Using PyTorch Lightning with Tune#

try-anyscale-quickstart

PyTorch Lightning is a framework which brings structure into training PyTorch models. It aims to avoid boilerplate code, so you don’t have to write the same training loops all over again when building a new model.

../../_images/pytorch_lightning_full.png

The main abstraction of PyTorch Lightning is the LightningModule class, which should be extended by your application. There is a great post on how to transfer your models from vanilla PyTorch to Lightning.

The class structure of PyTorch Lightning makes it very easy to define and tune model parameters. This tutorial will show you how to use Tune with PyTorch Lightning. Notably, the LightningModule does not have to be altered at all for this - so you can use it plug and play for your existing models, assuming their parameters are configurable!

Note

To run this example, you will need to install the following:

$ pip install -q "ray[tune]" torch torchvision pytorch_lightning

PyTorch Lightning classifier for MNIST#

Let’s first start with the basic PyTorch Lightning implementation of an MNIST classifier. This classifier does not include any tuning code at this point.

First, we run some imports:

import os
import torch
import tempfile
import pytorch_lightning as pl
import torch.nn.functional as F
from filelock import FileLock
from torchmetrics import Accuracy
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST
from torchvision import transforms

/home/ray/anaconda3/lib/python3.11/site-packages/lightning_utilities/core/imports.py:14: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources
/home/ray/anaconda3/lib/python3.11/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
  _torch_pytree._register_pytree_node(
/home/ray/anaconda3/lib/python3.11/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
  _torch_pytree._register_pytree_node(

Our example builds on the MNIST example from the blog post we mentioned before. We adapted the original model and dataset definitions into MNISTClassifier and MNISTDataModule.

class MNISTClassifier(pl.LightningModule):
    def __init__(self, config):
        super(MNISTClassifier, self).__init__()
        self.accuracy = Accuracy(task="multiclass", num_classes=10, top_k=1)
        self.layer_1_size = config["layer_1_size"]
        self.layer_2_size = config["layer_2_size"]
        self.lr = config["lr"]

        # mnist images are (1, 28, 28) (channels, width, height)
        self.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)
        self.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)
        self.layer_3 = torch.nn.Linear(self.layer_2_size, 10)
        self.eval_loss = []
        self.eval_accuracy = []

    def cross_entropy_loss(self, logits, labels):
        return F.nll_loss(logits, labels)

    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

    def training_step(self, train_batch, batch_idx):
        x, y = train_batch
        logits = self.forward(x)
        loss = self.cross_entropy_loss(logits, y)
        accuracy = self.accuracy(logits, y)

        self.log("ptl/train_loss", loss)
        self.log("ptl/train_accuracy", accuracy)
        return loss

    def validation_step(self, val_batch, batch_idx):
        x, y = val_batch
        logits = self.forward(x)
        loss = self.cross_entropy_loss(logits, y)
        accuracy = self.accuracy(logits, y)
        self.eval_loss.append(loss)
        self.eval_accuracy.append(accuracy)
        return {"val_loss": loss, "val_accuracy": accuracy}

    def on_validation_epoch_end(self):
        avg_loss = torch.stack(self.eval_loss).mean()
        avg_acc = torch.stack(self.eval_accuracy).mean()
        self.log("ptl/val_loss", avg_loss, sync_dist=True)
        self.log("ptl/val_accuracy", avg_acc, sync_dist=True)
        self.eval_loss.clear()
        self.eval_accuracy.clear()

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
        return optimizer


class MNISTDataModule(pl.LightningDataModule):
    def __init__(self, batch_size=128):
        super().__init__()
        self.data_dir = tempfile.mkdtemp()
        self.batch_size = batch_size
        self.transform = transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        )

    def setup(self, stage=None):
        with FileLock(f"{self.data_dir}.lock"):
            mnist = MNIST(
                self.data_dir, train=True, download=True, transform=self.transform
            )
            self.mnist_train, self.mnist_val = random_split(mnist, [55000, 5000])

            self.mnist_test = MNIST(
                self.data_dir, train=False, download=True, transform=self.transform
            )

    def train_dataloader(self):
        return DataLoader(self.mnist_train, batch_size=self.batch_size, num_workers=4)

    def val_dataloader(self):
        return DataLoader(self.mnist_val, batch_size=self.batch_size, num_workers=4)

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=self.batch_size, num_workers=4)

default_config = {
    "layer_1_size": 128,
    "layer_2_size": 256,
    "lr": 1e-3,
}

Define a training function that creates model, DataModule, and the PyTorch Lightning Trainer.

from ray.tune.integration.pytorch_lightning import TuneReportCheckpointCallback

def train_func(config):
    dm = MNISTDataModule(batch_size=config["batch_size"])
    model = MNISTClassifier(config)

    trainer = pl.Trainer(
        devices="auto",
        accelerator="auto",
        callbacks=[TuneReportCheckpointCallback()],
        enable_progress_bar=False,
    )
    trainer.fit(model, datamodule=dm)

Tuning the model parameters#

The parameters above should give you a good accuracy of over 90% already. However, we might improve on this simply by changing some of the hyperparameters. For instance, maybe we get an even higher accuracy if we used a smaller learning rate and larger middle layer size.

Instead of manually loop through all the parameter combinitions, let’s use Tune to systematically try out parameter combinations and find the best performing set.

First, we need some additional imports:

from ray import tune
from ray.tune.schedulers import ASHAScheduler

Configuring the search space#

Now we configure the parameter search space. We would like to choose between different layer dimensions, learning rate, and batch sizes. The learning rate should be sampled uniformly between 0.0001 and 0.1. The tune.loguniform() function is syntactic sugar to make sampling between these different orders of magnitude easier, specifically we are able to also sample small values. Similarly for tune.choice(), which samples from all the provided options.

search_space = {
    "layer_1_size": tune.choice([32, 64, 128]),
    "layer_2_size": tune.choice([64, 128, 256]),
    "lr": tune.loguniform(1e-4, 1e-1),
    "batch_size": tune.choice([32, 64]),
}

Selecting a scheduler#

In this example, we use an Asynchronous Hyperband scheduler. This scheduler decides at each iteration which trials are likely to perform badly, and stops these trials. This way we don’t waste any resources on bad hyperparameter configurations.

# The maximum training epochs
num_epochs = 5

# Number of samples from parameter space
num_samples = 10

If you have more resources available, you can modify the above parameters accordingly. e.g. more epochs, more parameter samples.

scheduler = ASHAScheduler(max_t=num_epochs, grace_period=1, reduction_factor=2)

Training with GPUs#

We can specify the number of resources, including GPUs, that Tune should request for each trial.

train_fn_with_resources = tune.with_resources(train_func, resources={"CPU": 1, "GPU": 1})

Putting it together#

Lastly, we need to create a Tuner() object and start Ray Tune with tuner.fit(). The full code looks like this:

def tune_mnist_asha(num_samples=10):
    scheduler = ASHAScheduler(max_t=num_epochs, grace_period=1, reduction_factor=2)

    tuner = tune.Tuner(
        train_fn_with_resources,
        param_space=search_space,
        tune_config=tune.TuneConfig(
            metric="ptl/val_accuracy",
            mode="max",
            num_samples=num_samples,
            scheduler=scheduler,
        ),
        run_config=tune.RunConfig(
            checkpoint_config=tune.CheckpointConfig(
                num_to_keep=2,
                checkpoint_score_attribute="ptl/val_accuracy",
                checkpoint_score_order="max",
            ),
        ),
    )
    return tuner.fit()


results = tune_mnist_asha(num_samples=num_samples)

results.get_best_result(metric="ptl/val_accuracy", mode="max")

Result(
  metrics={'ptl/train_loss': 0.001267582061700523, 'ptl/train_accuracy': 1.0, 'ptl/val_loss': 0.1036270260810852, 'ptl/val_accuracy': 0.9721123576164246},
  path='/home/ray/ray_results/train_func_2025-09-23_13-37-55/train_func_2f534_00006_6_batch_size=64,layer_1_size=64,layer_2_size=64,lr=0.0020_2025-09-23_13-37-55',
  filesystem='local',
  checkpoint=Checkpoint(filesystem=local, path=/home/ray/ray_results/train_func_2025-09-23_13-37-55/train_func_2f534_00006_6_batch_size=64,layer_1_size=64,layer_2_size=64,lr=0.0020_2025-09-23_13-37-55/checkpoint_000004)
)

In the example above, Tune runs 10 trials with different hyperparameter configurations.

As you can see in the training_iteration column, trials with a high loss (and low accuracy) have been terminated early. The best performing trial used batch_size=64, layer_1_size=128, layer_2_size=256, and lr=0.0037.

More PyTorch Lightning Examples#