Training Models Using PyTorch Lightning

How to Train models using Deep Lake and PyTorch Lightning

How to Train models using Deep Lake and PyTorch Lightning

This tutorial is also available as a Colab Notebook.

Deep Lake's integration with PyTorch can also be used to train models using an integration with PyTorch Lightning, a popular open-source high-level interface for PyTorch.


At a high-level, Deep Lake is connected to PyTorch lightning by passing the Deep Lake's PyTorch dataloader to any PyTorch Lightning API that expects a dataloader parameter, such as, train_dataloaders = deeplake_dataloader). The only caveats are:

  • Deep Lake handles distributed training via it's distributed parameter in the .pytorch() method. Therefore, the PyTorch Lightning Trainer class should be initialized with replace_sampler_ddp = False.

Example Code

This tutorial uses PyTorch Lightning to execute the identical training workflow that is shown here in PyTorch.

Data Preprocessing

The first step is to load the dataset for training. This tutorial uses the Fashion MNIST dataset that has already been converted into Deep Leake format. It is a simple image classification dataset that categorizes images by clothing type (trouser, shirt, etc.)

import deeplake
from PIL import Image
import torch
from torchvision import transforms, models
import pytorch_lightning as pl

# Connect to the training and testing datasets
ds_train = deeplake.load('hub://activeloop/fashion-mnist-train')
ds_val = deeplake.load('hub://activeloop/fashion-mnist-test')

The next step is to define a transformation function that will process the data and convert it into a format that can be passed into a deep learning model. In this particular example, torchvision.transforms is used as a part of the transformation pipeline that performs operations such as normalization and image augmentation (rotation).

tform = transforms.Compose([
    transforms.RandomRotation(20), # Image augmentation
    transforms.ToTensor(), # Must convert to pytorch tensor for subsequent operations to run
    transforms.Normalize([0.5], [0.5]),

You can now create a PyTorch dataloader that connects the Deep Lake dataset to the PyTorch model using the provided method ds.pytorch(). This method automatically applies the transformation function and takes care of random shuffling (if desired). The num_workers parameter can be used to parallelize data preprocessing, which is critical for ensuring that preprocessing does not bottleneck the overall training workflow.

The transform input is a dictionary where the key is the tensor name and the value is the transformation function that should be applied to that tensor. If a specific tensor's data does not need to be returned, it should be omitted from the keys. If the transformation function is set as None, the input tensor is converted to a torch tensor without additional modification.

batch_size = 32

# Since torchvision transforms expect PIL images, we use the 'pil' decode_method for the 'images' tensor. This is much faster than running ToPILImage inside the transform
train_loader = ds_train.pytorch(num_workers = 0, shuffle = True, transform = {'images': tform, 'labels': None}, batch_size = batch_size, decode_method = {'images': 'pil'})
val_loader = ds_val.pytorch(num_workers = 0, transform = {'images': tform, 'labels': None}, batch_size = batch_size, decode_method = {'images': 'pil'})

Model and LightningModule Definition

This tutorial uses a pre-trained ResNet18 neural network from the torchvision.models module, converted to a single-channel network for grayscale images. The LightningModule organizes the training code.

# Use a pre-trained ResNet18
def get_model(num_classes):
    model = models.resnet18(pretrained=True)

    # Convert model to grayscale
    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)

    # Update the fully connected layer based on the number of classes in the dataset
    model.fc = torch.nn.Linear(model.fc.in_features, num_classes)

    return model
class FashionMnistModule(pl.LightningModule):
    def __init__(self, num_classes):
            num_classes: Number of classes in the dataset and model

        # Create the model
        self.model = get_model(num_classes)

        # Create loss module
        self.loss_module = torch.nn.CrossEntropyLoss()

    def forward(self, imgs):
        return self.model(imgs)

    def configure_optimizers(self):
        return torch.optim.SGD(self.model.parameters(), lr=0.001, momentum=0.1)   

    def training_step(self, batch, batch_idx):
        images = batch['images']
        labels = torch.squeeze(batch['labels'])

        preds = self.model(images)
        loss = self.loss_module(preds, labels)
        acc = (preds.argmax(dim=-1) == labels).float().mean()

        self.log("train_acc", acc, on_step=True, on_epoch=True)
        self.log("train_loss", loss)
        return loss 

    def validation_step(self, batch, batch_idx):

        images = batch['images']
        labels = torch.squeeze(batch['labels'])
        preds = self.model(images).argmax(dim=-1)
        acc = (labels == preds).float().mean()

        # Log the valdation accuracy to the progress bar at the end of each epoch
        self.log("val_acc", acc, on_epoch=True, prog_bar=True)

Training the Model

PyTorchLightning takes care of the training loop, so the remaining steps are to initialize the Trainer and call the .fit() method using the training and validation dataloaders.

trainer = pl.Trainer(max_epochs = 3), train_dataloaders = train_loader, val_dataloaders = val_loader)

Congrats! You successfully trained an image classification model using PyTorch Lightning while streaming data directly from the cloud! 🎉

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