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151 lines
4.9 KiB
Python
151 lines
4.9 KiB
Python
# Copyright (c) 2017, PyTorch Team
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# All rights reserved
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# Licensed under BSD 3-Clause License.
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# This example is based on PyTorch MNIST example:
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# https://github.com/pytorch/examples/blob/master/mnist/main.py
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import mlflow
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import mlflow.pytorch
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from mlflow.utils.environment import _mlflow_conda_env
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import warnings
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import cloudpickle
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision
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from torchvision import datasets, transforms
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(1, 20, 5, 1)
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self.conv2 = nn.Conv2d(20, 50, 5, 1)
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self.fc1 = nn.Linear(4 * 4 * 50, 500)
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self.fc2 = nn.Linear(500, 10)
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def forward(self, x):
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# Added the view for reshaping score requests
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x = x.view(-1, 1, 28, 28)
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x = F.relu(self.conv1(x))
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x = F.max_pool2d(x, 2, 2)
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x = F.relu(self.conv2(x))
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x = F.max_pool2d(x, 2, 2)
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x = x.view(-1, 4 * 4 * 50)
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return F.log_softmax(x, dim=1)
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def train(args, model, device, train_loader, optimizer, epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch_idx % args.log_interval == 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
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epoch, batch_idx * len(data), len(train_loader.dataset),
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100. * batch_idx / len(train_loader), loss.item()))
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# Use MLflow logging
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mlflow.log_metric("epoch_loss", loss.item())
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def test(args, model, device, test_loader):
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model.eval()
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test_loss = 0
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correct = 0
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with torch.no_grad():
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for data, target in test_loader:
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data, target = data.to(device), target.to(device)
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output = model(data)
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# sum up batch loss
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test_loss += F.nll_loss(output, target, reduction="sum").item()
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# get the index of the max log-probability
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pred = output.argmax(dim=1, keepdim=True)
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correct += pred.eq(target.view_as(pred)).sum().item()
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test_loss /= len(test_loader.dataset)
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print("\n")
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print("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
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test_loss, correct, len(test_loader.dataset),
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100. * correct / len(test_loader.dataset)))
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# Use MLflow logging
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mlflow.log_metric("average_loss", test_loss)
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class Args(object):
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pass
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# Training settings
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args = Args()
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setattr(args, 'batch_size', 64)
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setattr(args, 'test_batch_size', 1000)
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setattr(args, 'epochs', 3) # Higher number for better convergence
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setattr(args, 'lr', 0.01)
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setattr(args, 'momentum', 0.5)
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setattr(args, 'no_cuda', True)
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setattr(args, 'seed', 1)
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setattr(args, 'log_interval', 10)
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setattr(args, 'save_model', True)
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use_cuda = not args.no_cuda and torch.cuda.is_available()
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torch.manual_seed(args.seed)
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device = torch.device("cuda" if use_cuda else "cpu")
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kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
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train_loader = torch.utils.data.DataLoader(
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datasets.MNIST('../data', train=True, download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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test_loader = torch.utils.data.DataLoader(
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datasets.MNIST(
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'../data',
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train=False,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))])),
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batch_size=args.test_batch_size, shuffle=True, **kwargs)
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def driver():
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warnings.filterwarnings("ignore")
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# Dependencies for deploying the model
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pytorch_index = "https://download.pytorch.org/whl/"
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pytorch_version = "cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl"
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deps = [
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"cloudpickle=={}".format(cloudpickle.__version__),
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pytorch_index + pytorch_version,
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"torchvision=={}".format(torchvision.__version__),
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"Pillow=={}".format("6.0.0")
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]
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with mlflow.start_run() as run:
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model = Net().to(device)
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optimizer = optim.SGD(
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model.parameters(),
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lr=args.lr,
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momentum=args.momentum)
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for epoch in range(1, args.epochs + 1):
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train(args, model, device, train_loader, optimizer, epoch)
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test(args, model, device, test_loader)
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# Log model to run history using MLflow
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if args.save_model:
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model_env = _mlflow_conda_env(additional_pip_deps=deps)
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mlflow.pytorch.log_model(model, "model", conda_env=model_env)
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return run
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if __name__ == "__main__":
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driver()
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