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.
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!
The easiest way to set up your environment to develop seedbank is to install
uv and just, and run:
uv venv create
just install-dev
You can also set up dev dependencies with pip:
pip install -e '.[dev,test,doc]
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.