Question: In tensorflow keras, when I'm training a model, at each epoch it print the accuracy and the loss, I want to do the same thing using pythorch lightning. I already create my module but I don't know how to do it. import torch import torch.nn as nn from residual_block import ResidualBlock import pytorch_lightning as pl from torchmetrics import Accuracy class ResNet(pl.LightningModule): def __init__(self, block, layers, image_channels, num_classes, learning_rate): super(ResNet, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d( image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer( block, layers[0], intermediate_channels=64, stride=1) self.layer2 = self._make_layer( block, layers[1], intermediate_channels=128, stride=2) self.layer3 = self._make_layer( block, layers[2], intermediate_channels=256, stride=2) self.layer4 = self._make_layer( block, layers[3], intermediate_channels=512, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * 4, num_classes) self.learning_rate = learning_rate self.train_accuracy = Accuracy() self.val_accuracy = Accuracy() self.test_accuracy = Accuracy() def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.reshape(x.shape[0], -1) x = self.fc(x) return x def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) return optimizer def training_step(self, train_batch, batch_idx): images, labels = train_batch outputs = self(images) criterion = nn.CrossEntropyLoss() loss = criterion(outputs, labels) self.train_accuracy(outputs, labels) self.log('train_loss', loss) self.log('train_accuracy', self.train_accuracy) return loss def validation_step(self, val_batch, batch_idx): images, labels = val_batch outputs = self(images) criterion = nn.CrossEntropyLoss() loss = criterion(outputs, labels) self.val_accuracy(outputs, labels) self.log('val_loss', loss) self.log('val_accuracy', self.val_accuracy) def test_step(self, test_batch, batch_idx): images, labels = test_batch outputs = self(images) criterion = nn.CrossEntropyLoss() loss = criterion(outputs, labels) self.test_accuracy(outputs, labels) self.log('test_loss', loss) self.log('test_accuracy', self.test_accuracy) def _make_layer(self, block, num_residual_blocks, intermediate_channels, stride): identity_downsample = None layers = [] if stride != 1 or self.in_channels != intermediate_channels * 4: identity_downsample = nn.Sequential(nn.Conv2d(self.in_channels, intermediate_channels * 4, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(intermediate_channels * 4),) layers.append( block(self.in_channels, intermediate_channels, identity_downsample, stride)) self.in_channels = intermediate_channels * 4 for i in range(num_residual_blocks - 1): layers.append(block(self.in_channels, intermediate_channels)) return nn.Sequential(*layers) @classmethod def ResNet50(cls, img_channels, num_classes, learning_rate): return ResNet(ResidualBlock, [3, 4, 6, 3], img_channels, num_classes, learning_rate) @classmethod def ResNet101(cls, img_channels, num_classes, learning_rate): return ResNet(ResidualBlock, [3, 4, 23, 3], img_channels, num_classes, learning_rate) @classmethod def ResNet152(cls, img_channels, num_classes, learning_rate): return ResNet(ResidualBlock, [3, 8, 36, 3], img_channels, num_classes, learning_rate)  I just want to print the training and validation accuracy and loss at the end of each epoch.

Answer: self.log("train_loss", loss, prog_bar=True, on_step=False, on_epoch=True) The above code logs train_loss to the progress bar. https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html#automatic-logging Or you can use this if on one device: def training_step(self, batch, batch_idx): ... loss = nn.functional.mse_loss(x_hat, x) return loss def training_epoch_end(self, outputs) -> None: loss = sum(output['loss'] for output in outputs) / len(outputs) print(loss)  Multiple GPUs: def training_epoch_end(self, outputs) -> None: gathered = self.all_gather(outputs) if self.global_rank == 0: # print(gathered) loss = sum(output['loss'].mean() for output in gathered) / len(outputs) print(loss.item())  Updated December 2023: I don't think it works anymore since Lightning 2.0 and I have to use TorchMetric to customize my metrics.