Using a parallel runner in memo.

The simulation function we're running could easily be run in parallel. That's why memo also has a Runner class available.

import numpy as np
from memo import memlist, grid, Runner

data = []

@memlist(data=data)
def birthday_experiment(class_size, n_sim=1000):
    """Simulates the birthday paradox. Vectorized = Fast!"""
    sims = np.random.randint(1, 365 + 1, (n_sim, class_size))
    sort_sims = np.sort(sims, axis=1)
    n_uniq = (sort_sims[:, 1:] != sort_sims[:, :-1]).sum(axis = 1) + 1
    return {"est_prob": np.mean(n_uniq != class_size), "number": 42}

settings = grid(
    class_size=range(2, 100),
    n_sim=[1_000, 10_000, 100_000]
)

Runner(backend="threading", n_jobs=4).run(birthday_experiment, settings)

This Runner class will allow us to run jobs in parallel but it will also give us a nifty little progress bar.