Download the Debugger XGBoost training report - Amazon SageMaker AI

Download the Debugger XGBoost training report

Download the Debugger XGBoost training report while your training job is running or after the job has finished using the Amazon SageMaker Python SDK and AWS Command Line Interface (CLI).

Download using the SageMaker Python SDK and AWS CLI

  1. Check the current job's default S3 output base URI.

    estimator.output_path

  2. Check the current job name.

    estimator.latest_training_job.job_name

  3. The Debugger XGBoost report is stored under <default-s3-output-base-uri>/<training-job-name>/rule-output. Configure the rule output path as follows:

    rule_output_path = estimator.output_path + "/" + estimator.latest_training_job.job_name + "/rule-output"

  4. To check if the report is generated, list directories and files recursively under the rule_output_path using aws s3 ls with the --recursive option.

    ! aws s3 ls {rule_output_path} --recursive

    This should return a complete list of files under autogenerated folders that are named CreateXgboostReport and ProfilerReport-1234567890. The XGBoost training report is stored in the CreateXgboostReport, and the profiling report is stored in the ProfilerReport-1234567890 folder. To learn more about the profiling report generated by default with the XGBoost training job, see SageMaker Debugger interactive report.

    An example of rule output.

    The xgboost_report.html is an autogenerated XGBoost training report by Debugger. The xgboost_report.ipynb is a Jupyter notebook that's used to aggregate training results into the report. You can download all of the files, browse the HTML report file, and modify the report using the notebook.

  5. Download the files recursively using aws s3 cp. The following command saves all of the rule output files to the ProfilerReport-1234567890 folder under the current working directory.

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

    If you are using a Jupyter notebook server, run !pwd to verify the current working directory.

  6. Under the /CreateXgboostReport directory, open xgboost_report.html. If you are using JupyterLab, choose Trust HTML to see the autogenerated Debugger training report.

    An example of rule output.

  7. Open the xgboost_report.ipynb file to explore how the report is generated. You can customize and extend the training report using the Jupyter notebook file.

Download using the Amazon S3 console

  1. Sign in to the AWS Management Console and open the Amazon S3 console at https://console.aws.amazon.com/s3/.

  2. Search for the base S3 bucket. For example, if you haven't specified any base job name, the base S3 bucket name should be in the following format: sagemaker-<region>-111122223333. Look up the base S3 bucket through the Find bucket by name field.

    The Find bucket by name field in the Amazon S3 console.

  3. In the base S3 bucket, look up the training job name by entering your job name prefix in Find objects by prefix and then choosing the training job name.

    The Find objects by prefix field in the Amazon S3 console.

  4. In the training job's S3 bucket, choose rule-output/ subfolder. There must be three subfolders for training data collected by Debugger: debug-output/, profiler-output/, and rule-output/.

    An example to the rule output S3 bucket URI.

  5. In the rule-output/ folder, choose the CreateXgboostReport/ folder. The folder contains xbgoost_report.html (the autogenerated report in html) and xbgoost_report.ipynb (a Jupyter notebook with scripts that are used for generating the report).

  6. Choose the xbgoost_report.html file, choose Download actions, and then choose Download.

    An example to the rule output S3 bucket URI.

  7. Open the downloaded xbgoost_report.html file in a web browser.