chore: import upstream snapshot with attribution

python/ray/tune/examples/mnist_pytorch.py

@@ -0,0 +1,161 @@
+# Original Code here:
+# https://github.com/pytorch/examples/blob/master/mnist/main.py
+
+import argparse
+import os
+import tempfile
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from filelock import FileLock
+from torchvision import datasets, transforms
+
+import ray
+from ray import tune
+from ray.tune import Checkpoint
+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 ConvNet(nn.Module):
+    def __init__(self):
+        super(ConvNet, 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_func(model, optimizer, train_loader, device=None):
+    device = device or torch.device("cpu")
+    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_func(model, data_loader, device=None):
+    device = device or torch.device("cpu")
+    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(batch_size=64):
+    mnist_transforms = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
+
+    # We add FileLock here because multiple workers will want to
+    # download data, and this may cause overwrites since
+    # DataLoader is not threadsafe.
+    with FileLock(os.path.expanduser("~/data.lock")):
+        train_loader = torch.utils.data.DataLoader(
+            datasets.MNIST(
+                "~/data", train=True, download=True, transform=mnist_transforms
+            ),
+            batch_size=batch_size,
+            shuffle=True,
+        )
+        test_loader = torch.utils.data.DataLoader(
+            datasets.MNIST(
+                "~/data", train=False, download=True, transform=mnist_transforms
+            ),
+            batch_size=batch_size,
+            shuffle=True,
+        )
+    return train_loader, test_loader
+
+
+def train_mnist(config):
+    should_checkpoint = config.get("should_checkpoint", False)
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+    train_loader, test_loader = get_data_loaders()
+    model = ConvNet().to(device)
+
+    optimizer = optim.SGD(
+        model.parameters(), lr=config["lr"], momentum=config["momentum"]
+    )
+
+    while True:
+        train_func(model, optimizer, train_loader, device)
+        acc = test_func(model, test_loader, device)
+        metrics = {"mean_accuracy": acc}
+
+        # Report metrics (and possibly a checkpoint)
+        if should_checkpoint:
+            with tempfile.TemporaryDirectory() as tempdir:
+                torch.save(model.state_dict(), os.path.join(tempdir, "model.pt"))
+                tune.report(metrics, checkpoint=Checkpoint.from_directory(tempdir))
+        else:
+            tune.report(metrics)
+
+
+if __name__ == "__main__":
+    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"
+    )
+    args, _ = parser.parse_known_args()
+
+    ray.init(num_cpus=2 if args.smoke_test else None)
+
+    # for early stopping
+    sched = AsyncHyperBandScheduler()
+
+    resources_per_trial = {"cpu": 2, "gpu": int(args.cuda)}  # set this for GPUs
+    tuner = tune.Tuner(
+        tune.with_resources(train_mnist, resources=resources_per_trial),
+        tune_config=tune.TuneConfig(
+            metric="mean_accuracy",
+            mode="max",
+            scheduler=sched,
+            num_samples=1 if args.smoke_test else 50,
+        ),
+        run_config=tune.RunConfig(
+            name="exp",
+            stop={
+                "mean_accuracy": 0.98,
+                "training_iteration": 5 if args.smoke_test else 100,
+            },
+        ),
+        param_space={
+            "lr": tune.loguniform(1e-4, 1e-2),
+            "momentum": tune.uniform(0.1, 0.9),
+        },
+    )
+    results = tuner.fit()
+
+    print("Best config is:", results.get_best_result().config)
+
+    assert not results.errors