| # |
| # 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)) |