This release is a pre-release and may not be stable for production use.
Overview
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more!
The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You can also customize the process to include your own work.
- Datasets: Select any of the publicly available datasets from the SDV project, or input your own data.
- Synthesizers: Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model.
- Evaluation: In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics.
Install
Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.
pip install sdgym
conda install -c pytorch -c conda-forge sdgym
For more information about using SDGym, visit the SDGym Documentation.
Usage
Let's benchmark synthetic data generation for single tables. First, let's define which modeling techniques we want to use. Let's choose a few synthesizers from the SDV library and a few others to use as baselines.
# these synthesizers come from the SDV library
# each one uses different modeling techniques
sdv_synthesizers = ['GaussianCopulaSynthesizer', 'CTGANSynthesizer']
# these basic synthesizers are available in SDGym
# as baselines
baseline_synthesizers = ['UniformSynthesizer']
Now, we can benchmark the different techniques:
import sdgym
sdgym.benchmark_single_table(synthesizers=(sdv_synthesizers + baseline_synthesizers))
The result is a detailed performance, memory and quality evaluation across the synthesizers on a variety of publicly available datasets.
Supplying a custom synthesizer
Benchmark your own synthetic data generation techniques. Define your synthesizer by specifying the training logic (using machine learning) and the sampling logic.
def my_training_logic(data, metadata):
# create an object to represent your synthesizer
# train it using the data
return synthesizer
def my_sampling_logic(trained_synthesizer, num_rows):
# use the trained synthesizer to create
# num_rows of synthetic data
return synthetic_data
Learn more in the Custom Synthesizers Guide.
Customizing your datasets
The SDGym library includes many publicly available datasets that you can include right away.
List these using the list_datasets feature.
sdgym.dataset_explorer.DatasetExplorer().list_datasets()
dataset_name size_MB num_tables
KRK_v1 0.072128 1
adult 3.907448 1
alarm 4.520128 1
asia 1.280128 1
...
You can also include any custom, private datasets that are stored on your computer on an Amazon S3 bucket.
my_datasets_folder = 's3://my-datasets-bucket'
For more information, see the docs for Customized Datasets.
What's next?
Visit the SDGym Documentation to learn more!
The Synthetic Data Vault Project was first created at MIT's Data to AI Lab 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 is the proud developer of SDV, the largest ecosystem for synthetic data generation & evaluation. It is home to multiple libraries that support synthetic data, including:
- 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data.
- 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular, multi table and time series data.
- 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data generation models.
Get started using the SDV package -- a fully integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries for specific needs.
6 maintainers
Metadata
6 maintainers
Release files for sdgym 0.15.1.dev0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sdgym-0.15.1.dev0.tar.gz | 115.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sdgym-0.15.1.dev0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 243.3 kB
Release files / sdgym-0.15.1.dev0.tar.gz
| Download URL | sdgym-0.15.1.dev0.tar.gz |
|---|---|
| Size | 115.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ed4979cb55e9ca659ce6687e4962b4c4249555aac5213d41985d8e9292fe27e2
|
|
BLAKE2b-256 checksum How to use checksums |
7034b83d2d72e1e41e8b5d8f31180cdb6bfbf4e27fe42e800c6e150f72ca7a6f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 30, 2026.
Transparency logRelease files / sdgym-0.15.1.dev0-py3-none-any.whl
| Download URL | sdgym-0.15.1.dev0-py3-none-any.whl |
|---|---|
| Size | 127.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
98b01b598e4f2e0c97f9e861d0028f592faec7f37e074d782621309513b41984
|
|
BLAKE2b-256 checksum How to use checksums |
043c2e427d39ca0179829222ecd5e66e5afa04019fc5fd6336897791826df599
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 30, 2026.
Transparency log
