seedall 0.2.1

Seed all common RNGs in one call for reproducible experiments.

seedall

Seed all common RNGs in one call for reproducible experiments.

import seedall

seedall.seed(42)  # seeds random, numpy, torch, tensorflow, jax, cupy — whatever is installed

Supported backends

Backend	What gets seeded
random	Python stdlib random
hashseed	PYTHONHASHSEED env var
numpy	np.random.seed()
torch	torch.manual_seed() + cuda.manual_seed_all()
tensorflow	tf.random.set_seed()
jax	Creates a jax.random.PRNGKey (retrieve via states API)
cupy	cp.random.seed()

Missing libraries are silently skipped — install only what you need.

API

seedall.seed(value, *, backends=None, deterministic=False, warn_missing=False)

Seed all (or selected) backends. Returns dict[str, bool] showing what was seeded.

# Seed everything
seedall.seed(42)

# Seed only specific backends
seedall.seed(42, backends=["numpy", "torch"])

# Also enable PyTorch deterministic mode (slower but fully reproducible)
seedall.seed(42, deterministic=True)

seedall.temp_seed(value, *, deterministic=False)

Context manager — seeds on entry, restores previous RNG states on exit.

with seedall.temp_seed(0):
    x = np.random.rand(100)   # reproducible
y = np.random.rand(100)       # back to original sequence

seedall.available()

List detected backends:

>>> seedall.available()
['random', 'hashseed', 'numpy', 'torch']

seedall.get_states() / seedall.set_states(states)

Snapshot and restore RNG states manually:

states = seedall.get_states()
# ... do stuff ...
seedall.set_states(states)  # rewind

seedall.SeedContext(value)

Class-based alternative when a context manager isn't convenient:

ctx = seedall.SeedContext(42)
ctx.enter()   # seed
# ... run experiment ...
ctx.exit()    # restore

Install

pip install seedall                # core (stdlib random only)
pip install seedall[numpy]         # + numpy
pip install seedall[torch]         # + pytorch
pip install seedall[all]           # everything

License

MIT