Notebook の仕様

from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor

import ase
from ase.build import bulk
from pfp_api_client import ASECalculator, Estimator


def calculate_energy():
    with ASECalculator(Estimator()) as calculator:
        atoms = bulk("Si")
        atoms.calc = calculator
        return atoms.get_potential_energy()

NUM_ACCESS=2

# OK: Running joblib.Parallel with default loky backend
from joblib import Parallel, delayed
energy_list = Parallel(NUM_ACCESS)(
    delayed(calculate_energy)() for _ in range(NUM_ACCESS))

# OK: No Estimator is instantiated in this process
with ProcessPoolExecutor(max_workers=NUM_ACCESS) as executor:
    futures = [executor.submit(calculate_energy) for _ in range(NUM_ACCESS)]
    energy_list = [future.result() for future in futures]

# OK: Running multi-threaded computations
# even after a Estimator is instantiated by `calculate_energy()` call in this process.
_ = calculate_energy()
energy_list = Parallel(NUM_ACCESS, backend="threading")(
    delayed(calculate_energy)() for _ in range(NUM_ACCESS))

with ThreadPoolExecutor(max_workers=NUM_ACCESS) as executor:
    futures = [executor.submit(calculate_energy) for _ in range(NUM_ACCESS)]
    energy_list = [future.result() for future in futures]

# NOT OK: A Estimator is instantiated by `calculate_energy()` call in this process
# before multi-process execution
_ = calculate_energy()
with ProcessPoolExecutor(max_workers=NUM_ACCESS) as executor:
    futures = [executor.submit(calculate_energy) for _ in range(NUM_ACCESS)]
    energy_list = [future.result() for future in futures]

# NOT OK: a Estimator is instantiated in this process
# before running joblib.Parallel with multiprocessing backend
energy_list = Parallel(NUM_ACCESS, backend="multiprocessing")(
    delayed(calculate_energy)() for _ in range(NUM_ACCESS))

import ase
from ase.build import bulk
from pfp_api_client import ASECalculator, Estimator


# Not sharing an estimator.
def calculate_energy():
    energies_by_repeats = {}
    for n_repeats in range(1, 20):
        atoms = bulk("Si") * (n_repeats, n_repeats, n_repeats)
        with ASECalculator(Estimator()) as calculator:
            atoms.calc = calculator
            energy = atoms.get_potential_energy()
            energies_by_repeats[n_repeats] = energy
    return energies_by_repeats


# Sharing an estimator during for-loop.
def calculate_energy_share():
    energies_by_repeats = {}
    with ASECalculator(Estimator()) as calculator:
        for n_repeats in range(1, 20):
            atoms = bulk("Si") * (n_repeats, n_repeats, n_repeats)
            atoms.calc = calculator
            energy = atoms.get_potential_energy()
            energies_by_repeats[n_repeats] = energy
    return energies_by_repeats

NUM_ACCESS=2

from joblib import Parallel, delayed

energies_list = Parallel(NUM_ACCESS)(
    delayed(calculate_energy_share)() for _ in range(NUM_ACCESS))