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#

# Licensed to the Apache Software Foundation (ASF) under one or more

# contributor license agreements. See the NOTICE file distributed with

# this work for additional information regarding copyright ownership.

# The ASF licenses this file to You under the Apache License, Version 2.0

# (the "License"); you may not use this file except in compliance with

# the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License 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.

#

# type: ignore

batch_size = 100

num_epochs = 3

momentum = 0.5

log_interval = 100

import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

from torchvision import datasets, transforms

import tempfile

import shutil

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)

def train_one_epoch(model, data_loader, optimizer, epoch):

model.train()

for batch_idx, (data, target) in enumerate(data_loader):

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(data_loader) * len(data),

100.0 * batch_idx / len(data_loader),

loss.item(),

)

)

def train(learning_rate):

import torch.distributed as dist

from torch.nn.parallel import DistributedDataParallel as DDP

from torch.utils.data.distributed import DistributedSampler

print("Running distributed training")

dist.init_process_group("gloo")

temp_dir = tempfile.mkdtemp()

train_dataset = datasets.MNIST(

temp_dir,

train=True,

download=True,

transform=transforms.Compose(

[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]

),

)

train_sampler = DistributedSampler(dataset=train_dataset)

data_loader = torch.utils.data.DataLoader(

train_dataset, batch_size=batch_size, sampler=train_sampler

)

model = Net()

ddp_model = DDP(model)

optimizer = optim.SGD(ddp_model.parameters(), lr=learning_rate, momentum=momentum)

for epoch in range(1, num_epochs + 1):

train_one_epoch(ddp_model, data_loader, optimizer, epoch)

dist.destroy_process_group()

shutil.rmtree(temp_dir)

if __name__ == "__main__":

import argparse

parser = argparse.ArgumentParser()

parser.add_argument("lr", help="learning_rate", default=0.001)

args = parser.parse_args()

print("learning rate chosen: ", float(args.lr))

train(float(args.lr))