| Copyright | (c) 2013-2018 Brendan Hay |
|---|---|
| License | Mozilla Public License, v. 2.0. |
| Maintainer | Brendan Hay <brendan.g.hay+amazonka@gmail.com> |
| Stability | auto-generated |
| Portability | non-portable (GHC extensions) |
| Safe Haskell | None |
| Language | Haskell2010 |
Network.AWS.SageMaker.Types
Contents
- Service Configuration
- Errors
- CompressionType
- DirectInternetAccess
- EndpointConfigSortKey
- EndpointSortKey
- EndpointStatus
- InstanceType
- ModelSortKey
- NotebookInstanceLifecycleConfigSortKey
- NotebookInstanceLifecycleConfigSortOrder
- NotebookInstanceSortKey
- NotebookInstanceSortOrder
- NotebookInstanceStatus
- OrderKey
- ProductionVariantInstanceType
- RecordWrapper
- S3DataDistribution
- S3DataType
- SecondaryStatus
- SortBy
- SortOrder
- TrainingInputMode
- TrainingInstanceType
- TrainingJobStatus
- AlgorithmSpecification
- Channel
- ContainerDefinition
- DataSource
- DesiredWeightAndCapacity
- EndpointConfigSummary
- EndpointSummary
- ModelArtifacts
- ModelSummary
- NotebookInstanceLifecycleConfigSummary
- NotebookInstanceLifecycleHook
- NotebookInstanceSummary
- OutputDataConfig
- ProductionVariant
- ProductionVariantSummary
- ResourceConfig
- S3DataSource
- StoppingCondition
- Tag
- TrainingJobSummary
- VPCConfig
Description
- sageMaker :: Service
- _ResourceLimitExceeded :: AsError a => Getting (First ServiceError) a ServiceError
- _ResourceInUse :: AsError a => Getting (First ServiceError) a ServiceError
- _ResourceNotFound :: AsError a => Getting (First ServiceError) a ServiceError
- data CompressionType
- data DirectInternetAccess
- data EndpointConfigSortKey
- = CreationTime
- | Name
- data EndpointSortKey
- data EndpointStatus
- data InstanceType
- data ModelSortKey
- data NotebookInstanceLifecycleConfigSortKey
- data NotebookInstanceLifecycleConfigSortOrder
- data NotebookInstanceSortKey
- data NotebookInstanceSortOrder
- data NotebookInstanceStatus
- data OrderKey
- data ProductionVariantInstanceType
- = PVITMl_C4_2XLarge
- | PVITMl_C4_4XLarge
- | PVITMl_C4_8XLarge
- | PVITMl_C4_Large
- | PVITMl_C4_XLarge
- | PVITMl_C5_18XLarge
- | PVITMl_C5_2XLarge
- | PVITMl_C5_4XLarge
- | PVITMl_C5_9XLarge
- | PVITMl_C5_Large
- | PVITMl_C5_XLarge
- | PVITMl_M4_10XLarge
- | PVITMl_M4_16XLarge
- | PVITMl_M4_2XLarge
- | PVITMl_M4_4XLarge
- | PVITMl_M4_XLarge
- | PVITMl_M5_12XLarge
- | PVITMl_M5_24XLarge
- | PVITMl_M5_2XLarge
- | PVITMl_M5_4XLarge
- | PVITMl_M5_Large
- | PVITMl_M5_XLarge
- | PVITMl_P2_16XLarge
- | PVITMl_P2_8XLarge
- | PVITMl_P2_XLarge
- | PVITMl_P3_16XLarge
- | PVITMl_P3_2XLarge
- | PVITMl_P3_8XLarge
- | PVITMl_T2_2XLarge
- | PVITMl_T2_Large
- | PVITMl_T2_Medium
- | PVITMl_T2_XLarge
- data RecordWrapper
- data S3DataDistribution
- data S3DataType
- data SecondaryStatus
- data SortBy
- data SortOrder
- data TrainingInputMode
- data TrainingInstanceType
- = TITMl_C4_2XLarge
- | TITMl_C4_4XLarge
- | TITMl_C4_8XLarge
- | TITMl_C4_XLarge
- | TITMl_C5_18XLarge
- | TITMl_C5_2XLarge
- | TITMl_C5_4XLarge
- | TITMl_C5_9XLarge
- | TITMl_C5_XLarge
- | TITMl_M4_10XLarge
- | TITMl_M4_16XLarge
- | TITMl_M4_2XLarge
- | TITMl_M4_4XLarge
- | TITMl_M4_XLarge
- | TITMl_M5_12XLarge
- | TITMl_M5_24XLarge
- | TITMl_M5_2XLarge
- | TITMl_M5_4XLarge
- | TITMl_M5_Large
- | TITMl_M5_XLarge
- | TITMl_P2_16XLarge
- | TITMl_P2_8XLarge
- | TITMl_P2_XLarge
- | TITMl_P3_16XLarge
- | TITMl_P3_2XLarge
- | TITMl_P3_8XLarge
- data TrainingJobStatus
- data AlgorithmSpecification
- algorithmSpecification :: Text -> TrainingInputMode -> AlgorithmSpecification
- asTrainingImage :: Lens' AlgorithmSpecification Text
- asTrainingInputMode :: Lens' AlgorithmSpecification TrainingInputMode
- data Channel
- channel :: Text -> DataSource -> Channel
- cRecordWrapperType :: Lens' Channel (Maybe RecordWrapper)
- cCompressionType :: Lens' Channel (Maybe CompressionType)
- cContentType :: Lens' Channel (Maybe Text)
- cChannelName :: Lens' Channel Text
- cDataSource :: Lens' Channel DataSource
- data ContainerDefinition
- containerDefinition :: Text -> ContainerDefinition
- cdModelDataURL :: Lens' ContainerDefinition (Maybe Text)
- cdEnvironment :: Lens' ContainerDefinition (HashMap Text Text)
- cdContainerHostname :: Lens' ContainerDefinition (Maybe Text)
- cdImage :: Lens' ContainerDefinition Text
- data DataSource
- dataSource :: S3DataSource -> DataSource
- dsS3DataSource :: Lens' DataSource S3DataSource
- data DesiredWeightAndCapacity
- desiredWeightAndCapacity :: Text -> DesiredWeightAndCapacity
- dwacDesiredInstanceCount :: Lens' DesiredWeightAndCapacity (Maybe Natural)
- dwacDesiredWeight :: Lens' DesiredWeightAndCapacity (Maybe Double)
- dwacVariantName :: Lens' DesiredWeightAndCapacity Text
- data EndpointConfigSummary
- endpointConfigSummary :: Text -> Text -> UTCTime -> EndpointConfigSummary
- ecsEndpointConfigName :: Lens' EndpointConfigSummary Text
- ecsEndpointConfigARN :: Lens' EndpointConfigSummary Text
- ecsCreationTime :: Lens' EndpointConfigSummary UTCTime
- data EndpointSummary
- endpointSummary :: Text -> Text -> UTCTime -> UTCTime -> EndpointStatus -> EndpointSummary
- esEndpointName :: Lens' EndpointSummary Text
- esEndpointARN :: Lens' EndpointSummary Text
- esCreationTime :: Lens' EndpointSummary UTCTime
- esLastModifiedTime :: Lens' EndpointSummary UTCTime
- esEndpointStatus :: Lens' EndpointSummary EndpointStatus
- data ModelArtifacts
- modelArtifacts :: Text -> ModelArtifacts
- maS3ModelArtifacts :: Lens' ModelArtifacts Text
- data ModelSummary
- modelSummary :: Text -> Text -> UTCTime -> ModelSummary
- msModelName :: Lens' ModelSummary Text
- msModelARN :: Lens' ModelSummary Text
- msCreationTime :: Lens' ModelSummary UTCTime
- data NotebookInstanceLifecycleConfigSummary
- notebookInstanceLifecycleConfigSummary :: Text -> Text -> NotebookInstanceLifecycleConfigSummary
- nilcsCreationTime :: Lens' NotebookInstanceLifecycleConfigSummary (Maybe UTCTime)
- nilcsLastModifiedTime :: Lens' NotebookInstanceLifecycleConfigSummary (Maybe UTCTime)
- nilcsNotebookInstanceLifecycleConfigName :: Lens' NotebookInstanceLifecycleConfigSummary Text
- nilcsNotebookInstanceLifecycleConfigARN :: Lens' NotebookInstanceLifecycleConfigSummary Text
- data NotebookInstanceLifecycleHook
- notebookInstanceLifecycleHook :: NotebookInstanceLifecycleHook
- nilhContent :: Lens' NotebookInstanceLifecycleHook (Maybe Text)
- data NotebookInstanceSummary
- notebookInstanceSummary :: Text -> Text -> NotebookInstanceSummary
- nisCreationTime :: Lens' NotebookInstanceSummary (Maybe UTCTime)
- nisURL :: Lens' NotebookInstanceSummary (Maybe Text)
- nisLastModifiedTime :: Lens' NotebookInstanceSummary (Maybe UTCTime)
- nisInstanceType :: Lens' NotebookInstanceSummary (Maybe InstanceType)
- nisNotebookInstanceStatus :: Lens' NotebookInstanceSummary (Maybe NotebookInstanceStatus)
- nisNotebookInstanceLifecycleConfigName :: Lens' NotebookInstanceSummary (Maybe Text)
- nisNotebookInstanceName :: Lens' NotebookInstanceSummary Text
- nisNotebookInstanceARN :: Lens' NotebookInstanceSummary Text
- data OutputDataConfig
- outputDataConfig :: Text -> OutputDataConfig
- odcKMSKeyId :: Lens' OutputDataConfig (Maybe Text)
- odcS3OutputPath :: Lens' OutputDataConfig Text
- data ProductionVariant
- productionVariant :: Text -> Text -> Natural -> ProductionVariantInstanceType -> ProductionVariant
- pvInitialVariantWeight :: Lens' ProductionVariant (Maybe Double)
- pvVariantName :: Lens' ProductionVariant Text
- pvModelName :: Lens' ProductionVariant Text
- pvInitialInstanceCount :: Lens' ProductionVariant Natural
- pvInstanceType :: Lens' ProductionVariant ProductionVariantInstanceType
- data ProductionVariantSummary
- productionVariantSummary :: Text -> ProductionVariantSummary
- pvsDesiredInstanceCount :: Lens' ProductionVariantSummary (Maybe Natural)
- pvsDesiredWeight :: Lens' ProductionVariantSummary (Maybe Double)
- pvsCurrentWeight :: Lens' ProductionVariantSummary (Maybe Double)
- pvsCurrentInstanceCount :: Lens' ProductionVariantSummary (Maybe Natural)
- pvsVariantName :: Lens' ProductionVariantSummary Text
- data ResourceConfig
- resourceConfig :: TrainingInstanceType -> Natural -> Natural -> ResourceConfig
- rcVolumeKMSKeyId :: Lens' ResourceConfig (Maybe Text)
- rcInstanceType :: Lens' ResourceConfig TrainingInstanceType
- rcInstanceCount :: Lens' ResourceConfig Natural
- rcVolumeSizeInGB :: Lens' ResourceConfig Natural
- data S3DataSource
- s3DataSource :: S3DataType -> Text -> S3DataSource
- sdsS3DataDistributionType :: Lens' S3DataSource (Maybe S3DataDistribution)
- sdsS3DataType :: Lens' S3DataSource S3DataType
- sdsS3URI :: Lens' S3DataSource Text
- data StoppingCondition
- stoppingCondition :: StoppingCondition
- scMaxRuntimeInSeconds :: Lens' StoppingCondition (Maybe Natural)
- data Tag
- tag :: Text -> Text -> Tag
- tagKey :: Lens' Tag Text
- tagValue :: Lens' Tag Text
- data TrainingJobSummary
- trainingJobSummary :: Text -> Text -> UTCTime -> TrainingJobStatus -> TrainingJobSummary
- tjsTrainingEndTime :: Lens' TrainingJobSummary (Maybe UTCTime)
- tjsLastModifiedTime :: Lens' TrainingJobSummary (Maybe UTCTime)
- tjsTrainingJobName :: Lens' TrainingJobSummary Text
- tjsTrainingJobARN :: Lens' TrainingJobSummary Text
- tjsCreationTime :: Lens' TrainingJobSummary UTCTime
- tjsTrainingJobStatus :: Lens' TrainingJobSummary TrainingJobStatus
- data VPCConfig
- vpcConfig :: NonEmpty Text -> NonEmpty Text -> VPCConfig
- vcSecurityGroupIds :: Lens' VPCConfig (NonEmpty Text)
- vcSubnets :: Lens' VPCConfig (NonEmpty Text)
Service Configuration
API version 2017-07-24 of the Amazon SageMaker Service SDK configuration.
Errors
_ResourceLimitExceeded :: AsError a => Getting (First ServiceError) a ServiceError Source #
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
_ResourceInUse :: AsError a => Getting (First ServiceError) a ServiceError Source #
Resource being accessed is in use.
_ResourceNotFound :: AsError a => Getting (First ServiceError) a ServiceError Source #
Resource being access is not found.
CompressionType
data CompressionType Source #
Instances
DirectInternetAccess
data DirectInternetAccess Source #
Instances
EndpointConfigSortKey
data EndpointConfigSortKey Source #
Constructors
| CreationTime | |
| Name |
Instances
EndpointSortKey
data EndpointSortKey Source #
Constructors
| ESKCreationTime | |
| ESKName | |
| ESKStatus |
Instances
EndpointStatus
data EndpointStatus Source #
Constructors
| Creating | |
| Deleting | |
| Failed | |
| InService | |
| OutOfService | |
| RollingBack | |
| Updating |
Instances
InstanceType
data InstanceType Source #
Constructors
Instances
ModelSortKey
data ModelSortKey Source #
Constructors
| MSKCreationTime | |
| MSKName |
Instances
NotebookInstanceLifecycleConfigSortKey
data NotebookInstanceLifecycleConfigSortKey Source #
Constructors
| NILCSKCreationTime | |
| NILCSKLastModifiedTime | |
| NILCSKName |
Instances
NotebookInstanceLifecycleConfigSortOrder
data NotebookInstanceLifecycleConfigSortOrder Source #
Constructors
| NILCSOAscending | |
| NILCSODescending |
Instances
NotebookInstanceSortKey
data NotebookInstanceSortKey Source #
Constructors
| NISKCreationTime | |
| NISKName | |
| NISKStatus |
Instances
NotebookInstanceSortOrder
data NotebookInstanceSortOrder Source #
Constructors
| Ascending | |
| Descending |
Instances
NotebookInstanceStatus
data NotebookInstanceStatus Source #
Constructors
| NISDeleting | |
| NISFailed | |
| NISInService | |
| NISPending | |
| NISStopped | |
| NISStopping |
Instances
OrderKey
Constructors
| OKAscending | |
| OKDescending |
Instances
ProductionVariantInstanceType
data ProductionVariantInstanceType Source #
Constructors
Instances
RecordWrapper
data RecordWrapper Source #
Instances
S3DataDistribution
data S3DataDistribution Source #
Constructors
| FullyReplicated | |
| ShardedByS3Key |
Instances
S3DataType
data S3DataType Source #
Constructors
| ManifestFile | |
| S3Prefix |
Instances
SecondaryStatus
data SecondaryStatus Source #
Constructors
| SSCompleted | |
| SSDownloading | |
| SSFailed | |
| SSMaxRuntimeExceeded | |
| SSStarting | |
| SSStopped | |
| SSStopping | |
| SSTraining | |
| SSUploading |
Instances
SortBy
Constructors
| SBCreationTime | |
| SBName | |
| SBStatus |
Instances
SortOrder
Constructors
| SOAscending | |
| SODescending |
Instances
TrainingInputMode
data TrainingInputMode Source #
Instances
TrainingInstanceType
data TrainingInstanceType Source #
Constructors
Instances
TrainingJobStatus
data TrainingJobStatus Source #
Constructors
| TJSCompleted | |
| TJSFailed | |
| TJSInProgress | |
| TJSStopped | |
| TJSStopping |
Instances
AlgorithmSpecification
data AlgorithmSpecification Source #
Specifies the training algorithm to use in a CreateTrainingJob request.
For more information about algorithms provided by Amazon SageMaker, see Algorithms . For information about using your own algorithms, see 'your-algorithms' .
See: algorithmSpecification smart constructor.
Instances
algorithmSpecification Source #
Creates a value of AlgorithmSpecification with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
asTrainingImage- The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see 'sagemaker-algo-docker-registry-paths' .asTrainingInputMode- The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports theFileinput mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports thePipeinput mode, Amazon SageMaker streams data directly from S3 to the container. In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any. For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
asTrainingImage :: Lens' AlgorithmSpecification Text Source #
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see 'sagemaker-algo-docker-registry-paths' .
asTrainingInputMode :: Lens' AlgorithmSpecification TrainingInputMode Source #
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container. In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any. For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
Channel
A channel is a named input source that training algorithms can consume.
See: channel smart constructor.
Arguments
| :: Text | |
| -> DataSource | |
| -> Channel |
Creates a value of Channel with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
cRecordWrapperType- Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which caseAmazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO . In FILE mode, leave this field unset or set it to None.cCompressionType- If training data is compressed, the compression type. The default value isNone.CompressionTypeis used only in PIPE input mode. In FILE mode, leave this field unset or set it to None.cContentType- The MIME type of the data.cChannelName- The name of the channel.cDataSource- The location of the channel data.
cRecordWrapperType :: Lens' Channel (Maybe RecordWrapper) Source #
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which caseAmazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO . In FILE mode, leave this field unset or set it to None.
cCompressionType :: Lens' Channel (Maybe CompressionType) Source #
If training data is compressed, the compression type. The default value is None . CompressionType is used only in PIPE input mode. In FILE mode, leave this field unset or set it to None.
cDataSource :: Lens' Channel DataSource Source #
The location of the channel data.
ContainerDefinition
data ContainerDefinition Source #
Describes the container, as part of model definition.
See: containerDefinition smart constructor.
Instances
Arguments
| :: Text | |
| -> ContainerDefinition |
Creates a value of ContainerDefinition with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
cdModelDataURL- The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).cdEnvironment- The environment variables to set in the Docker container. Each key and value in theEnvironmentstring to string map can have length of up to 1024. We support up to 16 entries in the map.cdContainerHostname- The DNS host name for the container after Amazon SageMaker deploys it.cdImage- The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. For more information, see Using Your Own Algorithms with Amazon SageMaker
cdModelDataURL :: Lens' ContainerDefinition (Maybe Text) Source #
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
cdEnvironment :: Lens' ContainerDefinition (HashMap Text Text) Source #
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
cdContainerHostname :: Lens' ContainerDefinition (Maybe Text) Source #
The DNS host name for the container after Amazon SageMaker deploys it.
cdImage :: Lens' ContainerDefinition Text Source #
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. For more information, see Using Your Own Algorithms with Amazon SageMaker
DataSource
data DataSource Source #
Describes the location of the channel data.
See: dataSource smart constructor.
Arguments
| :: S3DataSource | |
| -> DataSource |
Creates a value of DataSource with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
dsS3DataSource- The S3 location of the data source that is associated with a channel.
dsS3DataSource :: Lens' DataSource S3DataSource Source #
The S3 location of the data source that is associated with a channel.
DesiredWeightAndCapacity
data DesiredWeightAndCapacity Source #
Specifies weight and capacity values for a production variant.
See: desiredWeightAndCapacity smart constructor.
Instances
desiredWeightAndCapacity Source #
Arguments
| :: Text | |
| -> DesiredWeightAndCapacity |
Creates a value of DesiredWeightAndCapacity with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
dwacDesiredInstanceCount- The variant's capacity.dwacDesiredWeight- The variant's weight.dwacVariantName- The name of the variant to update.
dwacDesiredInstanceCount :: Lens' DesiredWeightAndCapacity (Maybe Natural) Source #
The variant's capacity.
dwacDesiredWeight :: Lens' DesiredWeightAndCapacity (Maybe Double) Source #
The variant's weight.
dwacVariantName :: Lens' DesiredWeightAndCapacity Text Source #
The name of the variant to update.
EndpointConfigSummary
data EndpointConfigSummary Source #
Provides summary information for an endpoint configuration.
See: endpointConfigSummary smart constructor.
Instances
endpointConfigSummary Source #
Arguments
| :: Text | |
| -> Text | |
| -> UTCTime | |
| -> EndpointConfigSummary |
Creates a value of EndpointConfigSummary with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
ecsEndpointConfigName- The name of the endpoint configuration.ecsEndpointConfigARN- The Amazon Resource Name (ARN) of the endpoint configuration.ecsCreationTime- A timestamp that shows when the endpoint configuration was created.
ecsEndpointConfigName :: Lens' EndpointConfigSummary Text Source #
The name of the endpoint configuration.
ecsEndpointConfigARN :: Lens' EndpointConfigSummary Text Source #
The Amazon Resource Name (ARN) of the endpoint configuration.
ecsCreationTime :: Lens' EndpointConfigSummary UTCTime Source #
A timestamp that shows when the endpoint configuration was created.
EndpointSummary
data EndpointSummary Source #
Provides summary information for an endpoint.
See: endpointSummary smart constructor.
Arguments
| :: Text | |
| -> Text | |
| -> UTCTime | |
| -> UTCTime | |
| -> EndpointStatus | |
| -> EndpointSummary |
Creates a value of EndpointSummary with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
esEndpointName- The name of the endpoint.esEndpointARN- The Amazon Resource Name (ARN) of the endpoint.esCreationTime- A timestamp that shows when the endpoint was created.esLastModifiedTime- A timestamp that shows when the endpoint was last modified.esEndpointStatus- The status of the endpoint.
esEndpointName :: Lens' EndpointSummary Text Source #
The name of the endpoint.
esEndpointARN :: Lens' EndpointSummary Text Source #
The Amazon Resource Name (ARN) of the endpoint.
esCreationTime :: Lens' EndpointSummary UTCTime Source #
A timestamp that shows when the endpoint was created.
esLastModifiedTime :: Lens' EndpointSummary UTCTime Source #
A timestamp that shows when the endpoint was last modified.
esEndpointStatus :: Lens' EndpointSummary EndpointStatus Source #
The status of the endpoint.
ModelArtifacts
data ModelArtifacts Source #
Provides information about the location that is configured for storing model artifacts.
See: modelArtifacts smart constructor.
Arguments
| :: Text | |
| -> ModelArtifacts |
Creates a value of ModelArtifacts with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
maS3ModelArtifacts- The path of the S3 object that contains the model artifacts. For example,s3:/bucket-namekeynameprefix/model.tar.gz.
maS3ModelArtifacts :: Lens' ModelArtifacts Text Source #
The path of the S3 object that contains the model artifacts. For example, s3:/bucket-namekeynameprefix/model.tar.gz .
ModelSummary
data ModelSummary Source #
Provides summary information about a model.
See: modelSummary smart constructor.
Arguments
| :: Text | |
| -> Text | |
| -> UTCTime | |
| -> ModelSummary |
Creates a value of ModelSummary with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
msModelName- The name of the model that you want a summary for.msModelARN- The Amazon Resource Name (ARN) of the model.msCreationTime- A timestamp that indicates when the model was created.
msModelName :: Lens' ModelSummary Text Source #
The name of the model that you want a summary for.
msModelARN :: Lens' ModelSummary Text Source #
The Amazon Resource Name (ARN) of the model.
msCreationTime :: Lens' ModelSummary UTCTime Source #
A timestamp that indicates when the model was created.
NotebookInstanceLifecycleConfigSummary
data NotebookInstanceLifecycleConfigSummary Source #
Provides a summary of a notebook instance lifecycle configuration.
See: notebookInstanceLifecycleConfigSummary smart constructor.
Instances
notebookInstanceLifecycleConfigSummary Source #
Arguments
| :: Text | |
| -> Text | |
| -> NotebookInstanceLifecycleConfigSummary |
Creates a value of NotebookInstanceLifecycleConfigSummary with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
nilcsCreationTime- A timestamp that tells when the lifecycle configuration was created.nilcsLastModifiedTime- A timestamp that tells when the lifecycle configuration was last modified.nilcsNotebookInstanceLifecycleConfigName- The name of the lifecycle configuration.nilcsNotebookInstanceLifecycleConfigARN- The Amazon Resource Name (ARN) of the lifecycle configuration.
nilcsCreationTime :: Lens' NotebookInstanceLifecycleConfigSummary (Maybe UTCTime) Source #
A timestamp that tells when the lifecycle configuration was created.
nilcsLastModifiedTime :: Lens' NotebookInstanceLifecycleConfigSummary (Maybe UTCTime) Source #
A timestamp that tells when the lifecycle configuration was last modified.
nilcsNotebookInstanceLifecycleConfigName :: Lens' NotebookInstanceLifecycleConfigSummary Text Source #
The name of the lifecycle configuration.
nilcsNotebookInstanceLifecycleConfigARN :: Lens' NotebookInstanceLifecycleConfigSummary Text Source #
The Amazon Resource Name (ARN) of the lifecycle configuration.
NotebookInstanceLifecycleHook
data NotebookInstanceLifecycleHook Source #
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the > PATH environment variable that is available to both scripts is sbin:bin:usrsbin:usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group awssagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see 'notebook-lifecycle-config' .
See: notebookInstanceLifecycleHook smart constructor.
Instances
notebookInstanceLifecycleHook :: NotebookInstanceLifecycleHook Source #
Creates a value of NotebookInstanceLifecycleHook with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
nilhContent- A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
nilhContent :: Lens' NotebookInstanceLifecycleHook (Maybe Text) Source #
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
NotebookInstanceSummary
data NotebookInstanceSummary Source #
Provides summary information for an Amazon SageMaker notebook instance.
See: notebookInstanceSummary smart constructor.
Instances
notebookInstanceSummary Source #
Creates a value of NotebookInstanceSummary with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
nisCreationTime- A timestamp that shows when the notebook instance was created.nisURL- The URL that you use to connect to the Jupyter instance running in your notebook instance.nisLastModifiedTime- A timestamp that shows when the notebook instance was last modified.nisInstanceType- The type of ML compute instance that the notebook instance is running on.nisNotebookInstanceStatus- The status of the notebook instance.nisNotebookInstanceLifecycleConfigName- The name of a notebook instance lifecycle configuration associated with this notebook instance. For information about notebook instance lifestyle configurations, see 'notebook-lifecycle-config' .nisNotebookInstanceName- The name of the notebook instance that you want a summary for.nisNotebookInstanceARN- The Amazon Resource Name (ARN) of the notebook instance.
nisCreationTime :: Lens' NotebookInstanceSummary (Maybe UTCTime) Source #
A timestamp that shows when the notebook instance was created.
nisURL :: Lens' NotebookInstanceSummary (Maybe Text) Source #
The URL that you use to connect to the Jupyter instance running in your notebook instance.
nisLastModifiedTime :: Lens' NotebookInstanceSummary (Maybe UTCTime) Source #
A timestamp that shows when the notebook instance was last modified.
nisInstanceType :: Lens' NotebookInstanceSummary (Maybe InstanceType) Source #
The type of ML compute instance that the notebook instance is running on.
nisNotebookInstanceStatus :: Lens' NotebookInstanceSummary (Maybe NotebookInstanceStatus) Source #
The status of the notebook instance.
nisNotebookInstanceLifecycleConfigName :: Lens' NotebookInstanceSummary (Maybe Text) Source #
The name of a notebook instance lifecycle configuration associated with this notebook instance. For information about notebook instance lifestyle configurations, see 'notebook-lifecycle-config' .
nisNotebookInstanceName :: Lens' NotebookInstanceSummary Text Source #
The name of the notebook instance that you want a summary for.
nisNotebookInstanceARN :: Lens' NotebookInstanceSummary Text Source #
The Amazon Resource Name (ARN) of the notebook instance.
OutputDataConfig
data OutputDataConfig Source #
Provides information about how to store model training results (model artifacts).
See: outputDataConfig smart constructor.
Instances
Arguments
| :: Text | |
| -> OutputDataConfig |
Creates a value of OutputDataConfig with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
odcKMSKeyId- The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.odcS3OutputPath- Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example,s3:/bucket-namekey-name-prefix.
odcKMSKeyId :: Lens' OutputDataConfig (Maybe Text) Source #
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
odcS3OutputPath :: Lens' OutputDataConfig Text Source #
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3:/bucket-namekey-name-prefix .
ProductionVariant
data ProductionVariant Source #
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.
See: productionVariant smart constructor.
Instances
Arguments
| :: Text | |
| -> Text | |
| -> Natural | |
| -> ProductionVariantInstanceType | |
| -> ProductionVariant |
Creates a value of ProductionVariant with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
pvInitialVariantWeight- Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of theVariantWeightto the sum of allVariantWeightvalues across all ProductionVariants. If unspecified, it defaults to 1.0.pvVariantName- The name of the production variant.pvModelName- The name of the model that you want to host. This is the name that you specified when creating the model.pvInitialInstanceCount- Number of instances to launch initially.pvInstanceType- The ML compute instance type.
pvInitialVariantWeight :: Lens' ProductionVariant (Maybe Double) Source #
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
pvVariantName :: Lens' ProductionVariant Text Source #
The name of the production variant.
pvModelName :: Lens' ProductionVariant Text Source #
The name of the model that you want to host. This is the name that you specified when creating the model.
pvInitialInstanceCount :: Lens' ProductionVariant Natural Source #
Number of instances to launch initially.
pvInstanceType :: Lens' ProductionVariant ProductionVariantInstanceType Source #
The ML compute instance type.
ProductionVariantSummary
data ProductionVariantSummary Source #
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.
See: productionVariantSummary smart constructor.
Instances
productionVariantSummary Source #
Arguments
| :: Text | |
| -> ProductionVariantSummary |
Creates a value of ProductionVariantSummary with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
pvsDesiredInstanceCount- The number of instances requested in theUpdateEndpointWeightsAndCapacitiesrequest.pvsDesiredWeight- The requested weight, as specified in theUpdateEndpointWeightsAndCapacitiesrequest.pvsCurrentWeight- The weight associated with the variant.pvsCurrentInstanceCount- The number of instances associated with the variant.pvsVariantName- The name of the variant.
pvsDesiredInstanceCount :: Lens' ProductionVariantSummary (Maybe Natural) Source #
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
pvsDesiredWeight :: Lens' ProductionVariantSummary (Maybe Double) Source #
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
pvsCurrentWeight :: Lens' ProductionVariantSummary (Maybe Double) Source #
The weight associated with the variant.
pvsCurrentInstanceCount :: Lens' ProductionVariantSummary (Maybe Natural) Source #
The number of instances associated with the variant.
pvsVariantName :: Lens' ProductionVariantSummary Text Source #
The name of the variant.
ResourceConfig
data ResourceConfig Source #
Describes the resources, including ML compute instances and ML storage volumes, to use for model training.
See: resourceConfig smart constructor.
Instances
Arguments
| :: TrainingInstanceType | |
| -> Natural | |
| -> Natural | |
| -> ResourceConfig |
Creates a value of ResourceConfig with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
rcVolumeKMSKeyId- The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.rcInstanceType- The ML compute instance type.rcInstanceCount- The number of ML compute instances to use. For distributed training, provide a value greater than 1.rcVolumeSizeInGB- The size of the ML storage volume that you want to provision. ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, chooseFileas theTrainingInputModein the algorithm specification. You must specify sufficient ML storage for your scenario.
rcVolumeKMSKeyId :: Lens' ResourceConfig (Maybe Text) Source #
The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
rcInstanceType :: Lens' ResourceConfig TrainingInstanceType Source #
The ML compute instance type.
rcInstanceCount :: Lens' ResourceConfig Natural Source #
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
rcVolumeSizeInGB :: Lens' ResourceConfig Natural Source #
The size of the ML storage volume that you want to provision. ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification. You must specify sufficient ML storage for your scenario.
S3DataSource
data S3DataSource Source #
Describes the S3 data source.
See: s3DataSource smart constructor.
Arguments
| :: S3DataType | |
| -> Text | |
| -> S3DataSource |
Creates a value of S3DataSource with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
sdsS3DataDistributionType- If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specifyFullyReplicated. If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specifyShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both FILE and PIPE modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might chooseShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputModeis set toFile), this copies 1/n of the number of objects.sdsS3DataType- If you chooseS3Prefix,S3Uriidentifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for model training. If you chooseManifestFile,S3Uriidentifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.sdsS3URI- Depending on the value specified for theS3DataType, identifies either a key name prefix or a manifest. For example: * A key name prefix might look like this:s3:/bucketnameexampleprefix. * A manifest might look like this:s3:/bucketnameexample.manifestThe manifest is an S3 object which is a JSON file with the following format:[{"prefix": "s3:/customer_bucketsomeprefix"},"relativepathto/custdata-1","relativepathcustdata-2",...]The preceding JSON matches the followings3Uris:s3:/customer_bucketsomeprefixrelativepathto/custdata-1s3:/customer_bucketsomeprefixrelativepathcustdata-1...The complete set ofs3urisin this manifest constitutes the input data for the channel for this datasource. The object that eachs3urispoints to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
sdsS3DataDistributionType :: Lens' S3DataSource (Maybe S3DataDistribution) Source #
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated . If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both FILE and PIPE modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
sdsS3DataType :: Lens' S3DataSource S3DataType Source #
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for model training. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
sdsS3URI :: Lens' S3DataSource Text Source #
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example: * A key name prefix might look like this: s3:/bucketnameexampleprefix . * A manifest might look like this: s3:/bucketnameexample.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3:/customer_bucketsomeprefix"}, "relativepathto/custdata-1", "relativepathcustdata-2", ... ] The preceding JSON matches the following s3Uris : s3:/customer_bucketsomeprefixrelativepathto/custdata-1 s3:/customer_bucketsomeprefixrelativepathcustdata-1 ... The complete set of s3uris in this manifest constitutes the input data for the channel for this datasource. The object that each s3uris points to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
StoppingCondition
data StoppingCondition Source #
Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost.
Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (CreateModel ).
See: stoppingCondition smart constructor.
Instances
stoppingCondition :: StoppingCondition Source #
Creates a value of StoppingCondition with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
scMaxRuntimeInSeconds- The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.
scMaxRuntimeInSeconds :: Lens' StoppingCondition (Maybe Natural) Source #
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.
Tag
Describes a tag.
See: tag smart constructor.
TrainingJobSummary
data TrainingJobSummary Source #
Provides summary information about a training job.
See: trainingJobSummary smart constructor.
Instances
Arguments
| :: Text | |
| -> Text | |
| -> UTCTime | |
| -> TrainingJobStatus | |
| -> TrainingJobSummary |
Creates a value of TrainingJobSummary with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
tjsTrainingEndTime- A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed,Failed, orStopped).tjsLastModifiedTime- Timestamp when the training job was last modified.tjsTrainingJobName- The name of the training job that you want a summary for.tjsTrainingJobARN- The Amazon Resource Name (ARN) of the training job.tjsCreationTime- A timestamp that shows when the training job was created.tjsTrainingJobStatus- The status of the training job.
tjsTrainingEndTime :: Lens' TrainingJobSummary (Maybe UTCTime) Source #
A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed , Failed , or Stopped ).
tjsLastModifiedTime :: Lens' TrainingJobSummary (Maybe UTCTime) Source #
Timestamp when the training job was last modified.
tjsTrainingJobName :: Lens' TrainingJobSummary Text Source #
The name of the training job that you want a summary for.
tjsTrainingJobARN :: Lens' TrainingJobSummary Text Source #
The Amazon Resource Name (ARN) of the training job.
tjsCreationTime :: Lens' TrainingJobSummary UTCTime Source #
A timestamp that shows when the training job was created.
tjsTrainingJobStatus :: Lens' TrainingJobSummary TrainingJobStatus Source #
The status of the training job.
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 'host-vpc' and 'train-vpc' .
See: vpcConfig smart constructor.
Creates a value of VPCConfig with the minimum fields required to make a request.
Use one of the following lenses to modify other fields as desired:
vcSecurityGroupIds- The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in theSubnetsfield.vcSubnets- The ID of the subnets in the VPC to which you want to connect your training job or model.