To Install DC/OS Data Science Engine with PyTorch. Run the following command:

dcos package install --options=options.json data-science-engine

With options.json having the following content:

{
    "service": {
        "jupyter_notebook_type": "PyTorch-1.4.0"
    }
}

PyTorch local machine learning

Open a Python 3 Notebook and put the following sections in different code cells.

Training settings

 batch_size = 1000        # input batch size for training
 test_batch_size = 1000   # input batch size for testing
 epochs = 1               # number of epochs to train
 lr = 1.0                 # learning rate
 gamma = 0.7              # learning rate step gamma
 seed = 1                 # random seed
 log_interval = 10        # how many batches to wait before logging training status

Describe layers of the model

from __future__ import print_function
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.optim.lr_scheduler import StepLR

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

Implement train function

def train(model, device, train_loader, optimizer, epoch, log_interval):
    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 % 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()))

Implement test function

def test(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)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    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)))

Training model

use_cuda = torch.cuda.is_available()

torch.manual_seed(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=batch_size, shuffle=True, **kwargs)

model = Net().to(device)
optimizer = optim.Adadelta(model.parameters(), lr=lr)

scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, epoch, log_interval)
    test(model, device, test_loader)
    scheduler.step()

PyTorch Distributed Learning with Horovod on Spark

Open a Python 3Notebook and put the following sections in different code cells.

Describe layers of the model

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
import torch.optim as optim
from torchvision import datasets, transforms
import torch.utils.data.distributed
import horovod.torch as hvd

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)

Implement training function

def train(optimizer, epoch, is_cuda, model, train_sampler, train_loader):
    model.train()
    # Horovod: set epoch to sampler for shuffling.
    train_sampler.set_epoch(epoch)
    for batch_idx, (data, target) in enumerate(train_loader):
        if is_cuda:
            data, target = data.cuda(), target.cuda()
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            # Horovod: use train_sampler to determine the number of examples in
            # this worker's partition.
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_sampler),
                100. * batch_idx / len(train_loader), loss.item()))

Averaging of the input tensor over all the Horovod processes

def metric_average(val, name):
    tensor = torch.tensor(val)
    avg_tensor = hvd.allreduce(tensor, name=name)
    return avg_tensor.item()

Implement test function

def test(is_cuda, model, test_sampler, test_loader):
    model.eval()
    test_loss = 0.
    test_accuracy = 0.
    for data, target in test_loader:
        if is_cuda:
            data, target = data.cuda(), target.cuda()
        output = model(data)
        # sum up batch loss
        test_loss += F.nll_loss(output, target, size_average=False).item()
        # get the index of the max log-probability
        pred = output.data.max(1, keepdim=True)[1]
        test_accuracy += pred.eq(target.data.view_as(pred)).cpu().float().sum()

    # Horovod: use test_sampler to determine the number of examples in
    # this worker's partition.
    test_loss /= len(test_sampler)
    test_accuracy /= len(test_sampler)

    # Horovod: average metric values across workers.
    test_loss = metric_average(test_loss, 'avg_loss')
    test_accuracy = metric_average(test_accuracy, 'avg_accuracy')

    # Horovod: print output only on first rank.
    if hvd.rank() == 0:
        print('\nTest set: Average loss: {:.4f}, Accuracy: {:.2f}%\n'.format(
            test_loss, 100. * test_accuracy))

Implement distributed training function using Horovod

def train_hvd():   
    batch = 1000
    epochs = 2
    is_cuda = torch.cuda.is_available()

    # Horovod: initialize library.
    hvd.init()
    torch.manual_seed(42)

    if is_cuda:
        # Horovod: pin GPU to local rank.
        torch.cuda.set_device(hvd.local_rank())
        torch.cuda.manual_seed(42)
    
    # Horovod: limit # of CPU threads to be used per worker.
    torch.set_num_threads(1)

    kwargs = {'num_workers': 1, 'pin_memory': True} if is_cuda else {}
    # When supported, use 'forkserver' to spawn dataloader workers instead of 'fork' to prevent
    # issues with Infiniband implementations that are not fork-safe
    if (kwargs.get('num_workers', 0) > 0 and hasattr(mp, '_supports_context') and
            mp._supports_context and 'forkserver' in mp.get_all_start_methods()):
        kwargs['multiprocessing_context'] = 'forkserver'

    train_dataset = \
        datasets.MNIST('data-%d' % hvd.rank(), train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ]))

    # Horovod: use DistributedSampler to partition the training data.
    train_sampler = torch.utils.data.distributed.DistributedSampler(
        train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=batch, sampler=train_sampler, **kwargs)

    model = Net()

    # By default, Adasum doesn't need scaling up learning rate.
    lr_scaler = hvd.size()

    if is_cuda:
        # Move model to GPU.
        model.cuda()
        # If using GPU Adasum allreduce, scale learning rate by local_size.
        if hvd.nccl_built():
            lr_scaler = hvd.local_size()

    # Horovod: scale learning rate by lr_scaler.
    optimizer = optim.SGD(model.parameters(), lr=0.01 * lr_scaler,
                          momentum=0.5)

    # Horovod: broadcast parameters & optimizer state.
    hvd.broadcast_parameters(model.state_dict(), root_rank=0)
    hvd.broadcast_optimizer_state(optimizer, root_rank=0)

    # Horovod: (optional) compression algorithm.
    compression = hvd.Compression.none

    # Horovod: wrap optimizer with DistributedOptimizer.
    optimizer = hvd.DistributedOptimizer(optimizer,
                                         named_parameters=model.named_parameters(),
                                         compression=compression,
                                         op=hvd.Average)

    for epoch in range(1, epochs + 1):
        train(optimizer, epoch, is_cuda, model, train_sampler, train_loader)
        test(is_cuda, model, test_sampler, test_loader)

Create Spark Session

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("PyTorchHorovodOnSpark").getOrCreate()

Run distributed training

import horovod.spark
horovod.spark.run(train_hvd, verbose=2)

Shutdown Spark workers
```
spark.stop()
```

PyTorch

Use PyTorch with DC/OS Data Science Engine

PyTorch local machine learning

PyTorch Distributed Learning with Horovod on Spark