Use Debugger Built-in Rules for Training Job Analysis

Amazon SageMaker Debugger rules analyze tensors emitted during the training of a model. Debugger offers the Rule API operation that monitors training job progress and errors for the success of training your model. For example, the rules can detect whether gradients are getting too large or too small, whether a model is overfitting or overtraining, and whether a training job does not decrease loss function and improve. Debugger Rules provides more than a dozen of convenient built-in rules that you can directly apply to your training jobs.

Note

The Debugger built-in rules are prepared in Amazon SageMaker containers. A full list of available pre-built docker images for the built-in rules is provided at Amazon SageMaker Debugger Registry URLs for Built-in Rule Evaluators. If you use the pre-built AWS containers, the step where you construct SageMaker estimators automatically calls the rule images and you do not need to manually specify it. You can also use all of the built-in rules without any additional cost when using the AWS managed containers.

Debugger Built-in Rules Example Notebooks

The following example notebooks show how to use Debugger built-in rules when running training jobs with Amazon SageMaker:

• Using a SageMaker Debugger built-in rule with TensorFlow

• Using a SageMaker Debugger built-in rule with Managed Spot Training and MXNet

• Using a SageMaker Debugger built-in rule with XGBoost

• Using a SageMaker Debugger built-in rule with parameter modifications for a real-time training job analysis with XGBoost

While running the example notebooks in SageMaker Studio, you can find the training job trial created on the Studio Experiment List tab. For example, as shown in the following screenshot, you can find and open a Describe Trial Component window of your current training job. On the Debugger tab, you can check if the Debugger rules, vanishing_gradient() and loss_not_decreasing(), are monitoring the training session in parallel. For a full instruction of how to find your training job trial components in the Studio UI, see SageMaker Studio - View Experiments, Trials, and Trial Components.

There are two ways of using the Debugger built-in rules in the SageMaker environment: deploy the built-in rules as it is prepared or adjust their parameters as you want. The following topics show you how to use the built-in rules with example codes.

Use Debugger Built-in Rules

The following code sample shows how to set the Debugger built-in rules using the Rule.sagemaker method. To specify built-in rules that you want to run, use the rules_configs API operation to call the built-in rules. To find a full list of Debugger built-in rules and default parameter values, see List of Debugger Built-in Rules.

import sagemaker
from sagemaker.tensorflow import TensorFlow
from sagemaker.debugger import Rule, CollectionConfig, rule_configs

# call built-in rules that you want to use.
built_in_rules = [ 
            Rule.sagemaker(rule_configs.vanishing_gradient())
            Rule.sagemaker(rule_configs.loss_not_decreasing())
]

# construct a SageMaker estimator with the Debugger built-in rules
sagemaker_estimator = TensorFlow(
    entry_point='directory/to/your_training_script.py',
    role=sm.get_execution_role(),
    base_job_name='debugger-built-in-rules-demo',
    train_instance_count=1,
    train_instance_type="ml.m4.xlarge",
    framework_version="2.1.0",
    py_version="py3",

    # debugger-specific arguments below
    rules=built_in_rules
)
sagemaker_estimator.fit()

Note

The Debugger built-in rules run in parallel on ml.t3.medium instances. The maximum number of built-in rule containers for a training job is 20. You cannot specify other instance types for the Debugger built-in rules.

For more information about the Debugger rule class, methods, and parameters, see the SageMaker Debugger Rule class in the Amazon SageMaker Python SDK.

To find an example of how to adjust the Debugger rule parameters, see the following Use Debugger Built-in Rules with Parameter Modifications section.

Use Debugger Built-in Rules with Parameter Modifications

If you want to modify the built-in rules to adjust parameter values, collect specific tensors, change the tensor save intervals, set start and end steps, and monitor different stages of training jobs, you need to specify base_config, rule_parameters, and collections_to_save arguments for the Rule.sagemaker method.

• The base_config argument is where you call the built-in rule methods.

• The rule_parameters argument is to adjust the default key values of the built-in rules listed in List of Debugger Built-in Rules.

• The collections_to_save argument takes in a tensor configuration through the CollectionConfig API, which requires name and parameters arguments.

  • To find available tensor collections for name, see Debugger Built-in Tensor Collections .

  • For a full list of adjustable parameters, see Debugger CollectionConfig API.

For more information about the Debugger rule class, methods, and parameters, see SageMaker Debugger Rule class in the Amazon SageMaker Python SDK.

The following code example shows the structure of built-in rules to adjust parameters. In this example, the stalled_training_rule collects the losses tensor collection from a training job at every 50 steps and an evaluation stage at every 10 steps. If the training process starts stalling and not collecting tensor outputs for 120 seconds, the stalled_training_rule stops the training job.

import sagemaker
from sagemaker.tensorflow import TensorFlow
from sagemaker.debugger import Rule, CollectionConfig, rule_configs

# call the built-in rules and modify the CollectionConfig parameters

base_job_name_prefix= 'smdebug-stalled-demo-' + str(int(time.time()))

built_in_rules_modified = [
    Rule.sagemaker(
        base_config=rule_configs.stalled_training_rule(),
        rule_parameters={
                'threshold': '120',
                'training_job_name_prefix': base_job_name_prefix,
                'stop_training_on_fire' : 'True'
        }
        collections_to_save=[ 
            CollectionConfig(
                name="losses", 
                parameters={
                      "train.save_interval": "50"
                      "eval.save_interval": "10"
                } 
            )
        ]
    )
]

# construct a SageMaker estimator with the modified Debugger built-in rule
sagemaker_estimator = TensorFlow(
    entry_point='directory/to/your_training_script.py',
    role=sm.get_execution_role(),
    base_job_name=base_job_name_prefix,
    train_instance_count=1,
    train_instance_type="ml.m4.xlarge",
    framework_version="2.1.0",
    py_version="py3",

    # debugger-specific arguments below
    rules=built_in_rules_modified
)
sagemaker_estimator.fit()

For an advanced configuration of the Debugger built-in rules using the CreateTrainingJob API, see Add Debugger Built-in Rule Configuration to the CreateTrainingJob API Operation.