def main():
print('Generating default config for surreal at ~/.surreal.yml')
default_path = U.f_expand('~/.surreal.yml')
U.move_with_backup(default_path)
fname = pkg_resources.resource_filename('surreal', 'sample_surreal.yml')
shutil.copyfile(fname, default_path)
import os
import numpy as np
from surreal.utils.checkpoint import *
import surreal.utils as U
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
torch.manual_seed(42)
DATA_FOLDER = '~/Temp/data'
DATA_FOLDER = U.f_expand(DATA_FOLDER)
def get_loader(train=False):
return torch.utils.data.DataLoader(datasets.MNIST(
DATA_FOLDER,
train=train,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]),
download=True),
batch_size=12,
shuffle=False)
def folder(self):
return U.f_expand(self.config.kube_metadata_folder)
def folder(self):
if 'subproc_results_folder' not in self.config:
raise KeyError(
'Please specify "subproc_results_folder" in ~/.surreal.yml')
return U.f_expand(self.config.subproc_results_folder)