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| :orphan: | ||
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| mnist_ptl_mini | ||
| ~~~~~~~~~~~~~~ | ||
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| .. literalinclude:: /../../python/ray/tune/examples/mnist_ptl_mini.py |
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| import torch | ||
| from torch.nn import functional as F | ||
| import pytorch_lightning as pl | ||
| from pl_bolts.datamodules import MNISTDataModule | ||
| import os | ||
| from ray.tune.integration.pytorch_lightning import TuneReportCallback | ||
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| import tempfile | ||
| from ray import tune | ||
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| class LightningMNISTClassifier(pl.LightningModule): | ||
| def __init__(self, config, data_dir=None): | ||
| super(LightningMNISTClassifier, self).__init__() | ||
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| self.data_dir = data_dir or os.getcwd() | ||
| self.lr = config["lr"] | ||
| layer_1, layer_2 = config["layer_1"], config["layer_2"] | ||
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| # mnist images are (1, 28, 28) (channels, width, height) | ||
| self.layer_1 = torch.nn.Linear(28 * 28, layer_1) | ||
| self.layer_2 = torch.nn.Linear(layer_1, layer_2) | ||
| self.layer_3 = torch.nn.Linear(layer_2, 10) | ||
| self.accuracy = pl.metrics.Accuracy() | ||
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| def forward(self, x): | ||
| batch_size, channels, width, height = x.size() | ||
| x = x.view(batch_size, -1) | ||
| x = self.layer_1(x) | ||
| x = torch.relu(x) | ||
| x = self.layer_2(x) | ||
| x = torch.relu(x) | ||
| x = self.layer_3(x) | ||
| x = torch.log_softmax(x, dim=1) | ||
| return x | ||
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| def configure_optimizers(self): | ||
| return torch.optim.Adam(self.parameters(), lr=self.lr) | ||
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| def training_step(self, train_batch, batch_idx): | ||
| x, y = train_batch | ||
| logits = self.forward(x) | ||
| loss = F.nll_loss(logits, y) | ||
| acc = self.accuracy(logits, y) | ||
| self.log("ptl/train_loss", loss) | ||
| self.log("ptl/train_accuracy", acc) | ||
| return loss | ||
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| def validation_step(self, val_batch, batch_idx): | ||
| x, y = val_batch | ||
| logits = self.forward(x) | ||
| loss = F.nll_loss(logits, y) | ||
| acc = self.accuracy(logits, y) | ||
| return {"val_loss": loss, "val_accuracy": acc} | ||
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| def validation_epoch_end(self, outputs): | ||
| avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean() | ||
| avg_acc = torch.stack([x["val_accuracy"] for x in outputs]).mean() | ||
| self.log("ptl/val_loss", avg_loss) | ||
| self.log("ptl/val_accuracy", avg_acc) | ||
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| def train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0): | ||
| model = LightningMNISTClassifier(config, data_dir) | ||
| dm = MNISTDataModule( | ||
| data_dir=data_dir, num_workers=1, batch_size=config["batch_size"]) | ||
| metrics = {"loss": "ptl/val_loss", "acc": "ptl/val_accuracy"} | ||
| trainer = pl.Trainer( | ||
| max_epochs=num_epochs, | ||
| gpus=num_gpus, | ||
| progress_bar_refresh_rate=0, | ||
| callbacks=[TuneReportCallback(metrics, on="validation_end")]) | ||
| trainer.fit(model, dm) | ||
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| def tune_mnist(num_samples=10, num_epochs=10, gpus_per_trial=0): | ||
| data_dir = os.path.join(tempfile.gettempdir(), "mnist_data_") | ||
| # Download data | ||
| MNISTDataModule(data_dir=data_dir).prepare_data() | ||
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| config = { | ||
| "layer_1": tune.choice([32, 64, 128]), | ||
| "layer_2": tune.choice([64, 128, 256]), | ||
| "lr": tune.loguniform(1e-4, 1e-1), | ||
| "batch_size": tune.choice([32, 64, 128]), | ||
| } | ||
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| trainable = tune.with_parameters( | ||
| train_mnist_tune, | ||
| data_dir=data_dir, | ||
| num_epochs=num_epochs, | ||
| num_gpus=gpus_per_trial) | ||
| tune.run( | ||
| trainable, | ||
| resources_per_trial={ | ||
| "cpu": 1, | ||
| "gpu": gpus_per_trial | ||
| }, | ||
| metric="loss", | ||
| mode="min", | ||
| config=config, | ||
| num_samples=num_samples, | ||
| name="tune_mnist") | ||
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| if __name__ == "__main__": | ||
| import argparse | ||
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| "--smoke-test", action="store_true", help="Finish quickly for testing") | ||
| args, _ = parser.parse_known_args() | ||
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| if args.smoke_test: | ||
| tune_mnist(num_samples=1, num_epochs=1, gpus_per_trial=0) | ||
| else: | ||
| tune_mnist(num_samples=10, num_epochs=10, gpus_per_trial=0) |
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