NumPy interop and reproducibility#
The API intentionally resembles numpy.random.Generator and provides common
distributions such as random, integers, uniform, normal,
choice, shuffle, permutation.
Reproducibility has a scope#
A fixed seed controls this generator’s state transition. It does not by itself make an entire machine-learning experiment deterministic: thread scheduling, BLAS libraries, GPU kernels, data ordering, third-party estimators, and process fork/spawn behavior can introduce additional randomness.
Record the seed, generator/bit width, package version, and other algorithmic random-state settings when results must be reproduced later.
Further reading#
The repository ships Good Practice in (Pseudo) Random Number
Generation alongside this guide. It is useful background
for thinking about independent streams, seeding discipline, and reproducibility.