smdebug-rulesconfig

from sagemaker.debugger import Rule, CollectionConfig, rule_configs from sagemaker.estimator import Estimator # Placeholder for a SageMaker Estimator import os # In a real scenario, replace this with your actual SageMaker Estimator setup # and ensure AWS credentials are configured (e.g., via environment variables or AWS CLI) # For example: estimator = TensorFlow(role=os.environ.get('SAGEMAKER_ROLE', 'arn:aws:iam::123456789012:role/SageMakerRole'), ...) # We use a placeholder here for quickstart reproducibility. # Example: Vanilla built-in rule without customization # This would typically be passed to a SageMaker Estimator's 'rules' parameter. rule_vanishing_gradient = Rule.sagemaker(rule_configs.vanishing_gradient()) print(f"Vanishing Gradient Rule: {rule_vanishing_gradient.name}") # Example: Built-in rule with customization # This demonstrates how to customize a rule's parameters and collections. rule_customized_weight_update = Rule.sagemaker( base_config=rule_configs.weight_update_ratio(), name="my_custom_wup_rule", # Optional # container_local_path="/local/path", # Optional, if running locally or specific container path # s3_output_path="s3://your-s3-bucket/debug-output/", # Optional, overrides default rule_parameters={ "threshold": "0.001" # Example parameter customization }, collections_to_save=[ CollectionConfig( name="weights", # Required. Debugger will collect tensors for this collection. parameters={ "save_interval": "100" # Example collection parameter } ) ] ) print(f"Customized Weight Update Ratio Rule: {rule_customized_weight_update.name}") print(f" Rule parameters: {rule_customized_weight_update.rule_parameters}") print(f" Collections to save: {[c.name for c in rule_customized_weight_update.collections_to_save]}")