List of Debugger Built-in Rules

Use the Debugger built-in rules provided by Amazon SageMaker Debugger and analyze metrics and tensors collected 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. There's no additional cost for using the built-in rules. For more information about billing, see the Amazon SageMaker Pricing page.

Note

The maximum numbers of built-in rules that you can attach to a training job are 20 for ProfilerRule and 20 for Rule. SageMaker Debugger fully manages the built-in rules and analyzes your training job synchronously.

Important

To use the new Debugger features, you need to upgrade the SageMaker Python SDK and the SMDebug client library. In your iPython kernel, Jupyter notebook, or JupyterLab environment, run the following code to install the latest versions of the libraries and restart the kernel.

import sys
import IPython
!{sys.executable} -m pip install -U sagemaker smdebug
IPython.Application.instance().kernel.do_shutdown(True)

Debugger ProfilerRule

The following rules are the Debugger built-in rules that are callable using the ProfilerRule.sagemaker classmethod.

Debugger built-in rule for generating the profiling report
Scope of Validity	Built-in Rules
Profiling Report for any SageMaker training job	ProfilerReport

Debugger built-in rules for profiling hardware system resource utilization (system metrics)
Scope of Validity	Built-in Rules
Generic system monitoring rules for any SageMaker training job	BatchSize CPUBottleneck GPUMemoryIncrease IOBottleneck LoadBalancing LowGPUUtilization OverallSystemUsage

Debugger built-in rules for profiling framework metrics
Scope of Validity	Built-in Rules
Profiling rules for deep learning frameworks (TensorFlow and PyTorch)	MaxInitializationTime OverallFrameworkMetrics StepOutlier

Warning

SageMaker Debugger deprecates the framework profiling feature starting from TensorFlow 2.11 and PyTorch 2.0. You can still use the feature in the previous versions of the frameworks and SDKs as follows.

• SageMaker Python SDK <= v2.130.0

• PyTorch >= v1.6.0, < v2.0

• TensorFlow >= v2.3.1, < v2.11

With the deprecation, SageMaker Debugger also discontinues support for the three ProfilerRules for framework profiling. See also Amazon SageMaker Debugger Release Notes: March 16, 2023.

Debugger Rule

The following rules are the Debugger built-in rules that are callable using the Rule.sagemaker classmethod.

Debugger built-in rules for generating training reports
Scope of Validity	Built-in Rules
Training Report for SageMaker XGboost training job	create_xgboost_report

Debugger built-in rules for debugging model training data (output tensors)
Scope of Validity	Built-in Rules
Deep learning frameworks (TensorFlow, MXNet, and PyTorch)	dead_relu exploding_tensor poor_weight_initialization saturated_activation vanishing_gradient weight_update_ratio
Deep learning frameworks (TensorFlow, MXNet, and PyTorch) and the XGBoost algorithm	all_zero class_imbalance loss_not_decreasing overfit overtraining similar_across_runs stalled_training_rule tensor_variance unchanged_tensor
Deep learning applications	check_input_images nlp_sequence_ratio
XGBoost algorithm	confusion feature_importance_overweight tree_depth

To use the built-in rules with default parameter values – use the following configuration format:

from sagemaker.debugger import Rule, ProfilerRule, rule_configs

rules = [
    ProfilerRule.sagemaker(rule_configs.BuiltInRuleName_1()),
    ProfilerRule.sagemaker(rule_configs.BuiltInRuleName_2()),
    ...
    ProfilerRule.sagemaker(rule_configs.BuiltInRuleName_n()),
    Rule.sagemaker(rule_configs.built_in_rule_name_1()),
    Rule.sagemaker(rule_configs.built_in_rule_name_2()),
    ...
    Rule.sagemaker(rule_configs.built_in_rule_name_n())
]

To use the built-in rules with customizing the parameter values – use the following configuration format:

from sagemaker.debugger import Rule, ProfilerRule, rule_configs

rules = [
    ProfilerRule.sagemaker(
        base_config=rule_configs.BuiltInRuleName(),
        rule_parameters={
                "key": "value"
        }
    )
    Rule.sagemaker(
        base_config=rule_configs.built_in_rule_name(),
        rule_parameters={
                "key": "value"
        }
        collections_to_save=[ 
            CollectionConfig(
                name="tensor_collection_name", 
                parameters={
                    "key": "value"
                } 
            )
        ]
    )
]

To find available keys for the rule_parameters parameter, see the parameter description tables.

Sample rule configuration codes are provided for each built-in rule below the parameter description tables.

• For a full instruction and examples of using the Debugger built-in rules, see Debugger Built-in Rules Example Code.

• For a full instruction on using the built-in rules with the low-level SageMaker API operations, see Configure Debugger Using Amazon SageMaker API.

ProfilerReport

The ProfilerReport rule invokes all of the built-in rules for monitoring and profiling. It creates a profiling report and updates when the individual rules are triggered. You can download a comprehensive profiling report while a training job is running or after the training job is complete. You can adjust the rule parameter values to customize sensitivity of the built-in monitoring and profiling rules. The following example code shows the basic format to adjust the built-in rule parameters through the ProfilerReport rule.

rules=[
    ProfilerRule.sagemaker(
        rule_configs.ProfilerReport(
            <BuiltInRuleName>_<parameter_name> = value
        )
    )  
]

If you trigger this ProfilerReport rule without any customized parameter as shown in the following example code, then the ProfilerReport rule triggers all of the built-in rules for monitoring and profiling with their default parameter values.

rules=[ProfilerRule.sagemaker(rule_configs.ProfilerReport())]

The following example code shows how to specify and adjust the CPUBottleneck rule's cpu_threshold parameter and the IOBottleneck rule's threshold parameter.

rules=[
    ProfilerRule.sagemaker(
        rule_configs.ProfilerReport(
            CPUBottleneck_cpu_threshold = 90,
            IOBottleneck_threshold = 90
        )
    )  
]

To explore what's in the profiler report, see SageMaker Debugger Profiling Report. Also, because this rule activates all of the profiling rules, you can also check the rule analysis status using the SageMaker Debugger UI in SageMaker Studio Experiments.

Parameter Descriptions for the OverallSystemUsage Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
<BuiltInRuleName>_<parameter_name>	Customizable parameter to adjust thresholds of other built-in monitoring and profiling rules. Optional Default value: None

BatchSize

The BatchSize rule helps detect if GPU is underutilized due to a small batch size. To detect this issue, this rule monitors the average CPU utilization, GPU utilization, and GPU memory utilization. If utilization on CPU, GPU, and GPU memory is low on average, it may indicate that the training job can either run on a smaller instance type or can run with a bigger batch size. This analysis does not work for frameworks that heavily overallocate memory. However, increasing the batch size can lead to processing or data loading bottlenecks because more data preprocessing time is required in each iteration.

Parameter Descriptions for the BatchSize Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
cpu_threshold_p95	Defines the threshold for 95th quantile of CPU utilization in percentage. Optional Valid values: Integer Default value: 70 (in percentage)
gpu_threshold_p95	Defines the threshold for 95th quantile of GPU utilization in percentage. Optional Valid values: Integer Default value: 70 (in percentage)
gpu_memory_threshold_p95	Defines the threshold for 95th quantile of GPU memory utilization in percentage. Optional Valid values: Integer Default values: 70 (in percentage)
patience	Defines the number of data points to skip until the rule starts evaluation. The first several steps of training jobs usually show high volume of data processes, so keep the rule patient and prevent it from being invoked too soon with a given number of profiling data that you specify with this parameter. Optional Valid values: Integer Default values: 100
window	Window size for computing quantiles. Optional Valid values: Integer Default values: 500
scan_interval_us	Time interval that timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

CPUBottleneck

The CPUBottleneck rule helps detect if GPU is underutilized due to CPU bottlenecks. Rule returns True if number of CPU bottlenecks exceeds a predefined threshold.

Parameter Descriptions for the CPUBottleneck Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold	Defines the threshold for proportion of bottlenecked time to the total training time. If the proportion exceeds the percentage specified to the threshold parameter, the rule switches the rule status to True. Optional Valid values: Integer Default value: 50 (in percentage)
gpu_threshold	A threshold that defines low GPU utilization. Optional Valid values: Integer Default value: 10 (in percentage)
cpu_threshold	A threshold that defines high CPU utilization. Optional Valid values: Integer Default values: 90 (in percentage)
patience	Defines the number of data points to skip until the rule starts evaluation. The first several steps of training jobs usually show high volume of data processes, so keep the rule patient and prevent it from being invoked too soon with a given number of profiling data that you specify with this parameter. Optional Valid values: Integer Default values: 100
scan_interval_us	Time interval with which timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

GPUMemoryIncrease

The GPUMemoryIncrease rule helps detect a large increase in memory usage on GPUs.

Parameter Descriptions for the GPUMemoryIncrease Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
increase	Defines the threshold for absolute memory increase. Optional Valid values: Integer Default value: 10 (in percentage)
patience	Defines the number of data points to skip until the rule starts evaluation. The first several steps of training jobs usually show high volume of data processes, so keep the rule patient and prevent it from being invoked too soon with a given number of profiling data that you specify with this parameter. Optional Valid values: Integer Default values: 100
window	Window size for computing quantiles. Optional Valid values: Integer Default values: 500
scan_interval_us	Time interval that timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

IOBottleneck

This rule helps to detect if GPU is underutilized due to data IO bottlenecks. Rule returns True if number of IO bottlenecks exceeds a predefined threshold.

Parameter Descriptions for the IOBottleneck Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold	Defines the threshold when Rule to return True. Optional Valid values: Integer Default value: 50 (in percentage)
gpu_threshold	A threshold that defines when GPU is considered underutilized. Optional Valid values: Integer Default value: 70 (in percentage)
io_threshold	A threshold that defines high IO wait time. Optional Valid values: Integer Default values: 50 (in percentage)
patience	Defines the number of data points to skip until the rule starts evaluation. The first several steps of training jobs usually show high volume of data processes, so keep the rule patient and prevent it from being invoked too soon with a given number of profiling data that you specify with this parameter. Optional Valid values: Integer Default values: 1000
scan_interval_us	Time interval that timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

LoadBalancing

The LoadBalancing rule helps detect issues in workload balancing among multiple GPUs.

Parameter Descriptions for the LoadBalancing Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold	Defines the workload percentage. Optional Valid values: Integer Default value: 0.5 (unitless proportion)
patience	Defines the number of data points to skip until the rule starts evaluation. The first several steps of training jobs usually show high volume of data processes, so keep the rule patient and prevent it from being invoked too soon with a given number of profiling data that you specify with this parameter. Optional Valid values: Integer Default values: 10
scan_interval_us	Time interval that timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

LowGPUUtilization

The LowGPUUtilization rule helps detect if GPU utilization is low or suffers from fluctuations. This is checked for each GPU on each worker. Rule returns True if 95th quantile is below threshold_p95 which indicates underutilization. Rule returns true if 95th quantile is above threshold_p95 and 5th quantile is below threshold_p5 which indicates fluctuations.

Parameter Descriptions for the LowGPUUtilization Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold_p95	A threshold for 95th quantile below which GPU is considered to be underutilized. Optional Valid values: Integer Default value: 70 (in percentage)
threshold_p5	A threshold for 5th quantile. Default is 10 percent. Optional Valid values: Integer Default values: 10 (in percentage)
patience	Defines the number of data points to skip until the rule starts evaluation. The first several steps of training jobs usually show high volume of data processes, so keep the rule patient and prevent it from being invoked too soon with a given number of profiling data that you specify with this parameter. Optional Valid values: Integer Default values: 1000
window	Window size for computing quantiles. Optional Valid values: Integer Default values: 500
scan_interval_us	Time interval that timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

OverallSystemUsage

The OverallSystemUsage rule measures overall system usage per worker node. The rule currently only aggregates values per node and computes their percentiles.

Parameter Descriptions for the OverallSystemUsage Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
scan_interval_us	Time interval to scan timeline files. Optional Valid values: Integer Default values: 60000000 (in microseconds)

MaxInitializationTime

The MaxInitializationTime rule helps detect if the training initialization is taking too much time. The rule waits until the first step is available.

Parameter Descriptions for the MaxInitializationTime Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold	Defines the threshold in minutes to wait for the first step to become available. Optional Valid values: Integer Default value: 20 (in minutes)
scan_interval_us	Time interval with which timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

OverallFrameworkMetrics

The OverallFrameworkMetrics rule summarizes the time spent on framework metrics, such as forward and backward pass, and data loading.

Parameter Descriptions for the OverallFrameworkMetrics Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
scan_interval_us	Time interval to scan timeline files. Optional Valid values: Integer Default values: 60000000 (in microseconds)

StepOutlier

The StepOutlier rule helps detect outliers in step durations. This rule returns True if there are outliers with step durations larger than stddev sigmas of the entire step durations in a time range.

Parameter Descriptions for the StepOutlier Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
stddev	Defines a factor by which to multiply the standard deviation. For example, the rule is invoked by default when a step duration is larger or smaller than 5 times the standard deviation. Optional Valid values: Integer Default value: 5 (in minutes)
mode	Mode under which steps have been saved and on which Rule should run on. Per default rule will run on steps from EVAL and TRAIN phase Optional Valid values: Integer Default value: 5 (in minutes)
n_outliers	How many outliers to ignore before rule returns True Optional Valid values: Integer Default value: 10
scan_interval_us	Time interval with which timeline files are scanned. Optional Valid values: Integer Default values: 60000000 (in microseconds)

CreateXgboostReport

The CreateXgboostReport rule collects output tensors from an XGBoost training job and autogenerates a comprehensive training report. You can download a comprehensive profiling report while a training job is running or after the training job is complete, and check progress of training or the final result of the training job. The CreateXgboostReport rule collects the following output tensors by default:

• hyperparameters – Saves at the first step

• metrics – Saves loss and accuracy every 5 steps

• feature_importance – Saves every 5 steps

• predictions – Saves every 5 steps

• labels – Saves every 5 steps

Parameter Descriptions for the CreateXgboostReport Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String

rules=[
    Rule.sagemaker(
        rule_configs.create_xgboost_report()
    )  
]

DeadRelu

This rule detects when the percentage of rectified linear unit (ReLU) activation functions in a trial are considered dead because their activation activity has dropped below a threshold. If the percent of inactive ReLUs in a layer is greater than the threshold_layer value of inactive ReLUs, the rule returns True.

Parameter Descriptions for the DeadRelu Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: ".*relu_output"
threshold_inactivity	Defines a level of activity below which a ReLU is considered to be dead. A ReLU might be active in the beginning of a trial and then slowly die during the training process. If the ReLU is active less than the threshold_inactivity, it is considered to be dead. Optional Valid values: Float Default values: 1.0 (in percentage)
threshold_layer	Returns True if the percentage of inactive ReLUs in a layer is greater than threshold_layer. Returns False if the percentage of inactive ReLUs in a layer is less than threshold_layer. Optional Valid values: Float Default values: 50.0 (in percentage)

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.dead_relu(),
        rule_parameters={
                "tensor_regex": ".*relu_output|.*ReLU_output",
                "threshold_inactivity": "1.0",
                "threshold_layer": "50.0"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="custom_relu_collection", 
                parameters={
                    "include_regex: ".*relu_output|.*ReLU_output",
                    "save_interval": "500"
                } 
            )
        ]
    )
]

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

This rule is not available for the XGBoost algorithm.

ExplodingTensor

This rule detects whether the tensors emitted during training have non-finite values, either infinite or NaN (not a number). If a non-finite value is detected, the rule returns True.

Parameter Descriptions for the ExplodingTensor Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
collection_names	The list of collection names whose tensors the rule inspects. Optional Valid values: String Default value: None
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: String Default value: None
only_nan	True to monitor the base_trial tensors only for NaN values and not for infinity. False to treat both NaN and infinity as exploding values and to monitor for both. Optional Default value: False

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.exploding_tensor(),
        rule_parameters={
                "tensor_regex": ".*gradient",
                "only_nan": "False"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="gradients", 
                parameters={
                    "save_interval": "500"
                }
            )
        ]
    )
]

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

This rule is not available for the XGBoost algorithm.

PoorWeightInitialization

This rule detects if your model parameters have been poorly initialized.

Good initialization breaks the symmetry of the weights and gradients in a neural network and maintains commensurate activation variances across layers. Otherwise, the neural network doesn't learn effectively. Initializers like Xavier aim to keep variance constant across activations, which is especially relevant for training very deep neural nets. Too small an initialization can lead to vanishing gradients. Too large an initialization can lead to exploding gradients. This rule checks the variance of activation inputs across layers, the distribution of gradients, and the loss convergence for the initial steps to determine if a neural network has been poorly initialized.

Parameter Descriptions for the PoorWeightInitialization Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
activation_inputs_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: String Default value: ".*relu_input"
threshold	If the ratio between minimum and maximum variance of weights per layer exceeds the threshold at a step, the rule returns True. Optional Valid values: Float Default value: 10.0
distribution_range	If the minimum difference between 5th and 95th percentiles of the gradient distribution is less than the distribution_range, the rule returns True. Optional Valid values: Float Default value: 0.001
patience	The number of steps to wait until the loss is considered to be no longer decreasing. Optional Valid values: Integer Default value: 5
steps	The number of steps this rule analyzes. You typically need to check only the first few iterations. Optional Valid values: Float Default value: 10

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.poor_weight_initialization(),
        rule_parameters={
                "activation_inputs_regex": ".*relu_input|.*ReLU_input",
                "threshold": "10.0",
                "distribution_range": "0.001",
                "patience": "5",
                "steps": "10"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="custom_relu_collection", 
                parameters={
                    "include_regex": ".*relu_input|.*ReLU_input",
                    "save_interval": "500"
                } 
            )
        ]
    )
]

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

This rule is not available for the XGBoost algorithm.

SaturatedActivation

This rule detects if the tanh and sigmoid activation layers are becoming saturated. An activation layer is saturated when the input of the layer is close to the maximum or minimum of the activation function. The minimum and maximum of the tanh and sigmoid activation functions are defined by their respective min_threshold and max_thresholds values. If the activity of a node drops below the threshold_inactivity percentage, it is considered saturated. If more than a threshold_layer percent of the nodes are saturated, the rule returns True.

Parameter Descriptions for the SaturatedActivation Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
collection_names	The list of collection names whose tensors the rule inspects. Optional Valid values: List of strings or a comma-separated string Default value: None
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: String Default value: ".*tanh_input|.*sigmoid_input".
threshold_tanh_min	The minimum and maximum thresholds that define the extremes of the input for a tanh activation function, defined as: (min_threshold, max_threshold). The default values are determined based on a vanishing gradient threshold of 0.0000001. Optional Valid values: Float Default values: -9.4999
threshold_tanh_max	The minimum and maximum thresholds that define the extremes of the input for a tanh activation function, defined as: (min_threshold, max_threshold). The default values are determined based on a vanishing gradient threshold of 0.0000001. Optional Valid values: Float Default values: 9.4999
threshold_sigmoid_min	The minimum and maximum thresholds that define the extremes of the input for a sigmoid activation function, defined as: (min_threshold, max_threshold). The default values are determined based on a vanishing gradient threshold of 0.0000001. Optional Valid values: Float Default values: -23
threshold_sigmoid_max	The minimum and maximum thresholds that define the extremes of the input for a sigmoid activation function, defined as: (min_threshold, max_threshold). The default values are determined based on a vanishing gradient threshold of 0.0000001. Optional Valid values: Float Default values: 16.99999
threshold_inactivity	The percentage of inactivity below which the activation layer is considered to be saturated. The activation might be active in the beginning of a trial and then slowly become less active during the training process. Optional Valid values: Float Default values: 1.0
threshold_layer	Returns True if the number of saturated activations in a layer is greater than the threshold_layer percentage. Returns False if the number of saturated activations in a layer is less than the threshold_layer percentage. Optional Valid values: Float Default values: 50.0

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.saturated_activation(),
        rule_parameters={
                "tensor_regex": ".*tanh_input|.*sigmoid_input",
                "threshold_tanh_min": "-9.4999",
                "threshold_tanh_max": "9.4999",
                "threshold_sigmoid_min": "-23",
                "threshold_sigmoid_max": "16.99999",
                "threshold_inactivity": "1.0",
                "threshold_layer": "50.0"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="custom_activations_collection",
                parameters={
                    "include_regex": ".*tanh_input|.*sigmoid_input"
                    "save_interval": "500"
                } 
            )
        ]
    )
]

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

This rule is not available for the XGBoost algorithm.

VanishingGradient

This rule detects if the gradients in a trial become extremely small or drop to a zero magnitude. If the mean of the absolute values of the gradients drops below a specified threshold, the rule returns True.

Parameters Descriptions for the VanishingGradient Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold	The value at which the gradient is determined to be vanishing. Optional Valid values: Float Default value: 0.0000001.

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.vanishing_gradient(),
        rule_parameters={
                "threshold": "0.0000001"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="gradients", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

This rule is not available for the XGBoost algorithm.

WeightUpdateRatio

This rule keeps track of the ratio of updates to weights during training and detects if that ratio gets too large or too small. If the ratio of updates to weights is larger than the large_threshold value or if this ratio is smaller than small_threshold, the rule returns True.

Conditions for training are best when the updates are commensurate to gradients. Excessively large updates can push the weights away from optimal values, and very small updates result in very slow convergence. This rule requires weights to be available for two training steps, and train.save_interval needs to be set equal to num_steps.

Parameter Descriptions for the WeightUpdateRatio Rule
Parameter Name,	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
num_steps	The number of steps across which the rule checks to determine if the tensor has changed. The number of steps across which you want to compare the weight ratios. If you pass no value, the rule runs by default against the current step and the immediately previous saved step. If you override the default by passing a value for this parameter, the comparison is done between weights at step s and at a step >= s - num_steps. Optional Valid values: Integer Default value: None
large_threshold	The maximum value that the ratio of updates to weight can take before the rule returns True. Optional Valid values: Float Default value: 10.0
small_threshold	The minimum value that the ratio of updates to weight can take, below which the rule returns True. Optional Valid values: Float Default value: 0.00000001
epsilon	A small constant used to ensure that Debugger does not divide by zero when computing the ratio updates to weigh. Optional Valid values: Float Default value: 0.000000001

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.weight_update_ratio(),
        rule_parameters={
                "num_steps": "100",
                "large_threshold": "10.0",
                "small_threshold": "0.00000001",
                "epsilon": "0.000000001"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="weights", 
                parameters={
                    "train.save_interval": "100"
                } 
            )
        ]
    )
]

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

This rule is not available for the XGBoost algorithm.

AllZero

This rule detects if all or a specified percentage of the tensor values are zero.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm. You must specify either the collection_names or tensor_regex parameter. If both the parameters are specified, the rule inspects the union of tensors from both sets.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameters Descriptions for the AllZero Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
collection_names	The list of collection names whose tensors the rule inspects. Optional Valid values: List of strings or a comma-separated string Default value: None
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: None
threshold	Specifies the percentage of values in the tensor that needs to be zero for this rule to be invoked. Optional Valid values: Float Default value: 100 (in percentage)

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.all_zero(),
        rule_parameters={
                "tensor_regex": ".*",
                "threshold": "100"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="all", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

ClassImbalance

This rule measures sampling imbalances between classes and throws errors if the imbalance exceeds a threshold or if too many mispredictions for underrepresented classes occur as a result of the imbalance.

Classification models require well-balanced classes in the training dataset or a proper weighting/sampling of classes during training. The rule performs the following checks:

• It counts the occurrences per class. If the ratio of number of samples between smallest and largest class is larger than the threshold_imbalance, an error is thrown.

• It checks the prediction accuracy per class. If resampling or weighting has not been correctly applied, then the model can reach high accuracy for the class with many training samples, but low accuracy for the classes with few training samples. If a fraction of mispredictions for a certain class is above threshold_misprediction, an error is thrown.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the ClassImbalance Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold_imbalance	The acceptable imbalance between the number of samples in the smallest class and in the largest class. Exceeding this threshold value throws an error. Optional Valid values: Float Default value: 10
threshold_misprediction	A limit on the fraction of mispredictions allowed for each class. Exceeding this threshold throws an error. The underrepresented classes are most at risk of crossing this threshold. Optional Valid values: Float Default value: 0.7
samples	The number of labels that have to be processed before an imbalance is evaluated. The rule might not be triggered until it has seen sufficient samples across several steps. The more classes that your dataset contains, the larger this sample number should be. Optional Valid values: Integer Default value: 500 (assuming a dataset like MNIST with 10 classes)
argmax	If True, np.argmax is applied to the prediction tensor. Required when you have a vector of probabilities for each class. It is used to determine which class has the highest probability. Conditional Valid values: Boolean Default value: False
labels_regex	The name of the tensor that contains the labels. Optional Valid values: String Default value: ".*labels"
predictions_regex	The name of the tensor that contains the predictions. Optional Valid values: String Default value: ".*predictions"

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.class_imbalance(),
        rule_parameters={
                "threshold_imbalance": "10",
                "threshold_misprediction": "0.7",
                "samples": "500",
                "argmax": "False",
                "labels_regex": ".*labels",
                "predictions_regex": ".*predictions"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="custom_output_collection",
                parameters={
                    "include_regex": ".*labels|.*predictions",
                    "save_interval": "500"
                } 
            )
        ]
    )
]

LossNotDecreasing

This rule detects when the loss is not decreasing in value at an adequate rate. These losses must be scalars.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm. You must specify either the collection_names or tensor_regex parameter. If both the parameters are specified, the rule inspects the union of tensors from both sets.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the LossNotDecreasing Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
collection_names	The list of collection names whose tensors the rule inspects. Optional Valid values: List of strings or a comma-separated string Default value: None
tensor_regex	A list of regex patterns that is used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: None
use_losses_collection	If set to True, looks for losses in the collection named "losses" when the collection is present. Optional Valid values: Boolean Default value: True
num_steps	The minimum number of steps after which the rule checks if the loss has decreased. Rule evaluation happens every num_steps. The rule compares the loss for this step with the loss at a step which is at least num_steps behind the current step. For example, suppose that the loss is being saved every three steps, but num_steps is set to 10. At step 21, loss for step 21 is compared with loss for step 9. The next step at which loss is checked is step 33, because ten steps after step 21 is step 31, and at step 31 and step 32 loss is not saved. Optional Valid values: Integer Default value: 10
diff_percent	The minimum percentage difference by which the loss should decrease between num_steps. Optional Valid values: 0.0 < float < 100 Default value: 0.1 (in percentage)
increase_threshold_percent	The maximum threshold percent that loss is allowed to increase in case loss has been increasing Optional Valid values: 0 < float < 100 Default value: 5 (in percentage)
mode	The name of the Debugger mode to query tensor values for rule checking. If this is not passed, the rule checks in order by default for the mode.EVAL, then mode.TRAIN, and then mode.GLOBAL. Optional Valid values: String (EVAL, TRAIN, or GLOBAL) Default value: GLOBAL

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.loss_not_decreasing(),
        rule_parameters={
                "tensor_regex": ".*",
                "use_losses_collection": "True",
                "num_steps": "10",
                "diff_percent": "0.1",
                "increase_threshold_percent": "5",
                "mode": "GLOBAL"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="losses", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

Overfit

This rule detects if your model is being overfit to the training data by comparing the validation and training losses.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

A standard way to prevent overfitting is to regularize your model.

Parameter Descriptions for the Overfit Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: None
start_step	The step from which to start comparing the validation and training loss. Optional Valid values: Integer Default value: 0
patience	The number of steps for which the ratio_threshold is allowed to exceed the value set before the model is considered to be overfit. Optional Valid values: Integer Default value: 1
ratio_threshold	The maximum ratio of the difference between the mean validation loss and mean training loss to the mean training loss. If this threshold is exceeded for a patience number of steps, the model is being overfit and the rule returns True. Optional Valid values: Float Default value: 0.1

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.overfit(),
        rule_parameters={
                "tensor_regex": ".*",
                "start_step": "0",
                "patience": "1",
                "ratio_threshold": "0.1"
        },
        collections_to_save=[
            CollectionConfig(
                name="losses", 
                parameters={
                    "train.save_interval": "100",
                    "eval.save_interval": "10"
                } 
            )
        ]
    )
]

Overtraining

This rule detects if a model is being overtrained. After a number of training iterations on a well-behaved model (both training and validation loss decrease), the model approaches to a minimum of the loss function and does not improve anymore. If the model continues training it can happen that validation loss starts increasing, because the model starts overfitting. This rule sets up thresholds and conditions to determine if the model is not improving, and prevents overfitting problems due to overtraining.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Note

Overtraining can be avoided by early stopping. For information on early stopping, see Stop Training Jobs Early. For an example that shows how to use spot training with Debugger, see Enable Spot Training with Amazon SageMaker Debugger.

Parameter Descriptions for the Overtraining Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
patience_train	The number of steps to wait before the training loss is considered to not to be improving anymore. Optional Valid values: Integer Default value: 5
patience_validation	The number of steps to wait before the validation loss is considered to not to be improving anymore. Optional Valid values: Integer Default value: 10
delta	The minimum threshold by how much the error should improve before it is considered as a new optimum. Optional Valid values: Float Default value: 0.01

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.overtraining(),
        rule_parameters={
                "patience_train": "5",
                "patience_validation": "10",
                "delta": "0.01"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="losses", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

SimilarAcrossRuns

This rule compares tensors gathered from a base trial with tensors from another trial.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the SimilarAcrossRuns Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
other_trials	A completed training job name whose tensors you want to compare to those tensors gathered from the current base_trial. Required Valid values: String
collection_names	The list of collection names whose tensors the rule inspects. Optional Valid values: List of strings or a comma-separated string Default value: None
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: None

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.similar_across_runs(),
        rule_parameters={
                "other_trials": "<specify-another-job-name>",
                "collection_names": "losses",
                "tensor_regex": ".*"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="losses", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

StalledTrainingRule

StalledTrainingRule detects if there is no progress made on training job, and stops the training job if the rule fires. This rule requires tensors to be periodically saved in a time interval defined by its threshold parameter. This rule keeps on monitoring for new tensors, and if no new tensor has been emitted for threshold interval rule gets fired.

Parameter Descriptions for the StalledTrainingRule Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold	A threshold that defines by how much time in seconds the rule waits for a tensor output until it fires a stalled training issue. Default value is 1800 seconds. Optional Valid values: Integer Default value: 1800
stop_training_on_fire	If set to True, watches if the base training job outputs tensors in "threshold" seconds. Optional Valid values: Boolean Default value: False
training_job_name_prefix	The prefix of base training job name. If stop_training_on_fire is true, the rule searches for SageMaker training jobs with this prefix in the same account. If there is an inactivity found, the rule takes a StopTrainingJob action. Note if there are multiple jobs found with same prefix, the rule skips termination. It is important that the prefix is set unique per each training job. Optional Valid values: String

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.stalled_training_rule(),
        rule_parameters={
                "threshold": "1800",
                "stop_training_on_fire": "True",
                "training_job_name_prefix": "<specify-training-base-job-name>"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="losses", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

TensorVariance

This rule detects if you have tensors with very high or low variances. Very high or low variances in a tensor could lead to neuron saturation, which reduces the learning ability of the neural network. Very high variance in tensors can also eventually lead to exploding tensors. Use this rule to detect such issues early.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm. You must specify either the collection_names or tensor_regex parameter. If both the parameters are specified, the rule inspects the union of tensors from both sets.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the TensorVariance Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
collection_names	The list of collection names whose tensors the rule inspects. Optional Valid values: List of strings or a comma-separated string Default value: None
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: None
max_threshold	The threshold for the upper bound of tensor variance. Optional Valid values: Float Default value: None
min_threshold	The threshold for the lower bound of tensor variance. Optional Valid values: Float Default value: None

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.tensor_variance(),
        rule_parameters={
                "collection_names": "weights",
                "max_threshold": "10",
                "min_threshold": "0.00001",
        },
        collections_to_save=[ 
            CollectionConfig(
                name="weights", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

UnchangedTensor

This rule detects whether a tensor is no longer changing across steps.

This rule runs the numpy.allclose method to check if the tensor isn't changing.

This rule can be applied either to one of the supported deep learning frameworks (TensorFlow, MXNet, and PyTorch) or to the XGBoost algorithm. You must specify either the collection_names or tensor_regex parameter. If both the parameters are specified, the rule inspects the union of tensors from both sets.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the UnchangedTensor Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
collection_names	The list of collection names whose tensors the rule inspects. Optional Valid values: List of strings or a comma-separated string Default value: None
tensor_regex	A list of regex patternsused to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: None
num_steps	The number of steps across which the rule checks to determine if the tensor has changed. This checks the last num_steps that are available. They don't need to be consecutive. If num_steps is 2, at step s it doesn't necessarily check for s-1 and s. If s-1 isn't available, it checks the last available step along with s. In that case, it checks the last available step with the current step. Optional Valid values: Integer Default value: 3
rtol	The relative tolerance parameter to be passed to the numpy.allclose method. Optional Valid values: Float Default value: 1e-05
atol	The absolute tolerance parameter to be passed to the numpy.allclose method. Optional Valid values: Float Default value: 1e-08
equal_nan	Whether to compare NaNs as equal. If True, NaNs in input array a are considered equal to NaNs in input array b in the output array. This parameter is passed to the numpy.allclose method. Optional Valid values: Boolean Default value: False

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.unchanged_tensor(),
        rule_parameters={
                "collection_names": "losses",
                "tensor_regex": "",
                "num_steps": "3",
                "rtol": "1e-05",
                "atol": "1e-08",
                "equal_nan": "False"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="losses", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

CheckInputImages

This rule checks if input images have been correctly normalized. Specifically, it detects if the mean of the sample data differs by more than a threshold value from zero. Many computer vision models require that input data has a zero mean and unit variance.

This rule is applicable to deep learning applications.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the CheckInputImages Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold_mean	A threshold that defines by how much mean of the input data can differ from 0. Optional Valid values: Float Default value: 0.2
threshold_samples	The number of images that have to be sampled before an error can be thrown. If the value is too low, the estimation of the dataset mean will be inaccurate. Optional Valid values: Integer Default value: 500
regex	The name of the input data tensor. Optional Valid values: String Default value: ".*hybridsequential0_input_0" (the name of the input tensor for Apache MXNet models using HybridSequential)
channel	The position of the color channel in the input tensor shape array. Optional Valid values: Integer Default value: 1 (for example, MXNet expects input data in the form of (batch_size, channel, height, width))

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.check_input_images(),
        rule_parameters={
                "threshold_mean": "0.2",
                "threshold_samples": "500",
                "regex": ".*hybridsequential0_input_0",
                "channel": "1"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="custom_inputs_collection", 
                parameters={
                    "include_regex": ".*hybridsequential0_input_0",
                    "save_interval": "500"
                } 
            )
        ]
    )
]

NLPSequenceRatio

This rule calculates the ratio of specific tokens given the rest of the input sequence that is useful for optimizing performance. For example, you can calculate the percentage of padding end-of-sentence (EOS) tokens in your input sequence. If the number of EOS tokens is too high, an alternate bucketing strategy should be performed. You also can calculate the percentage of unknown tokens in your input sequence. If the number of unknown words is too high, an alternate vocabulary could be used.

This rule is applicable to deep learning applications.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the NLPSequenceRatio Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
tensor_regex	A list of regex patterns used to restrict this comparison to specific scalar-valued tensors. The rule inspects only the tensors that match the regex patterns specified in the list. If no patterns are passed, the rule compares all tensors gathered in the trials by default. Only scalar-valued tensors can be matched. Optional Valid values: List of strings or a comma-separated string Default value: ".*embedding0_input_0" (assuming an embedding as the initial layer of the network)
token_values	A string of a list of the numerical values of the tokens. For example, "3, 0". Optional Valid values: Comma-separated string of numerical values Default value: 0
token_thresholds_percent	A string of a list of thresholds (in percentages) that correspond to each of the token_values. For example,"50.0, 50.0". Optional Valid values: Comma-separated string of floats Default value: "50"

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.nlp_sequence_ratio(),
        rule_parameters={
                "tensor_regex": ".*embedding0_input_0",
                "token_values": "0",
                "token_thresholds_percent": "50"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="custom_inputs_collection", 
                parameters={
                    "include_regex": ".*embedding0_input_0"
                } 
            )
        ]
    )
]

Confusion

This rule evaluates the goodness of a confusion matrix for a classification problem.

It creates a matrix of size category_no*category_no and populates it with data coming from (labels, predictions) pairs. For each (labels, predictions) pair, the count in confusion[labels][predictions] is incremented by 1. When the matrix is fully populated, the ratio of data on-diagonal values and off-diagonal values are evaluated as follows:

• For elements on the diagonal: confusion[i][i]/sum_j(confusion[j][j])>=min_diag

• For elements off the diagonal: confusion[j][i])/sum_j(confusion[j][i])<=max_off_diag

This rule can be applied to the XGBoost algorithm.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the Confusion Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
category_no	The number of categories. Optional Valid values: Integer ≥2 Default value: "None"
labels	The labels tensor collection or an 1-d vector of true labels. Optional Valid values: String Default value: "labels"
predictions	The predictions tensor collection or an 1-d vector of estimated labels. Optional Valid values: String Default value: "predictions"
labels_collection	The rule inspects the tensors in this collection for labels. Optional Valid values: String Default value: "labels"
predictions_collection	The rule inspects the tensors in this collection for predictions. Optional Valid values: String Default value: "predictions"
min_diag	The minimum threshold for the ratio of data on the diagonal. Optional Valid values: 0≤float≤1 Default value: 0.9
max_off_diag	The maximum threshold for the ratio of data off the diagonal. Optional Valid values: 0≤float≤1 Default value: 0.1

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.confusion(),
        rule_parameters={
                "category_no": "10",
                "labels": "labels",
                "predictions": "predictions",
                "labels_collection": "labels",
                "predictions_collection": "predictions",
                "min_diag": "0.9",
                "max_off_diag": "0.1"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="labels",
                parameters={
                    "save_interval": "500"
                } 
            ),
            CollectionConfig(
                name="predictions",
                parameters={
                    "include_regex": "500"
                } 
            )
        ]
    )
]

Note

This rule infers default values for the optional parameters if their values aren't specified.

FeatureImportanceOverweight

This rule accumulates the weights of the n largest feature importance values per step and ensures that they do not exceed the threshold. For example, you can set the threshold for the top 3 features to not hold more than 80 percent of the total weights of the model.

This rule is valid only for the XGBoost algorithm.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the FeatureImportanceOverweight Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
threshold	Defines the threshold for the proportion of the cumulative sum of the n largest features. The number n is defined by the nfeatures parameter. Optional Valid values: Float Default value: 0.8
nfeatures	The number of largest features. Optional Valid values: Integer Default value: 3
tensor_regex	Regular expression (regex) of tensor names the rule to analyze. Optional Valid values: String Default value: ".*feature_importance/weight"

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.feature_importance_overweight(),
        rule_parameters={
                "threshold": "0.8",
                "nfeatures": "3",
                "tensor_regex": ".*feature_importance/weight"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="feature_importance", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]

TreeDepth

This rule measures the depth of trees in an XGBoost model. XGBoost rejects splits if they do not improve loss. This regularizes the training. As a result, the tree might not grow as deep as defined by the depth parameter.

This rule is valid only for the XGBoost algorithm.

For an example of how to configure and deploy a built-in rule, see Configure Debugger Built-in Rules.

Parameter Descriptions for the TreeDepth Rule
Parameter Name	Description
base_trial	The base trial training job name. This parameter is automatically set to the current training job by Amazon SageMaker Debugger. Required Valid values: String
depth	The depth of the tree. The depth of the tree is obtained by computing the base 2 logarithm of the largest node ID. Optional Valid values: Float Default value: 4

built_in_rules = [
    Rule.sagemaker(
        base_config=rule_configs.tree_depth(),
        rule_parameters={
                "depth": "4"
        },
        collections_to_save=[ 
            CollectionConfig(
                name="tree", 
                parameters={
                    "save_interval": "500"
                } 
            )
        ]
    )
]