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"""
Modification version of https://github.com/optuna/optuna/pull/2303 with nccl backend

Optuna example that optimizes multi-layer perceptrons using PyTorch distributed.

In this example, we optimize the validation accuracy of hand-written digit recognition using
PyTorch distributed data parallel and MNIST. We optimize the neural network architecture as well
as the optimizer configuration. As it is too time consuming to use the whole MNIST dataset, we
here use a small subset of it.

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# CIFAR-100

import numpy as np
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR100


train_transform = transforms.Compose(
    [

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fmatrix.py

• print data を print(data) に置換
• from six.moves import range を追加し xrange を range に置換

logging.py

python3 から logging が標準ライブラリにあります。import 時に衝突するため、別名に変更する必要があります。

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# -*- coding: utf-8 -*-
require 'twitter'


consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

client = Twitter::REST::Client.new do |config|

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