fit ( autoencoder, DataLoader ( train ), DataLoader ( val )) Advanced features ToTensor ()) train, val = random_split ( dataset, ) autoencoder = LitAutoEncoder () trainer = pl. getcwd (), download = True, transform = transforms. Forward defines how the LightningModule behaves during inference/prediction. Note: Training_step defines the training loop. parameters (), lr = 1e-3 ) return optimizer log ( "train_loss", loss ) return loss def configure_optimizers ( self ): optimizer = torch. It is independent of forward x, y = batch x = x. encoder ( x ) return embedding def training_step ( self, batch, batch_idx ): # training_step defines the train loop. Linear ( 128, 28 * 28 )) def forward ( self, x ): # in lightning, forward defines the prediction/inference actions embedding = self. LightningModule ): def _init_ ( self ): super (). Step 1: Add these imports import os import torch from torch import nn import torch.nn.functional as F from torchvision.datasets import MNIST from import DataLoader, random_split from torchvision import transforms import pytorch_lightning as pl Step 2: Define a LightningModule (nn.Module subclass)Ī LightningModule defines a full system (ie: a GAN, autoencoder, BERT or a simple Image Classifier). Simple installation from PyPI pip install pytorch-lightning Current build statuses System / PyTorch ver. Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs and against major Python and PyTorch versions. Once you do this, you can train on multiple-GPUs, TPUs, CPUs, IPUs, HPUs and even in 16-bit precision without changing your code! Data (use PyTorch DataLoaders or organize them into a LightningDataModule).Non-essential research code (logging, etc. Engineering code (you delete, and is handled by the Trainer).Lightning forces the following structure to your code which makes it reusable and shareable:
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