diff --git a/how-to-use-azureml/using-mlflow/train-deploy-pytorch/scripts/train.py b/how-to-use-azureml/using-mlflow/train-deploy-pytorch/scripts/train.py
deleted file mode 100644
index 85fa510f..00000000
--- a/how-to-use-azureml/using-mlflow/train-deploy-pytorch/scripts/train.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright (c) 2017, PyTorch Team
-# All rights reserved
-# Licensed under BSD 3-Clause License.
-
-# This example is based on PyTorch MNIST example:
-# https://github.com/pytorch/examples/blob/master/mnist/main.py
-
-import mlflow
-import mlflow.pytorch
-from mlflow.utils.environment import _mlflow_conda_env
-import warnings
-import cloudpickle
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import torchvision
-from torchvision import datasets, transforms
-
-
-class Net(nn.Module):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5, 1)
-        self.conv2 = nn.Conv2d(20, 50, 5, 1)
-        self.fc1 = nn.Linear(4 * 4 * 50, 500)
-        self.fc2 = nn.Linear(500, 10)
-
-    def forward(self, x):
-        # Added the view for reshaping score requests
-        x = x.view(-1, 1, 28, 28)
-        x = F.relu(self.conv1(x))
-        x = F.max_pool2d(x, 2, 2)
-        x = F.relu(self.conv2(x))
-        x = F.max_pool2d(x, 2, 2)
-        x = x.view(-1, 4 * 4 * 50)
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return F.log_softmax(x, dim=1)
-
-
-def train(args, model, device, train_loader, optimizer, epoch):
-    model.train()
-    for batch_idx, (data, target) in enumerate(train_loader):
-        data, target = data.to(device), target.to(device)
-        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.item()))
-            # Use MLflow logging
-            mlflow.log_metric("epoch_loss", loss.item())
-
-
-def test(args, model, device, test_loader):
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for data, target in test_loader:
-            data, target = data.to(device), target.to(device)
-            output = model(data)
-            # sum up batch loss
-            test_loss += F.nll_loss(output, target, reduction="sum").item()
-            # get the index of the max log-probability
-            pred = output.argmax(dim=1, keepdim=True)
-            correct += pred.eq(target.view_as(pred)).sum().item()
-
-    test_loss /= len(test_loader.dataset)
-    print("\n")
-    print("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
-        test_loss, correct, len(test_loader.dataset),
-        100. * correct / len(test_loader.dataset)))
-    # Use MLflow logging
-    mlflow.log_metric("average_loss", test_loss)
-
-
-class Args(object):
-    pass
-
-
-# Training settings
-args = Args()
-setattr(args, 'batch_size', 64)
-setattr(args, 'test_batch_size', 1000)
-setattr(args, 'epochs', 3)  # Higher number for better convergence
-setattr(args, 'lr', 0.01)
-setattr(args, 'momentum', 0.5)
-setattr(args, 'no_cuda', True)
-setattr(args, 'seed', 1)
-setattr(args, 'log_interval', 10)
-setattr(args, 'save_model', True)
-
-use_cuda = not args.no_cuda and torch.cuda.is_available()
-
-torch.manual_seed(args.seed)
-
-device = torch.device("cuda" if use_cuda else "cpu")
-
-kwargs = {'num_workers': 1, 'pin_memory': True} if use_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)
-
-
-def driver():
-    warnings.filterwarnings("ignore")
-    # Dependencies for deploying the model
-    pytorch_index = "https://download.pytorch.org/whl/"
-    pytorch_version = "cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl"
-    deps = [
-        "cloudpickle=={}".format(cloudpickle.__version__),
-        pytorch_index + pytorch_version,
-        "torchvision=={}".format(torchvision.__version__),
-        "Pillow=={}".format("6.0.0")
-    ]
-    with mlflow.start_run() as run:
-        model = Net().to(device)
-        optimizer = optim.SGD(
-            model.parameters(),
-            lr=args.lr,
-            momentum=args.momentum)
-        for epoch in range(1, args.epochs + 1):
-            train(args, model, device, train_loader, optimizer, epoch)
-            test(args, model, device, test_loader)
-        # Log model to run history using MLflow
-        if args.save_model:
-            model_env = _mlflow_conda_env(additional_pip_deps=deps)
-            mlflow.pytorch.log_model(model, "model", conda_env=model_env)
-    return run
-
-
-if __name__ == "__main__":
-    driver()