rule_config: List of Debugger Built-in Rules
In DyfanJones/sagemaker-r-debugger: R6sagemaker debugger for sagemaker operations

Description Usage Value

Use the Debugger built-in rules provided by Amazon SageMaker Debugger and analyze tensors emitted while training your models. The Debugger built-in rules monitor various common conditions that are critical for the success of a training job. You can call the built-in rules using Amazon SageMaker Python SDK or the low-level SageMaker API operations. Depending on deep learning frameworks of your choice, there are four scopes of validity for the built-in rules as shown in the following table. https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html

vanishing_gradient()

similar_across_runs()

weight_update_ratio()

all_zero()

exploding_tensor()

unchanged_tensor()

loss_not_decreasing()

check_input_images()

dead_relu()

confusion()

tree_depth()

class_imbalance()

overfit()

tensor_variance()

overtraining()

poor_weight_initialization()

saturated_activation()

nlp_sequence_ratio()

stalled_training_rule()

feature_importance_overweight()

create_xgboost_report()