Synthetic Data Generation with optional Differential Privacy
Project description
gretel-synthetics
This code has been developed and tested on Python 3.7. Python 3.8 is currently unsupported.
This package allows developers to quickly get emersed with synthetic data generation through the use of neural networks. The more complex pieces of working with libraries like Tensorflow and differential privacy are bundled into friendly Python classes and functions.
For example usage, please launch the example Jupyter Notebook and step through the config, train, and generation examples.
NOTE: The settings in the Jupyter Notebook are optimized to run on a CPU, so you can get the hang of how things work. We
highly recommend running with no max_char
limitation and at least 30 epochs on a GPU.
Getting Started
pip install -U .
or
pip install gretel-synthetics
then...
$ pip install jupyter
$ jupyter notebook
When the UI launches in your browser, navigate to examples/synthetic_records.ipynb
and get generating!
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