Example notebooks and code samples to configure Debugger rules

In the following sections, notebooks and code samples of how to use Debugger rules to monitor SageMaker training jobs are provided.

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 AI:

• 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 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 AI 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.

Debugger built-in rules example code

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 AI 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',
    instance_count=1,
    instance_type="ml.p3.2xlarge",
    framework_version="2.9.0",
    py_version="py39",

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

Note

The Debugger built-in rules run in parallel with your training job. The maximum number of built-in rule containers for a training job is 20.

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

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 AI 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,
    instance_count=1,
    instance_type="ml.p3.2xlarge",
    framework_version="2.9.0",
    py_version="py39",

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