SDK Notebooks

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

Docs
Gretel's Python SDK and CLI: https://python.docs.gretel.ai​
Gretel's Open Source Synthetic Core: https://synthetics.docs.gretel.ai​
Examples
Notebook
Description
Description
​trainer-notebook 

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.
​create-synthetic-data 
Open in Colab​
Walk through the basics of using Gretel's Python SDK to create a synthetic dataset from a Pandas DataFrame or CSV.
​create-synthetic-data-in-your-cloud​
Open in Colab​
Train a synthetic model locally and generate data in your environment.
​conditional-data-generation-example​
Open in Colab​
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.
​balance-dataset-example​
Open in Colab​
Balance demographic representation bias in a healthcare set using conditional data generation with a synthetic model.
​create-synthetic-time-serie​s​
Open in Colab​
Create synthetic time-series data from a Pandas DataFrame or CSV.
​synthetic-oversampling-for-imbalanced-data​
Open in Colab​
Use a synthetic model to boost the representation of an extreme minority class in a dataset by incorporating features from nearest neighbors.
​time-series-poc 
Open in Colab​
Use Gretel APIs to anonymize, synthesize, and then compare synthetic accuracy for a time-series dataset vs real world data.
​parameter-sweep-wandb 
Open in Colab​
Run a sweep to automate hyper parameter optimization for a synthetic model using Weights and Biases.
​improve-accuracy-heart-disease 
Open in Colab​
Augment a popular machine learning dataset with synthetic data to improve downstream accuracy and algorithmic fairness.
​synthetic-data-with-differential-privacy​
Open in Colab​
Measure the effects of different differential privacy settings on a model's ability to memorize and replay secrets in a dataset.
​synthesize-a-postgres-database​
Open in Colab​
This notebook shows how to generate synthetic data directly from a multi-table relational database to support data augmentation and subsetting use cases.
​conditional-text-generation-with-gpt​
Open in Colab​
Generate realistic but synthetic text examples using an open-source implementation of the GPT-3 architecture.
​timeseries-with-DoppelGANger​
Open in Colab​
Generate synthetic daily oil price data using the DoppelGANger GAN for time-series data.
​evaluate-synthetic-data-quality​
Open in Colab​
Produce a quality score and detailed report for any synthetic dataset vs. real world data.
Videos
Walk through creating synthetic data with Gretel.ai, Python, Pandas, and Jupyter.

Gretel Amplify

Next - Synthetics

CLI Examples

Last modified 6mo ago

Notebook	Description	Description
trainer-notebook	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.
create-synthetic-data	Open in Colab	Walk through the basics of using Gretel's Python SDK to create a synthetic dataset from a Pandas DataFrame or CSV.
create-synthetic-data-in-your-cloud	Open in Colab	Train a synthetic model locally and generate data in your environment.
conditional-data-generation-example	Open in Colab	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.
balance-dataset-example	Open in Colab	Balance demographic representation bias in a healthcare set using conditional data generation with a synthetic model.
create-synthetic-time-series	Open in Colab	Create synthetic time-series data from a Pandas DataFrame or CSV.
synthetic-oversampling-for-imbalanced-data	Open in Colab	Use a synthetic model to boost the representation of an extreme minority class in a dataset by incorporating features from nearest neighbors.
time-series-poc	Open in Colab	Use Gretel APIs to anonymize, synthesize, and then compare synthetic accuracy for a time-series dataset vs real world data.
parameter-sweep-wandb	Open in Colab	Run a sweep to automate hyper parameter optimization for a synthetic model using Weights and Biases.
improve-accuracy-heart-disease	Open in Colab	Augment a popular machine learning dataset with synthetic data to improve downstream accuracy and algorithmic fairness.
synthetic-data-with-differential-privacy	Open in Colab	Measure the effects of different differential privacy settings on a model's ability to memorize and replay secrets in a dataset.
synthesize-a-postgres-database	Open in Colab	This notebook shows how to generate synthetic data directly from a multi-table relational database to support data augmentation and subsetting use cases.
conditional-text-generation-with-gpt	Open in Colab	Generate realistic but synthetic text examples using an open-source implementation of the GPT-3 architecture.
timeseries-with-DoppelGANger	Open in Colab	Generate synthetic daily oil price data using the DoppelGANger GAN for time-series data.
evaluate-synthetic-data-quality	Open in Colab	Produce a quality score and detailed report for any synthetic dataset vs. real world data.