FastAI Reproducibility Experiment

fastai_reproducibility.py

import random
import numpy as np
import torch

from fastai.vision import *


def set_seeds():
    random.seed(42)
    np.random.seed(12345)
    torch.manual_seed(1234)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
model = models.resnet18

# Trial 1: only set seeds once
print('Trial 1\n')
set_seeds()
learner = cnn_learner(data, model)
learner.fit_one_cycle(3, 1e-3)
print('\n')

learner = cnn_learner(data, model)
learner.fit_one_cycle(3, 1e-3)
print('\n\n')
# ======================================================

# Trial 2: set seeds before each call to fit
print('Trial 2\n')
set_seeds()
learner = cnn_learner(data, model)
learner.fit_one_cycle(3, 1e-3)
print('\n')

set_seeds()
learner = cnn_learner(data, model)
learner.fit_one_cycle(3, 1e-3)
print('\n')
# ================================================