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import json
import logging
import os
import sys
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))
class Net(nn.Module):
# Based on https://github.com/pytorch/examples/blob/master/mnist/main.py
def __init__(self):
logger.info("Create neural network module")
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, dim=1)
def _get_train_data_loader(training_dir, is_distributed, batch_size, **kwargs):
logger.info("Get train data loader")
dataset = datasets.MNIST(
training_dir,
train=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
download=False, # True sets a dependency on an external site for our canaries.
)
train_sampler = (
torch.utils.data.distributed.DistributedSampler(dataset) if is_distributed else None
)
train_loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=train_sampler is None,
sampler=train_sampler,
**kwargs
)
return train_sampler, train_loader
def _get_test_data_loader(training_dir, **kwargs):
logger.info("Get test data loader")
return torch.utils.data.DataLoader(
datasets.MNIST(
training_dir,
train=False,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
download=False, # True sets a dependency on an external site for our canaries.
),
batch_size=1000,
shuffle=True,
**kwargs
)
def _average_gradients(model):
# Gradient averaging.
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM, group=0)
param.grad.data /= size
def train(args):
world_size = len(args.hosts)
is_distributed = world_size > 1
logger.debug("Number of hosts {}. Distributed training - {}".format(world_size, is_distributed))
use_cuda = args.num_gpus > 0
logger.debug("Number of gpus available - {}".format(args.num_gpus))
kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {}
device = torch.device("cuda" if use_cuda else "cpu")
if is_distributed:
# Initialize the distributed environment.
backend = "gloo"
os.environ["WORLD_SIZE"] = str(world_size)
host_rank = args.hosts.index(args.current_host)
dist.init_process_group(backend=backend, rank=host_rank, world_size=world_size)
logger.info(
"Initialized the distributed environment: '{}' backend on {} nodes. ".format(
backend, dist.get_world_size()
)
+ "Current host rank is {}. Is cuda available: {}. Number of gpus: {}".format(
dist.get_rank(), torch.cuda.is_available(), args.num_gpus
)
)
# set the seed for generating random numbers
seed = 1
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed(seed)
train_sampler, train_loader = _get_train_data_loader(
args.data_dir, is_distributed, args.batch_size, **kwargs
)
test_loader = _get_test_data_loader(args.data_dir, **kwargs)
logger.debug(
"Processes {}/{} ({:.0f}%) of train data".format(
len(train_loader.sampler),
len(train_loader.dataset),
100.0 * len(train_loader.sampler) / len(train_loader.dataset),
)
)
logger.debug(
"Processes {}/{} ({:.0f}%) of test data".format(
len(test_loader.sampler),
len(test_loader.dataset),
100.0 * len(test_loader.sampler) / len(test_loader.dataset),
)
)
model = Net().to(device)
if is_distributed and use_cuda:
# multi-machine multi-gpu case
logger.debug("Multi-machine multi-gpu: using DistributedDataParallel.")
model = torch.nn.parallel.DistributedDataParallel(model)
elif use_cuda:
# single-machine multi-gpu case
logger.debug("Single-machine multi-gpu: using DataParallel().cuda().")
model = torch.nn.DataParallel(model)
else:
# single-machine or multi-machine cpu case
logger.debug("Single-machine/multi-machine cpu: using DataParallel.")
model = torch.nn.DataParallel(model)
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.5)
log_interval = 100
for epoch in range(1, args.epochs + 1):
if is_distributed:
train_sampler.set_epoch(epoch)
model.train()
for batch_idx, (data, target) in enumerate(train_loader, 1):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
if is_distributed and not use_cuda:
# average gradients manually for multi-machine cpu case only
_average_gradients(model)
optimizer.step()
if batch_idx % log_interval == 0:
logger.debug(
"Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}".format(
epoch,
batch_idx * len(data),
len(train_loader.sampler),
100.0 * batch_idx / len(train_loader),
loss.item(),
)
)
accuracy = test(model, test_loader, device)
save_model(model, args.model_dir)
logger.debug("Overall test accuracy: {};".format(accuracy))
def test(model, test_loader, device):
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, size_average=False).item() # sum up batch loss
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100.0 * correct / len(test_loader.dataset)
logger.debug(
"Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
test_loss, correct, len(test_loader.dataset), accuracy
)
)
return accuracy
def model_fn(model_dir):
model = torch.nn.DataParallel(Net())
with open(os.path.join(model_dir, "model.pth"), "rb") as f:
model.load_state_dict(torch.load(f))
return model
def save_model(model, model_dir):
logger.info("Saving the model.")
path = os.path.join(model_dir, "model.pth")
# recommended way from http://pytorch.org/docs/master/notes/serialization.html
torch.save(model.state_dict(), path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--epochs", type=int, default=1, metavar="N")
parser.add_argument("--batch-size", type=int, default=64, metavar="N")
# Container environment
parser.add_argument("--hosts", type=list, default=json.loads(os.environ["SM_HOSTS"]))
parser.add_argument("--current-host", type=str, default=os.environ["SM_CURRENT_HOST"])
parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
parser.add_argument("--data-dir", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
parser.add_argument("--num-gpus", type=int, default=os.environ["SM_NUM_GPUS"])
parser.add_argument("--num-cpus", type=int, default=os.environ["SM_NUM_CPUS"])
train(parser.parse_args())
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