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============================= test session starts ==============================
platform linux -- Python 3.6.4, pytest-3.5.0, py-1.5.3, pluggy-0.6.0
rootdir: /home/tmain/dvc, inifile:
plugins: sugar-0.9.1, remotedata-0.2.0, openfiles-0.2.0, doctestplus-0.1.2, arraydiff-0.2
collected 1 item

tests/test_install.py F                                                  [100%]

=================================== FAILURES ===================================
_______________________________ TestInstall.test _______________________________

tdeboissiere

============================= test session starts ==============================
platform linux -- Python 3.6.4, pytest-3.5.0, py-1.5.3, pluggy-0.6.0
rootdir: /home/tmain/Lyrebird/dvc, inifile:
plugins: sugar-0.9.1, remotedata-0.2.0, openfiles-0.2.0, doctestplus-0.1.2, arraydiff-0.2
collected 1 item

tests/test_install.py F                                                  [100%]

=================================== FAILURES ===================================
_______________________________ TestInstall.test _______________________________

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####################################
# Apt libraries
####################################
sudo apt-get update && sudo apt-get install -y --no-install-recommends\
        build-essential \
        vim \
        numactl \
        git \
        curl \
        git \

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import math
import numpy as np
import tensorflow as tf

log_sigma1 = 0.
log_sigma2 = -0.5
prior_pi = 0.25
sigma1 = math.exp(log_sigma1)
sigma2 = math.exp(log_sigma2)

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def extract_patches(imgs, landmarks, patch_shape):
    """ Extracts patches from an image.
    Args:
        imgs: a numpy array of dimensions [batch_size, width, height, channels]
        landmarks: a numpy array of dimensions [num_patches, 2]
        patch_shape: (width, height)
    Returns:
        a numpy array [num_patches, width, height, channels]
    """

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import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
from yellowfin_tuner import YFOptimizer
from tqdm import tqdm

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import torch
from torch.autograd import Variable
import torch.nn as nn
import numpy as np
import torch.optim as optim
import torch.nn.functional as F
import time
from torchvision.models import vgg

# Initialize network

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import pandas as pd
import numpy as np
from tsfresh.feature_extraction import feature_calculators as fc
from time import time


def count_above_mean(x):

    x = np.asarray(x)
    m = np.mean(x)

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from __future__ import print_function
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.utils import generic_utils
import multiprocessing
import os
import time

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print 1