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Python Seed Manager

Python programs, particularly data science applications, often need to interact with multiple different random number generators.

This package provides a unified interface to seeding them, along with APIs for deriving additional RNG seeds in a predictable way (using NumPy 1.17's new random infrastructure) and constructing random generators.

Quick Start

To get started, just use the seedbank.initialize() function to seed all available random number generators:

import seedbank

SeedBank will seed all of the known generators that will be available, including:

  • Python standard random
  • NumPy legacy random numpy.random
  • PyTorch (with torch.manual_seed())
  • Numba’s NumPy random
  • TensorFlow (with tf.random.set_seed())
  • cupy (with cupy.random.seed())

In addition, it will initialize a root seed for constructing new-style NumPy Generator instances.

If SeedBank doesn’t support your RNG yet, please submit a pull request!

Developing SeedBank

SeedBank uses Flit for managing dependencies. To set up in a fresh virtual environment:

python -m pip install flit
flit install --pth-file

[conda-lock][] can help you set up a Conda environment (replace linux-64 with your platform):

# install conda-lock in base environment
# alternatively: pip install conda-lock
conda install -c conda-forge conda-lock
# create the lock file
conda-lock -p linux-64 -f pyproject.toml
# create the environment
conda env create -n seedbank -f conda-linux-64.lock


This material is based upon work supported by the National Science Foundation under Grant No. IIS 17-51278. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.