Synthetic Data SDK ✨

SDK Documentation | Platform Documentation | Usage Examples

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

• Client mode connects to a remote MOSTLY AI platform for training & generating synthetic data there.
• Local mode trains and generates synthetic data locally on your own compute resources.
• 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	Documentation
Train a Generator on tabular or language data	g = mostly.train(config)	see mostly.train
Generate any number of synthetic data records	sd = mostly.generate(g, config)	see mostly.generate
Live probe the generator on demand	df = mostly.probe(g, config)	see mostly.probe
Connect to any data source within your org	c = mostly.connect(config)	see mostly.connect

Installation

Client mode only

pip install -U mostlyai

Client + Local mode

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

Optional Connectors

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

E.g.

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

Quick Start

Generate your first samples based on your own trained generator with a few lines of code. For local mode, initialize the SDK with local=True. For client mode, initialize the SDK with base_url and api_key obtained from your account settings page.

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").sample(n=5_000)

# initialize the SDK in local or client mode
mostly = MostlyAI(local=True)                       # local mode
# mostly = MostlyAI(base_url='xxx', api_key='xxx')  # client mode

# train a synthetic data generator
g = mostly.train(
    config={
        "name": "US Census Income",
        "tables": [
            {
                "name": "census",
                "data": df_original,
                "tabular_model_configuration": {  # tabular model configuration (optional)
                    "max_training_time": 1,  # - limit training time (in minutes)
                    # model, max_epochs,,..        # further model configurations (optional)
                    # 'differential_privacy': {    # differential privacy configuration (optional)
                    #     'max_epsilon': 5.0,      # - max epsilon value, used as stopping criterion
                    #     'delta': 1e-5,           # - delta value
                    # }
                },
                # columns, keys, compute,..      # further table configurations (optional)
            }
        ],
    },
    start=True,  # start training immediately (default: True)
    wait=True,  # wait for completion (default: True)
)

Once the generator has been trained, you can use it to 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

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

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}
}