Spaces:
Running
Running
File size: 3,917 Bytes
74dabbb 2ce590e 74dabbb 27d7a4f 74dabbb 5426d51 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
---
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},
}
```
|