOutput
-
Describes the results of a transform job.
-
TransformResources
-
Describes the resources, including ML instance types and ML instance count,
to use for transform job.
-
TransformS3DataSource
-
Describes the S3 data source.
-
Trial
-
The properties of a trial as returned by the Search API.
-
TrialComponent
-
The properties of a trial component as returned by the Search API.
-
TrialComponentArtifact
-
Represents an input or output artifact of a trial component. You specify
TrialComponentArtifact
as part of the
InputArtifacts
and OutputArtifacts
parameters in
the CreateTrialComponent request.
-
TrialComponentMetricSummary
-
A summary of the metrics of a trial component.
-
TrialComponentParameterValue
-
The value of a hyperparameter. Only one of
NumberValue
or
StringValue
can be specified.
-
TrialComponentSimpleSummary
-
A short summary of a trial component.
-
TrialComponentSource
-
The Amazon Resource Name (ARN) and job type of the source of a trial
component.
-
TrialComponentSourceDetail
-
Detailed information about the source of a trial component. Either
ProcessingJob
or TrainingJob
is returned.
-
TrialComponentStatus
-
The status of the trial component.
-
TrialComponentSummary
-
A summary of the properties of a trial component. To get all the properties,
call the DescribeTrialComponent API and provide the
TrialComponentName
.
-
TrialSource
-
The source of the trial.
-
TrialSummary
-
A summary of the properties of a trial. To get the complete set of
properties, call the DescribeTrial API and provide the
TrialName
.
-
TuningJobCompletionCriteria
-
The job completion criteria.
-
UiConfig
-
Provided configuration information for the worker UI for a labeling job.
-
UiTemplate
-
The Liquid template for the worker user interface.
-
UiTemplateInfo
-
Container for user interface template information.
-
UpdateActionResponse
-
-
UpdateAppImageConfigResponse
-
-
UpdateArtifactResponse
-
-
UpdateCodeRepositoryOutput
-
-
UpdateContextResponse
-
-
UpdateDomainResponse
-
-
UpdateEndpointOutput
-
-
UpdateEndpointWeightsAndCapacitiesOutput
-
-
UpdateExperimentResponse
-
-
UpdateImageResponse
-
-
UpdateModelPackageOutput
-
-
UpdateMonitoringScheduleResponse
-
-
UpdateNotebookInstanceLifecycleConfigOutput
-
-
UpdateNotebookInstanceOutput
-
-
UpdatePipelineExecutionResponse
-
-
UpdatePipelineResponse
-
-
UpdateTrainingJobResponse
-
-
UpdateTrialComponentResponse
-
-
UpdateTrialResponse
-
-
UpdateUserProfileResponse
-
-
UpdateWorkforceResponse
-
-
UpdateWorkteamResponse
-
-
USD
-
Represents an amount of money in United States dollars/
-
UserContext
-
Information about the user who created or modified an experiment, trial, or
trial component.
-
UserProfileDetails
-
The user profile details.
-
UserSettings
-
A collection of settings.
-
VariantProperty
-
Specifies a production variant property type for an Endpoint.
-
VpcConfig
-
Specifies a VPC that your training jobs and hosted models have access to.
Control access to and from your training and model containers by configuring
the VPC. For more information, see Protect
Endpoints by Using an Amazon Virtual Private Cloud and Protect
Training Jobs by Using an Amazon Virtual Private Cloud.
-
Workforce
-
A single private workforce, which is automatically created when you create
your first private work team. You can create one private work force in each
AWS Region. By default, any workforce-related API operation used in a
specific region will apply to the workforce created in that region. To learn
how to create a private workforce, see Create
a Private Workforce.
-
Workteam
-
Provides details about a labeling work team.