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).It is very easy to use and does a good job of staying out of your way, as it rests in the system tray and is integrated into the Windows context menu.Lightning forces the following structure to your code which makes it reusable and shareable:

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Instaling ImgDrive 2.0.5