# Analyze data using the Debugger Python client library
<a name="debugger-analyze-data"></a>

**Note**  
End of support notice: On June 30, 2027, AWS will end support for Amazon SageMaker Profiler. After June 30, 2027, you will no longer be able to access the Profiler console or Profiler resources. For more information, see [Profiler availability change](profiler-availability-change.md).

While your training job is running or after it has completed, you can access the training data collected by Debugger using the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) and the [SMDebug client library](https://github.com/awslabs/sagemaker-debugger/). The Debugger Python client library provides analysis and visualization tools that enable you to drill down into your training job data.

**To install the library and use its analysis tools (in a JupyterLab notebook or an iPython kernel)**

```
! pip install -U smdebug
```

The following topics walk you through how to use the Debugger Python tools to visualize and analyze the training data collected by Debugger.

**Analyze system and framework metrics**
+ [Access the profile data](debugger-analyze-data-profiling.md)
+ [Plot the system metrics and framework metrics data](debugger-access-data-profiling-default-plot.md)
+ [Access the profiling data using the pandas data parsing tool](debugger-access-data-profiling-pandas-frame.md)
+ [Access the Python profiling stats data](debugger-access-data-python-profiling.md)
+ [Merge timelines of multiple profile trace files](debugger-merge-timeline.md)
+ [Profiling data loaders](debugger-data-loading-time.md)