File size: 7,752 Bytes
476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import print_function
import argparse
import os
import time
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
from smdistributed.dataparallel.torch.parallel.distributed import DistributedDataParallel as DDP
import smdistributed.dataparallel.torch.distributed as dist
dist.init_process_group()
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.relu(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
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 and args.rank == 0:
print(
"Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
epoch,
batch_idx * len(data) * args.world_size,
len(train_loader.dataset),
100.0 * batch_idx / len(train_loader),
loss.item(),
)
)
if args.verbose:
print("Batch", batch_idx, "from rank", args.rank)
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), 100.0 * correct / len(test_loader.dataset)
)
)
def main():
# 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=14,
metavar="N",
help="number of epochs to train (default: 14)",
)
parser.add_argument(
"--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)"
)
parser.add_argument(
"--gamma",
type=float,
default=0.7,
metavar="M",
help="Learning rate step gamma (default: 0.7)",
)
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",
)
parser.add_argument(
"--save-model", action="store_true", default=False, help="For Saving the current Model"
)
parser.add_argument(
"--verbose",
action="store_true",
default=False,
help="For displaying SM Distributed Data Parallel-specific logs",
)
parser.add_argument(
"--data-path",
type=str,
default=os.environ["SM_CHANNEL_TRAINING"],
help="Path for downloading the MNIST dataset",
)
args = parser.parse_args()
args.world_size = dist.get_world_size()
args.rank = rank = dist.get_rank()
args.local_rank = local_rank = dist.get_local_rank()
args.lr = 1.0
args.batch_size //= args.world_size // 8
args.batch_size = max(args.batch_size, 1)
data_path = args.data_path
if args.verbose:
print(
"Hello from rank",
rank,
"of local_rank",
local_rank,
"in world size of",
args.world_size,
)
if not torch.cuda.is_available():
raise Exception(
"Must run SM Distributed Data Parallel MNIST example on CUDA-capable devices."
)
torch.manual_seed(args.seed)
device = torch.device("cuda")
if local_rank == 0:
train_dataset = datasets.MNIST(
data_path,
train=True,
download=False, # True sets a dependency on an external site for our tests.
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
)
else:
time.sleep(8)
train_dataset = datasets.MNIST(
data_path,
train=True,
download=False,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=args.world_size, rank=rank
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True,
sampler=train_sampler,
)
if rank == 0:
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(
data_path,
train=False,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
),
batch_size=args.test_batch_size,
shuffle=True,
)
model = DDP(Net().to(device))
torch.cuda.set_device(local_rank)
model.cuda(local_rank)
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
if rank == 0:
test(model, device, test_loader)
scheduler.step()
if args.save_model:
torch.save(model.state_dict(), "mnist_cnn.pt")
if __name__ == "__main__":
main()
|