Values_1.DebugRuleConfigurationSourceConfiguration 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.
type nonrec t = {ruleConfigurationName : RuleConfigurationName.t;The name of the rule configuration. It must be unique relative to other rule configuration names.
*)localPath : Values_0.DirectoryPath.t option;Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
*)s3OutputPath : Values_0.S3Uri.t option;Path to Amazon S3 storage location for rules.
*)ruleEvaluatorImage : Values_0.AlgorithmImage.t;The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
*)instanceType : Values_0.ProcessingInstanceType.t option;The instance type to deploy a custom rule for debugging a training job.
*)volumeSizeInGB : Values_0.OptionalVolumeSizeInGB.t option;The size, in GB, of the ML storage volume attached to the processing instance.
*)ruleParameters : RuleParameters.t option;Runtime configuration for rule container.
*)}val make :
?localPath:??? ->
?s3OutputPath:??? ->
?instanceType:??? ->
?volumeSizeInGB:??? ->
?ruleParameters:??? ->
ruleConfigurationName:RuleConfigurationName.t ->
ruleEvaluatorImage:Values_0.AlgorithmImage.t ->
unit ->
tval to_value :
t ->
[> `Structure of
(string
* [> `Enum of string
| `Integer of Values_0.OptionalVolumeSizeInGB.t
| `Map of
([> `String of Values_0.ConfigKey.t ]
* [> `String of Values_0.ConfigValue.t ])
list
| `String of RuleConfigurationName.t ])
list ]