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

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)