---
title: Synthetic Sdk Demo
emoji: 🚀
colorFrom: green
colorTo: green
sdk: docker
pinned: false
license: apache-2.0
short_description: The Synthetic Data SDK is a Python toolkit from MOSTLY AI
---

# Synthetic Data SDK by MOSTLY AI Demo

[Documentation](https://mostly-ai.github.io/mostlyai/) | [Technical White Paper](https://arxiv.org/abs/2508.00718) | [Usage Examples](https://mostly-ai.github.io/mostlyai/usage/) | [Free Cloud Service](https://app.mostly.ai/)

The **Synthetic Data SDK** is a Python toolkit for high-fidelity, privacy-safe **Synthetic Data**.

- **LOCAL** mode trains and generates synthetic data locally on your own compute resources.
- **CLIENT** mode connects to a remote MOSTLY AI platform for training & generating synthetic data there.
- Generators, that were trained locally, can be easily imported to a platform for further sharing.

## Overview

The SDK allows you to programmatically create, browse and manage 3 key resources:

1. **Generators** - Train a synthetic data generator on your existing tabular or language data assets
2. **Synthetic Datasets** - Use a generator to create any number of synthetic samples to your needs
3. **Connectors** - Connect to any data source within your organization, for reading and writing data

| Intent                                        | Primitive                         | API Reference                                                                                                 |
|-----------------------------------------------|-----------------------------------|---------------------------------------------------------------------------------------------------------------|
| Train a Generator on tabular or language data | `g = mostly.train(config)`        | [mostly.train](https://mostly-ai.github.io/mostlyai/api_client/#mostlyai.sdk.client.api.MostlyAI.train)       |
| Generate any number of synthetic data records | `sd = mostly.generate(g, config)` | [mostly.generate](https://mostly-ai.github.io/mostlyai/api_client/#mostlyai.sdk.client.api.MostlyAI.generate) |
| Live probe the generator on demand            | `df = mostly.probe(g, config)`    | [mostly.probe](https://mostly-ai.github.io/mostlyai/api_client/#mostlyai.sdk.client.api.MostlyAI.probe)       |
| Connect to any data source within your org    | `c = mostly.connect(config)`      | [mostly.connect](https://mostly-ai.github.io/mostlyai/api_client/#mostlyai.sdk.client.api.MostlyAI.connect)   |

https://github.com/user-attachments/assets/9e233213-a259-455c-b8ed-d1f1548b492f

## Key Features

- **Broad Data Support**
  - Mixed-type data (categorical, numerical, geospatial, text, etc.)
  - Single-table, multi-table, and time-series
- **Multiple Model Types**
  - State-of-the-art performance via TabularARGN
  - Fine-tune Hugging Face hosted language models
  - Efficient LSTM for text synthesis from scratch
- **Advanced Training Options**
  - GPU/CPU support
  - Differential Privacy
  - Progress Monitoring
- **Automated Quality Assurance**
  - Quality metrics for fidelity and privacy
  - In-depth HTML reports for visual analysis
- **Flexible Sampling**
  - Up-sample to any data volumes
  - Conditional simulations based on any columns
  - Re-balance underrepresented segments
  - Context-aware data imputation
  - Statistical fairness controls
  - Rule-adherence via temperature
- **Seamless Integration**
  - Connect to external data sources (DBs, cloud storages)
  - Fully permissive open-source license

## Citation

Please consider citing our project if you find it useful:

```bibtex
@misc{mostlyai,
      title={Democratizing Tabular Data Access with an Open-Source Synthetic-Data SDK},
      author={Ivona Krchova and Mariana Vargas Vieyra and Mario Scriminaci and Andrey Sidorenko},
      year={2025},
      eprint={2508.00718},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.00718},
}
```