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train.py
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train.py
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# 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()