Pytorch Katib example

Author: johnugeorge

examples/katib/Dockerfile

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+FROM pytorch/pytorch:0.4_cuda9_cudnn7
+
+RUN pip install tensorboardX
+ADD . /opt/pytorch_dist_mnist
+ENTRYPOINT ["python", "/opt/pytorch_dist_mnist/mnist_with_summary.py"]

examples/katib/mnist_with_summary.py

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+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
+from tensorboardX import SummaryWriter
+writer = SummaryWriter('runs')
+# 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.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)
+
+model = Net()
+if args.cuda:
+    model.cuda()
+
+print('Learning rate: {} Momentum: {}'.format(args.lr, args.momentum))
+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={:.4f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+            niter = epoch*len(train_loader)+batch_idx
+            writer.add_scalar('loss', loss.item(), niter)
+
+def test(epoch):
+    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).item() # sum up batch loss
+        pred = output.data.max(1)[1] # get the index of the max log-probability
+        correct += pred.eq(target.data).cpu().sum()
+
+    test_loss /= len(test_loader.dataset)
+    print('\naccuracy={:.4f}\n'.format(float(correct) / len(test_loader.dataset)))
+    writer.add_scalar('accuracy', float(correct) / len(test_loader.dataset), epoch)
+
+
+for epoch in range(1, args.epochs + 1):
+    train(epoch)
+    test(epoch)

examples/katib/pytorch_job_mnist.yaml

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+apiVersion: "kubeflow.org/v1beta1"
+kind: "PyTorchJob"
+metadata:
+  name: "pytorch-mnist-with-summary"
+spec:
+  pytorchReplicaSpecs:
+    Worker:
+      replicas: 1
+      restartPolicy: OnFailure
+      template:
+        spec:
+          containers:
+            - name: pytorch
+              image: gcr.io/kubeflow-ci/pytorch-mnist-with-summary:0.4