title: Pytorch, pipenv and numpy support?
body: <p>I am using <a href="https://github.com/pytorch/pytorch" rel="nofollow noreferrer">pytorch</a> and installed it follow <a href="https://github.com/pytorch/pytorch/issues/4793" rel="nofollow noreferrer">these instructions</a>:</p>
  
  <pre><code>pipenv install git+https://github.com/pytorch/pytorch#egg=pytorch
  pipenv install git+https://github.com/pytorch/vision#egg=torchvision
  </code></pre>
  
  <p>in order to install pytorch <em>with</em> pipenv. This runs some of the examples, but for instance, <a href="https://github.com/pytorch/examples/blob/master/mnist/main.py" rel="nofollow noreferrer">this MNIST code</a>:</p>
  
  <pre><code>from __future__ import print_function
  import argparse
  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
  
  # Training settings
  parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
  parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                      help='input batch size for training (default: 64)')
  parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                      help='input batch size for testing (default: 1000)')
  parser.add_argument('--epochs', type=int, default=10, metavar='N',
                      help='number of epochs to train (default: 10)')
  parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                      help='learning rate (default: 0.01)')
  parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                      help='SGD momentum (default: 0.5)')
  parser.add_argument('--no-cuda', action='store_true', default=False,
                      help='disables CUDA training')
  parser.add_argument('--seed', type=int, default=1, metavar='S',
                      help='random seed (default: 1)')
  parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                      help='how many batches to wait before logging training status')
  args = parser.parse_args()
  args.cuda = not args.no_cuda and torch.cuda.is_available()
  
  torch.manual_seed(args.seed)
  if args.cuda:
      torch.cuda.manual_seed(args.seed)
  
  
  kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
  train_loader = torch.utils.data.DataLoader(
      datasets.MNIST('../data', train=True, download=True,
                     transform=transforms.Compose([
                         transforms.ToTensor(),
                         transforms.Normalize((0.1307,), (0.3081,))
                     ])),
      batch_size=args.batch_size, shuffle=True, **kwargs)
  test_loader = torch.utils.data.DataLoader(
      datasets.MNIST('../data', train=False, transform=transforms.Compose([
                         transforms.ToTensor(),
                         transforms.Normalize((0.1307,), (0.3081,))
                     ])),
      batch_size=args.test_batch_size, shuffle=True, **kwargs)
  
  
  class Net(nn.Module):
      def __init__(self):
          super(Net, self).__init__()
          self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
          self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
          self.conv2_drop = nn.Dropout2d()
          self.fc1 = nn.Linear(320, 50)
          self.fc2 = nn.Linear(50, 10)
  
      def forward(self, x):
          x = F.relu(F.max_pool2d(self.conv1(x), 2))
          x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
          x = x.view(-1, 320)
          x = F.relu(self.fc1(x))
          x = F.dropout(x, training=self.training)
          x = self.fc2(x)
          return F.log_softmax(x, dim=1)
  
  model = Net()
  if args.cuda:
      model.cuda()
  
  optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
  
  def train(epoch):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          if args.cuda:
              data, target = data.cuda(), target.cuda()
          data, target = Variable(data), Variable(target)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % args.log_interval == 0:
              print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                  epoch, batch_idx * len(data), len(train_loader.dataset),
                  100. * batch_idx / len(train_loader), loss.data[0]))
  
  def test():
      model.eval()
      test_loss = 0
      correct = 0
      for data, target in test_loader:
          if args.cuda:
              data, target = data.cuda(), target.cuda()
          data, target = Variable(data, volatile=True), Variable(target)
          output = model(data)
          test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
          pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
          correct += pred.eq(target.data.view_as(pred)).long().cpu().sum()
  
      test_loss /= len(test_loader.dataset)
      print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
          test_loss, correct, len(test_loader.dataset),
          100. * correct / len(test_loader.dataset)))
  
  
  for epoch in range(1, args.epochs + 1):
      train(epoch)
      test()
  </code></pre>
  
  <p>Does not run giving the error:</p>
  
  <pre><code>RuntimeError: PyTorch was compiled without NumPy support
  </code></pre>
  
  <p>Running:</p>
  
  <pre><code>pipenv install numpy
  </code></pre>
  
  <p>does not resolve the error. Is there a way to use pipenv and install pytorch with numpy support?</p>