# Estimator configuration with parameters for basic profiling using the Amazon SageMaker Debugger Python modules By default, SageMaker Debugger basic profiling is on by default and monitors resource utilization metrics, such as CPU utilization, GPU utilization, GPU memory utilization, Network, and I/O wait time, of all SageMaker training jobs submitted using the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). SageMaker Debugger collects these resource utilization metrics every 500 milliseconds. You don't need to make any additional changes in your code, training script, or the job launcher for tracking basic resource utilization. If you want to change the metric collection interval for basic profiling, you can specify Debugger-specific parameters while creating a SageMaker training job launcher using the SageMaker Python SDK, AWS SDK for Python (Boto3), or AWS Command Line Interface (CLI). In this guide, we focus on how to change profiling options using the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). This page gives reference templates for configuring this estimator object. If you want to access the resource utilization metrics dashboard of your training job in SageMaker Studio, you can jump onto the [Amazon SageMaker Debugger UI in Amazon SageMaker Studio Classic Experiments](debugger-on-studio.md). If you want to activate the rules that detect system resource utilization problems automatically, you can add the `rules` parameter in the estimator object for activating the rules. **Important** To use the latest SageMaker 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) ``` ## Code template for configuring a SageMaker AI estimator object with the SageMaker Debugger Python modules in the SageMaker AI Python SDK To adjust the basic profiling configuration (`profiler_config`) or add the profiler rules (`rules`), choose one of the tabs to get the template for setting up a SageMaker AI estimator. In the subsequent pages, you can find more information about how to configure the two parameters. **Note** The following code examples are not directly executable. Proceed to the next sections to learn how to configure each parameter. ------ #### [ PyTorch ] ``` # An example of constructing a SageMaker AI PyTorch estimator import boto3 import sagemaker from sagemaker.pytorch import PyTorch from sagemaker.debugger import ProfilerConfig, ProfilerRule, rule_configs session=boto3.session.Session() region=session.region_name profiler_config=ProfilerConfig(...) rules=[ ProfilerRule.sagemaker(rule_configs.BuiltInRule()) ] estimator=PyTorch( entry_point="directory/to/your_training_script.py", role=sagemaker.get_execution_role(), base_job_name="debugger-profiling-demo", instance_count=1, instance_type="ml.p3.2xlarge", framework_version="1.12.0", py_version="py37", # SageMaker Debugger parameters profiler_config=profiler_config, rules=rules ) estimator.fit(wait=False) ``` ------ #### [ TensorFlow ] ``` # An example of constructing a SageMaker AI TensorFlow estimator import boto3 import sagemaker from sagemaker.tensorflow import TensorFlow from sagemaker.debugger import ProfilerConfig, ProfilerRule, rule_configs session=boto3.session.Session() region=session.region_name profiler_config=ProfilerConfig(...) rules=[ ProfilerRule.sagemaker(rule_configs.BuiltInRule()) ] estimator=TensorFlow( entry_point="directory/to/your_training_script.py", role=sagemaker.get_execution_role(), base_job_name="debugger-profiling-demo", instance_count=1, instance_type="ml.p3.2xlarge", framework_version="2.8.0", py_version="py37", # SageMaker Debugger parameters profiler_config=profiler_config, rules=rules ) estimator.fit(wait=False) ``` ------ #### [ MXNet ] ``` # An example of constructing a SageMaker AI MXNet estimator import sagemaker from sagemaker.mxnet import MXNet from sagemaker.debugger import ProfilerConfig, ProfilerRule, rule_configs profiler_config=ProfilerConfig(...) rules=[ ProfilerRule.sagemaker(rule_configs.BuiltInRule()) ] estimator=MXNet( entry_point="directory/to/your_training_script.py", role=sagemaker.get_execution_role(), base_job_name="debugger-profiling-demo", instance_count=1, instance_type="ml.p3.2xlarge", framework_version="1.7.0", py_version="py37", # SageMaker Debugger parameters profiler_config=profiler_config, rules=rules ) estimator.fit(wait=False) ``` **Note** For MXNet, when configuring the `profiler_config` parameter, you can only configure for system monitoring. Profiling framework metrics is not supported for MXNet. ------ #### [ XGBoost ] ``` # An example of constructing a SageMaker AI XGBoost estimator import sagemaker from sagemaker.xgboost.estimator import XGBoost from sagemaker.debugger import ProfilerConfig, ProfilerRule, rule_configs profiler_config=ProfilerConfig(...) rules=[ ProfilerRule.sagemaker(rule_configs.BuiltInRule()) ] estimator=XGBoost( entry_point="directory/to/your_training_script.py", role=sagemaker.get_execution_role(), base_job_name="debugger-profiling-demo", instance_count=1, instance_type="ml.p3.2xlarge", framework_version="1.5-1", # Debugger-specific parameters profiler_config=profiler_config, rules=rules ) estimator.fit(wait=False) ``` **Note** For XGBoost, when configuring the `profiler_config` parameter, you can only configure for system monitoring. Profiling framework metrics is not supported for XGBoost. ------ #### [ Generic estimator ] ``` # An example of constructing a SageMaker AI generic estimator using the XGBoost algorithm base image import boto3 import sagemaker from sagemaker.estimator import Estimator from sagemaker import image_uris from sagemaker.debugger import ProfilerConfig, DebuggerHookConfig, Rule, ProfilerRule, rule_configs profiler_config=ProfilerConfig(...) rules=[ ProfilerRule.sagemaker(rule_configs.BuiltInRule()) ] region=boto3.Session().region_name xgboost_container=sagemaker.image_uris.retrieve("xgboost", region, "1.5-1") estimator=Estimator( role=sagemaker.get_execution_role() image_uri=xgboost_container, base_job_name="debugger-demo", instance_count=1, instance_type="ml.m5.2xlarge", # Debugger-specific parameters profiler_config=profiler_config, rules=rules ) estimator.fit(wait=False) ``` ------ The following provides brief descriptions of the parameters. + `profiler_config` – Configure Debugger to collect system metrics and framework metrics from your training job and save into your secured S3 bucket URI or local machine. You can set how frequently or loosely collect the system metrics. To learn how to configure the `profiler_config` parameter, see [Configure settings for basic profiling of system resource utilization](debugger-configure-system-monitoring.md) and [Estimator configuration for framework profiling](debugger-configure-framework-profiling.md). + `rules` – Configure this parameter to activate SageMaker Debugger built-in rules that you want to run in parallel. Make sure that your training job has access to this S3 bucket. The rules runs on processing containers and automatically analyze your training job to find computational and operational performance issues. The [ProfilerReport](debugger-built-in-profiler-rules.md#profiler-report) rule is the most integrated rule that runs all built-in profiling rules and saves the profiling results as a report into your secured S3 bucket. To learn how to configure the `rules` parameter, see [Use built-in profiler rules managed by Amazon SageMaker Debugger](use-debugger-built-in-profiler-rules.md). **Note** Debugger securely saves output data in subfolders of your default S3 bucket. For example, the format of the default S3 bucket URI is `s3://sagemaker--<12digit_account_id>///`. There are three subfolders created by Debugger: `debug-output`, `profiler-output`, and `rule-output`. You can also retrieve the default S3 bucket URIs using the [SageMaker AI estimator classmethods](debugger-estimator-classmethods.md). See the following topics to find out how to configure the Debugger-specific parameters in detail. **Topics** + [Code template for configuring a SageMaker AI estimator object with the SageMaker Debugger Python modules in the SageMaker AI Python SDK](#debugger-configuration-structure-profiler) + [Configure settings for basic profiling of system resource utilization](debugger-configure-system-monitoring.md) + [Estimator configuration for framework profiling](debugger-configure-framework-profiling.md) + [Updating Debugger system monitoring and framework profiling configuration while a training job is running](debugger-update-monitoring-profiling.md) + [Turn off Debugger](debugger-turn-off-profiling.md)