Synthetic Data SDK ✨

Documentation | Usage Examples | Free Cloud Service

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
Generate any number of synthetic data records	sd = mostly.generate(g, config)	mostly.generate
Live probe the generator on demand	df = mostly.probe(g, config)	mostly.probe
Connect to any data source within your org	c = mostly.connect(config)	mostly.connect

https://github.com/user-attachments/assets/d1613636-06e4-4147-bef7-25bb4699e8fc

Key Features

• Broad Data Support
  • Mixed-type data (categorical, numerical, geospatial, text, etc.)
  • Single-table, multi-table, and time-series
• Multiple Model Types
  • TabularARGN for SOTA tabular performance
  • Fine-tune HuggingFace-based 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 generation by 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

Quick Start

Install the SDK via pip:

pip install mostlyai

Train your first generator:

import pandas as pd
from mostlyai.sdk import MostlyAI

# load original data
repo_url = "https://github.com/mostly-ai/public-demo-data/raw/refs/heads/dev"
df_original = pd.read_csv(f"{repo_url}/census/census.csv.gz")
df_original = df_original.sample(n=10_000)  # sub-sample to speed up demo

# initialize the SDK
mostly = MostlyAI()

# train a synthetic data generator, with default configs
g = mostly.train(name="Quick Start Demo", data=df_original)

# display the quality assurance report
g.reports(display=True)

Once the generator has been trained, generate synthetic data samples. Either via probing:

# probe for some representative synthetic samples
df_samples = mostly.probe(g, size=100)
df_samples

or by creating a synthetic dataset entity for larger data volumes:

# generate a large representative synthetic dataset
sd = mostly.generate(g, size=100_000)
df_synthetic = sd.data()
df_synthetic

or by conditionally probing / generating synthetic data:

# create 100 seed records of 24y old Mexicans
df_seed = pd.DataFrame({
    'age': [24] * 100,
    'native_country': ['Mexico'] * 100,
})
# conditionally probe, based on provided seed
df_samples = mostly.probe(g, seed=df_seed)
df_samples

Installation

Use pip (or better uv pip) to install the official mostlyai package via PyPI. Python 3.10 or higher is required.

It is highly recommended to install the package within a dedicated virtual environment, such as venv, uv, or conda. E.g.

conda create -n mostlyai python=3.12
conda activate mostlyai

CLIENT mode

This is a light-weight installation for using the SDK in CLIENT mode only. It communicates to a MOSTLY AI platform to perform requested tasks. See e.g. app.mostly.ai for a free-to-use hosted version.

pip install -U mostlyai

CLIENT + LOCAL mode

This is a full installation for using the SDK in both CLIENT and LOCAL mode. It includes all dependencies, incl. PyTorch, for training and generating synthetic data locally.

# for CPU on macOS
pip install -U 'mostlyai[local]'
# for CPU on Linux
pip install -U 'mostlyai[local-cpu]' --extra-index-url https://download.pytorch.org/whl/cpu
# for GPU on Linux
pip install -U 'mostlyai[local-gpu]'

Note for Google Colab users: Installing any of the local extras (mostlyai[local], mostlyai[local-cpu], or mostlyai[local-gpu]) will downgrade PyTorch from 2.6.0 to 2.5.1. You'll need to restart the runtime after installation for the changes to take effect.

Add any of the following extras for further data connectors support in LOCAL mode: databricks, googlebigquery, hive, mssql, mysql, oracle, postgres, snowflake. E.g.

pip install -U 'mostlyai[local, databricks, snowflake]'

Citation

Please consider citing our project if you find it useful:

@software{mostlyai,
    author = {{MOSTLY AI}},
    title = {{MOSTLY AI SDK}},
    url = {https://github.com/mostly-ai/mostlyai},
    year = {2025}
}