# Download the SageMaker Debugger profiling report
Download the SageMaker Debugger profiling report while your training job is running or after the job has finished using the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) and AWS Command Line Interface (CLI).
**Note**
To get the profiling report generated by SageMaker Debugger, you must use the built-in [ProfilerReport](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html#profiler-report) rule offered by SageMaker Debugger. To activate the rule with your training job, see [Configure Built-in Profiler Rules](https://docs.aws.amazon.com/sagemaker/latest/dg/use-debugger-built-in-profiler-rules.html).
**Tip**
You can also download the report with a single click in the SageMaker Studio Debugger insights dashboard. This doesn't require any additional scripting to download the report. To find out how to download the report from Studio, see [Open the Amazon SageMaker Debugger Insights dashboard](debugger-on-studio-insights.md).
------
#### [ Download using SageMaker Python SDK and AWS CLI ]
1. Check the current job's default S3 output base URI.
```
estimator.output_path
```
1. Check the current job name.
```
estimator.latest_training_job.job_name
```
1. The Debugger profiling report is stored under `//rule-output`. Configure the rule output path as follows:
```
rule_output_path = estimator.output_path + estimator.latest_training_job.job_name + "/rule-output"
```
1. 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 an autogenerated folder that's named as `ProfilerReport-1234567890`. The folder name is a combination of strings: `ProfilerReport` and a unique 10-digit tag based on the Unix timestamp when the ProfilerReport rule is initiated.
![\[An example of rule output\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/debugger-rule-output-ls-example.png)
The `profiler-report.html` is an autogenerated profiling report by Debugger. The remaining files are the built-in rule analysis components stored in JSON and a Jupyter notebook that are used to aggregate them into the report.
1. 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 using a Jupyter notebook server, run `!pwd` to double check the current working directory.
1. Under the `/ProfilerReport-1234567890/profiler-output` directory, open `profiler-report.html`. If using JupyterLab, choose **Trust HTML** to see the autogenerated Debugger profiling report.
![\[An example of rule output\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/debugger-rule-output-open-html.png)
1. Open the `profiler-report.ipynb` file to explore how the report is generated. You can also customize and extend the profiling report using the Jupyter notebook file.
------
#### [ Download using Amazon S3 Console ]
1. Sign in to the AWS Management Console and open the Amazon S3 console at [https://console.aws.amazon.com/s3/](https://console.aws.amazon.com/s3/).
1. 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--111122223333`. Look up the base S3 bucket through the *Find bucket by name* field.
![\[An example to the rule output S3 bucket URI\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/debugger-report-download-s3console-0.png)
1. In the base S3 bucket, look up the training job name by specifying your job name prefix into the *Find objects by prefix* input field. Choose the training job name.
![\[An example to the rule output S3 bucket URI\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/debugger-report-download-s3console-1.png)
1. In the training job's S3 bucket, there must be three subfolders for training data collected by Debugger: **debug-output/**, **profiler-output/**, and **rule-output/**. Choose **rule-output/**.
![\[An example to the rule output S3 bucket URI\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/debugger-report-download-s3console-2.png)
1. In the **rule-output/** folder, choose **ProfilerReport-1234567890**, and choose **profiler-output/** folder. The **profiler-output/** folder contains **profiler-report.html** (the autogenerated profiling report in html), **profiler-report.ipynb** (a Jupyter notebook with scripts that are used for generating the report), and a **profiler-report/** folder (contains rule analysis JSON files that are used as components of the report).
1. Select the **profiler-report.html** file, choose **Actions**, and **Download**.
![\[An example to the rule output S3 bucket URI\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/debugger/debugger-report-download-s3console-3.png)
1. Open the downloaded **profiler-report.html** file in a web browser.
------
**Note**
If you started your training job without configuring the Debugger-specific parameters, Debugger generates the report based only on the system monitoring rules because the Debugger parameters are not configured to save framework metrics. To enable framework metrics profiling and receive an extended Debugger profiling report, configure the `profiler_config` parameter when constructing or updating SageMaker AI estimators.
To learn how to configure the `profiler_config` parameter before starting a training job, see [Estimator configuration for framework profiling](debugger-configure-framework-profiling.md).
To update the current training job and enable framework metrics profiling, see [Update Debugger Framework Profiling Configuration](debugger-update-monitoring-profiling.md).