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SDK Notebooks
Jupyter notebook-based tutorials for popular use cases around synthetic data.

Docs

Examples

Notebook
Description
Description
Open in Colab
This notebook is designed to help users successfully train synthetic models on complex datasets with high row and column counts. The code works by intelligently dividing a dataset into a set of smaller datasets of correlated columns that can be parallelized and then joined together.
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Walk through the basics of using Gretel's Python SDK to create a synthetic dataset from a Pandas DataFrame or CSV.
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Train a synthetic model locally and generate data in your environment.
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Conditional data generation (seeding a model) is helpful when you want to preserve some of the original row data (primary keys, dates, important categorical data) in synthetic datasets.
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Balance demographic representation bias in a healthcare set using conditional data generation with a synthetic model.
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Create synthetic time-series data from a Pandas DataFrame or CSV.
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Use a synthetic model to boost the representation of an extreme minority class in a dataset by incorporating features from nearest neighbors.
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Use Gretel APIs to anonymize, synthesize, and then compare synthetic accuracy for a time-series dataset vs real world data.
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Run a sweep to automate hyper parameter optimization for a synthetic model using Weights and Biases.
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Augment a popular machine learning dataset with synthetic data to improve downstream accuracy and algorithmic fairness.
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Measure the effects of different differential privacy settings on a model's ability to memorize and replay secrets in a dataset.
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This notebook shows how to generate synthetic data directly from a multi-table relational database to support data augmentation and subsetting use cases.
Open in Colab
Generate realistic but synthetic text examples using an open-source implementation of the GPT-3 architecture.

Videos

Walk through creating synthetic data with Gretel.ai, Python, Pandas, and Jupyter.
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