# Welcome to SDGym!

The **Synthetic Data Gym** (SDGym) is a Python library for benchmarking different synthetic data generators. For example you can compare synthesizers that use classical statistics versus those that use deep learning.

<figure><img src="/files/92gDlYNQucrt9srwaxAL" alt=""><figcaption></figcaption></figure>

## Why benchmarking?

The past few years have seen an increasing amount of research in the synthetic data space. With such a variety of models available, we feel that it's important to validate new techniques with a proper framework in place.

We built SDGym as flexible and customizable framework that you can use for a thorough analysis of synthetic data. This will give you the confidence when publishing your results and incorporating synthetic data into your projects.

### 🧮 Use multiple datasets for a reliable benchmark

If your goal is to test a robust synthetic data model, relying on a single dataset may not give you much confidence. The SDGym library comes with a **variety of publicly available datasets** that are immediately for use.

This is helpful for detecting errors in your synthesizer as well as identifying its strengths and weaknesses.

### ⚙️ Customize your benchmarking framework

We recognize that benchmarking may not be a one-size-fits-all solution. Our framework allows you to input your own **custom datasets** if you are working with private data.

You can also **customize your evaluation metrics** by selecting any metric in the [SDMetrics library](https://docs.sdv.dev/sdmetrics/).

## Get started

[Install](/sdgym/installation.md) the sdgym software and kick off a benchmarking run in only 2 lines of code!

```python
import sdgym

results = sdgym.benchmark_single_table()
```

## Owned & Maintained by DataCebo

The SDGym library is a part of the [Synthetic Data Vault Project](https://sdv.dev/), first created at MIT's [Data to AI Lab](http://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project.

Today, [DataCebo](https://datacebo.com/) is the proud developer of the SDV, the largest ecosystem for synthetic data generation & evaluation.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.sdv.dev/sdgym/welcome-to-sdgym.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.