# 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.
**Topics**
+ [Debugger built-in rules example notebooks](#debugger-built-in-rules-notebook-example)
+ [Debugger built-in rules example code](#debugger-deploy-built-in-rules)
+ [Use Debugger built-in rules with parameter modifications](#debugger-deploy-modified-built-in-rules)
## 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](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-debugger/tensorflow_builtin_rule)
+ [Using a SageMaker Debugger built-in rule with Managed Spot Training and MXNet](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-debugger/mxnet_spot_training)
+ [Using a SageMaker Debugger built-in rule with parameter modifications for a real-time training job analysis with XGBoost](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-debugger/xgboost_realtime_analysis)
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](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-tasks.html#studio-tasks-experiments).
![\[An image of running a training job with Debugger built-in rules activated in SageMaker Studio\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/debugger-built-in-rule-studio.png)
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](debugger-built-in-rules.md).
```
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](https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html) in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable).
To find an example of how to adjust the Debugger rule parameters, see the following [Use Debugger built-in rules with parameter modifications](#debugger-deploy-modified-built-in-rules) 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()
```