Step 4: Train a Model

To run a model training job

1. Import the Amazon SageMaker Python SDK and start by retrieving the basic information from your current SageMaker session.

   import sagemaker
   
   region = sagemaker.Session().boto_region_name
   print("Amazon Region: {}".format(region))
   
   role = sagemaker.get_execution_role()
   print("RoleArn: {}".format(role))

   This returns the following information:

   • region – The current Amazon Region where the SageMaker notebook instance is running.

   • role – The IAM role used by the notebook instance.

   Note

   Check the SageMaker Python SDK version by running sagemaker.__version__. This tutorial is based on sagemaker>=2.20. If the SDK is outdated, install the latest version by running the following command:

   ! pip install -qU sagemaker

   If you run this installation in your exiting SageMaker Studio or notebook instances, you need to manually refresh the kernel to finish applying the version update.

2. Create an XGBoost estimator using the sagemaker.estimator.Estimator class. In the following example code, the XGBoost estimator is named xgb_model.

   from sagemaker.debugger import Rule, ProfilerRule, rule_configs
   from sagemaker.session import TrainingInput
   
   s3_output_location='s3://{}/{}/{}'.format(bucket, prefix, 'xgboost_model')
   
   container=sagemaker.image_uris.retrieve("xgboost", region, "1.2-1")
   print(container)
   
   xgb_model=sagemaker.estimator.Estimator(
       image_uri=container,
       role=role,
       instance_count=1,
       instance_type='ml.m4.xlarge',
       volume_size=5,
       output_path=s3_output_location,
       sagemaker_session=sagemaker.Session(),
       rules=[
           Rule.sagemaker(rule_configs.create_xgboost_report()),
           ProfilerRule.sagemaker(rule_configs.ProfilerReport())
       ]
   )

   To construct the SageMaker estimator, specify the following parameters:

   • image_uri – Specify the training container image URI. In this example, the SageMaker XGBoost training container URI is specified using sagemaker.image_uris.retrieve.

   • role – The Amazon Identity and Access Management (IAM) role that SageMaker uses to perform tasks on your behalf (for example, reading training results, call model artifacts from Amazon S3, and writing training results to Amazon S3).

   • instance_count and instance_type – The type and number of Amazon EC2 ML compute instances to use for model training. For this training exercise, you use a single ml.m4.xlarge instance, which has 4 CPUs, 16 GB of memory, an Amazon Elastic Block Store (Amazon EBS) storage, and a high network performance. For more information about EC2 compute instance types, see Amazon EC2 Instance Types. For more information about billing, see Amazon SageMaker pricing.

   • volume_size – The size, in GB, of the EBS storage volume to attach to the training instance. This must be large enough to store training data if you use File mode (File mode is on by default). If you don't specify this parameter, its value defaults to 30.

   • output_path – The path to the S3 bucket where SageMaker stores the model artifact and training results.

   • sagemaker_session – The session object that manages interactions with SageMaker API operations and other Amazon service that the training job uses.

   • rules – Specify a list of SageMaker Debugger built-in rules. In this example, the create_xgboost_report() rule creates an XGBoost report that provides insights into the training progress and results, and the ProfilerReport() rule creates a report regarding the EC2 compute resource utilization. For more information, see SageMaker Debugger XGBoost Training Report.

   Tip

   If you want to run distributed training of large sized deep learning models, such as convolutional neural networks (CNN) and natural language processing (NLP) models, use SageMaker Distributed for data parallelism or model parallelism. For more information, see Distributed training in Amazon SageMaker.

3. Set the hyperparameters for the XGBoost algorithm by calling the set_hyperparameters method of the estimator. For a complete list of XGBoost hyperparameters, see XGBoost Hyperparameters.

   xgb_model.set_hyperparameters(
       max_depth = 5,
       eta = 0.2,
       gamma = 4,
       min_child_weight = 6,
       subsample = 0.7,
       objective = "binary:logistic",
       num_round = 1000
   )

   Tip

   You can also tune the hyperparameters using the SageMaker hyperparameter optimization feature. For more information, see Perform Automatic Model Tuning with SageMaker.

4. Use the TrainingInput class to configure a data input flow for training. The following example code shows how to configure TrainingInput objects to use the training and validation datasets you uploaded to Amazon S3 in the Split the Dataset into Train, Validation, and Test Datasets section.

   from sagemaker.session import TrainingInput
   
   train_input = TrainingInput(
       "s3://{}/{}/{}".format(bucket, prefix, "data/train.csv"), content_type="csv"
   )
   validation_input = TrainingInput(
       "s3://{}/{}/{}".format(bucket, prefix, "data/validation.csv"), content_type="csv"
   )

5. To start model training, call the estimator's fit method with the training and validation datasets. By setting wait=True, the fit method displays progress logs and waits until training is complete.

   xgb_model.fit({"train": train_input, "validation": validation_input}, wait=True)

   For more information about model training, see Train a Model with Amazon SageMaker. This tutorial training job might take up to 10 minutes.

   After the training job has done, you can download an XGBoost training report and a profiling report generated by SageMaker Debugger. The XGBoost training report offers you insights into the training progress and results, such as the loss function with respect to iteration, feature importance, confusion matrix, accuracy curves, and other statistical results of training. For example, you can find the following loss curve from the XGBoost training report which clearly indicates that there is an overfitting problem.

   

   Run the following code to specify the S3 bucket URI where the Debugger training reports are generated and check if the reports exist.

   rule_output_path = xgb_model.output_path + "/" + xgb_model.latest_training_job.job_name + "/rule-output"
   ! aws s3 ls {rule_output_path} --recursive

   Download the Debugger XGBoost training and profiling reports to the current workspace:

   ! aws s3 cp {rule_output_path} ./ --recursive

   Run the following IPython script to get the file link of the XGBoost training report:

   from IPython.display import FileLink, FileLinks
   display("Click link below to view the XGBoost Training report", FileLink("CreateXgboostReport/xgboost_report.html"))

   The following IPython script returns the file link of the Debugger profiling report that shows summaries and details of the EC2 instance resource utilization, system bottleneck detection results, and python operation profiling results:

   profiler_report_name = [rule["RuleConfigurationName"] 
                           for rule in xgb_model.latest_training_job.rule_job_summary() 
                           if "Profiler" in rule["RuleConfigurationName"]][0]
   profiler_report_name
   display("Click link below to view the profiler report", FileLink(profiler_report_name+"/profiler-output/profiler-report.html"))

   Tip

   If the HTML reports do not render plots in the JupyterLab view, you must choose Trust HTML at the top of the reports.

   To identify training issues, such as overfitting, vanishing gradients, and other problems that prevents your model from converging, use SageMaker Debugger and take automated actions while prototyping and training your ML models. For more information, see Use Amazon SageMaker Debugger to debug and improve model performance. To find a complete analysis of model parameters, see the Explainability with Amazon SageMaker Debugger example notebook.