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.