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[tune] Update MNIST Example (ray-project#4991)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,189 +1,134 @@ | ||
| # Original Code here: | ||
| # https://github.com/pytorch/examples/blob/master/mnist/main.py | ||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| import numpy as np | ||
| import argparse | ||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| import torch.optim as optim | ||
| from torchvision import datasets, transforms | ||
|
|
||
| # 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=1, | ||
| metavar="N", | ||
| help="number of epochs to train (default: 1)") | ||
| parser.add_argument( | ||
| "--lr", | ||
| type=float, | ||
| default=0.01, | ||
| metavar="LR", | ||
| help="learning rate (default: 0.01)") | ||
| parser.add_argument( | ||
| "--momentum", | ||
| type=float, | ||
| default=0.5, | ||
| metavar="M", | ||
| help="SGD momentum (default: 0.5)") | ||
| parser.add_argument( | ||
| "--no-cuda", | ||
| action="store_true", | ||
| default=False, | ||
| help="disables CUDA training") | ||
| parser.add_argument( | ||
| "--seed", | ||
| type=int, | ||
| default=1, | ||
| metavar="S", | ||
| help="random seed (default: 1)") | ||
| parser.add_argument( | ||
| "--smoke-test", action="store_true", help="Finish quickly for testing") | ||
|
|
||
|
|
||
| def train_mnist(args, config, reporter): | ||
| vars(args).update(config) | ||
| args.cuda = not args.no_cuda and torch.cuda.is_available() | ||
|
|
||
| torch.manual_seed(args.seed) | ||
| if args.cuda: | ||
| torch.cuda.manual_seed(args.seed) | ||
|
|
||
| kwargs = {"num_workers": 1, "pin_memory": True} if args.cuda else {} | ||
| import ray | ||
| from ray import tune | ||
| from ray.tune import track | ||
| from ray.tune.schedulers import AsyncHyperBandScheduler | ||
|
|
||
| # Change these values if you want the training to run quicker or slower. | ||
| EPOCH_SIZE = 512 | ||
| TEST_SIZE = 256 | ||
|
|
||
|
|
||
| class Net(nn.Module): | ||
| def __init__(self, config): | ||
| super(Net, self).__init__() | ||
| self.conv1 = nn.Conv2d(1, 3, kernel_size=3) | ||
| self.fc = nn.Linear(192, 10) | ||
|
|
||
| def forward(self, x): | ||
| x = F.relu(F.max_pool2d(self.conv1(x), 3)) | ||
| x = x.view(-1, 192) | ||
| x = self.fc(x) | ||
| return F.log_softmax(x, dim=1) | ||
|
|
||
|
|
||
| def train(model, optimizer, train_loader, device): | ||
| model.train() | ||
| for batch_idx, (data, target) in enumerate(train_loader): | ||
| if batch_idx * len(data) > EPOCH_SIZE: | ||
| return | ||
| data, target = data.to(device), target.to(device) | ||
| optimizer.zero_grad() | ||
| output = model(data) | ||
| loss = F.nll_loss(output, target) | ||
| loss.backward() | ||
| optimizer.step() | ||
|
|
||
|
|
||
| def test(model, data_loader, device): | ||
| model.eval() | ||
| correct = 0 | ||
| total = 0 | ||
| with torch.no_grad(): | ||
| for batch_idx, (data, target) in enumerate(data_loader): | ||
| if batch_idx * len(data) > TEST_SIZE: | ||
| break | ||
| data, target = data.to(device), target.to(device) | ||
| outputs = model(data) | ||
| _, predicted = torch.max(outputs.data, 1) | ||
| total += target.size(0) | ||
| correct += (predicted == target).sum().item() | ||
|
|
||
| return correct / total | ||
|
|
||
|
|
||
| def get_data_loaders(): | ||
| mnist_transforms = transforms.Compose( | ||
| [transforms.ToTensor(), | ||
| transforms.Normalize((0.1307, ), (0.3081, ))]) | ||
|
|
||
| train_loader = torch.utils.data.DataLoader( | ||
| datasets.MNIST( | ||
| "~/data", | ||
| train=True, | ||
| download=False, | ||
| transform=transforms.Compose([ | ||
| transforms.ToTensor(), | ||
| transforms.Normalize((0.1307, ), (0.3081, )) | ||
| ])), | ||
| batch_size=args.batch_size, | ||
| shuffle=True, | ||
| **kwargs) | ||
| "~/data", train=True, download=True, transform=mnist_transforms), | ||
| batch_size=64, | ||
| shuffle=True) | ||
| test_loader = torch.utils.data.DataLoader( | ||
| datasets.MNIST( | ||
| "~/data", | ||
| train=False, | ||
| transform=transforms.Compose([ | ||
| transforms.ToTensor(), | ||
| transforms.Normalize((0.1307, ), (0.3081, )) | ||
| ])), | ||
| batch_size=args.test_batch_size, | ||
| shuffle=True, | ||
| **kwargs) | ||
|
|
||
| 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, dim=1) | ||
|
|
||
| model = Net() | ||
| if args.cuda: | ||
| model.cuda() | ||
| datasets.MNIST("~/data", train=False, transform=mnist_transforms), | ||
| batch_size=64, | ||
| shuffle=True) | ||
| return train_loader, test_loader | ||
|
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||
|
|
||
| def train_mnist(config): | ||
| use_cuda = config.get("use_gpu") and torch.cuda.is_available() | ||
| device = torch.device("cuda" if use_cuda else "cpu") | ||
| train_loader, test_loader = get_data_loaders() | ||
| model = Net(config).to(device) | ||
|
|
||
| optimizer = optim.SGD( | ||
| model.parameters(), lr=args.lr, momentum=args.momentum) | ||
|
|
||
| def train(epoch): | ||
| model.train() | ||
| for batch_idx, (data, target) in enumerate(train_loader): | ||
| if args.cuda: | ||
| data, target = data.cuda(), target.cuda() | ||
| optimizer.zero_grad() | ||
| output = model(data) | ||
| loss = F.nll_loss(output, target) | ||
| loss.backward() | ||
| optimizer.step() | ||
|
|
||
| def test(): | ||
| model.eval() | ||
| test_loss = 0 | ||
| correct = 0 | ||
| with torch.no_grad(): | ||
| for data, target in test_loader: | ||
| if args.cuda: | ||
| data, target = data.cuda(), target.cuda() | ||
| output = model(data) | ||
| # sum up batch loss | ||
| test_loss += F.nll_loss(output, target, reduction="sum").item() | ||
| # get the index of the max log-probability | ||
| pred = output.argmax(dim=1, keepdim=True) | ||
| correct += pred.eq( | ||
| target.data.view_as(pred)).long().cpu().sum() | ||
|
|
||
| test_loss = test_loss / len(test_loader.dataset) | ||
| accuracy = correct.item() / len(test_loader.dataset) | ||
| reporter(mean_loss=test_loss, mean_accuracy=accuracy) | ||
|
|
||
| for epoch in range(1, args.epochs + 1): | ||
| train(epoch) | ||
| test() | ||
| model.parameters(), lr=config["lr"], momentum=config["momentum"]) | ||
|
|
||
| while True: | ||
| train(model, optimizer, train_loader, device) | ||
| acc = test(model, test_loader, device) | ||
| track.log(mean_accuracy=acc) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| datasets.MNIST("~/data", train=True, download=True) | ||
| parser = argparse.ArgumentParser(description="PyTorch MNIST Example") | ||
| parser.add_argument( | ||
| "--cuda", | ||
| action="store_true", | ||
| default=False, | ||
| help="Enables GPU training") | ||
| parser.add_argument( | ||
| "--smoke-test", action="store_true", help="Finish quickly for testing") | ||
| parser.add_argument( | ||
| "--ray-redis-address", | ||
| help="Address of Ray cluster for seamless distributed execution.") | ||
| args = parser.parse_args() | ||
|
|
||
| import ray | ||
| from ray import tune | ||
| from ray.tune.schedulers import AsyncHyperBandScheduler | ||
|
|
||
| ray.init() | ||
| if args.ray_redis_address: | ||
| ray.init(redis_address=args.ray_redis_address) | ||
| sched = AsyncHyperBandScheduler( | ||
| time_attr="training_iteration", | ||
| metric="mean_loss", | ||
| mode="min", | ||
| max_t=400, | ||
| grace_period=20) | ||
| tune.register_trainable( | ||
| "TRAIN_FN", | ||
| lambda config, reporter: train_mnist(args, config, reporter)) | ||
| time_attr="training_iteration", metric="mean_accuracy") | ||
| tune.run( | ||
| "TRAIN_FN", | ||
| train_mnist, | ||
| name="exp", | ||
| scheduler=sched, | ||
| **{ | ||
| "stop": { | ||
| "mean_accuracy": 0.98, | ||
| "training_iteration": 1 if args.smoke_test else 20 | ||
| }, | ||
| "resources_per_trial": { | ||
| "cpu": 3, | ||
| "gpu": int(not args.no_cuda) | ||
| }, | ||
| "num_samples": 1 if args.smoke_test else 10, | ||
| "config": { | ||
| "lr": tune.uniform(0.001, 0.1), | ||
| "momentum": tune.uniform(0.1, 0.9), | ||
| } | ||
| stop={ | ||
| "mean_accuracy": 0.98, | ||
| "training_iteration": 5 if args.smoke_test else 20 | ||
| }, | ||
| resources_per_trial={ | ||
| "cpu": 2, | ||
| "gpu": int(args.cuda) | ||
| }, | ||
| num_samples=1 if args.smoke_test else 10, | ||
| config={ | ||
| "lr": tune.sample_from(lambda spec: 10**(-10 * np.random.rand())), | ||
| "momentum": tune.uniform(0.1, 0.9), | ||
| "use_gpu": int(args.cuda) | ||
| }) |