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)