seedbank 0.1.3

Common infrastructure for initializing random number generators.

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.initialize(65000)

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

Acknowledgements

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.