manual_backward ( loss_a, opt_a ) opt_a. optimizers ( use_pl_optimizer = True ) loss_a =. automatic_optimization = False def training_step ( self, batch, batch_idx ): # access your optimizers with use_pl_optimizer=False. LightningModule ): def _init_ ( self ): super (). name, input_sample, export_params = True ) Pro-level control of optimization (advanced users)įor complex/professional level work, you have optional full control of the optimizers. NamedTemporaryFile ( suffix = ".onnx", delete = False ) as tmpfile : autoencoder. to_torchscript (), "model.pt" ) Export to ONNX (production use) autoencoder = LitAutoEncoder () input_sample = torch. and dozens more EarlyStopping es = EarlyStopping ( monitor = "val_loss" ) trainer = Trainer ( callbacks = ) Checkpointing checkpointing = ModelCheckpoint ( monitor = "val_loss" ) trainer = Trainer ( callbacks = ) Export to torchscript (JIT) (production use) # torchscript autoencoder = LitAutoEncoder () torch. MLFlowLogger ()) # neptune trainer = Trainer ( logger = loggers. CometLogger ()) # mlflow trainer = Trainer ( logger = loggers. WandbLogger ()) # comet trainer = Trainer ( logger = loggers. Highlighted feature code snippets # 8 GPUs # no code changes needed trainer = Trainer ( max_epochs = 1, accelerator = "gpu", devices = 8 ) # 256 GPUs trainer = Trainer ( max_epochs = 1, accelerator = "gpu", devices = 8, num_nodes = 32 ) Train on TPUs without code changes # no code changes needed trainer = Trainer ( accelerator = "tpu", devices = 8 ) 16-bit precision # no code changes needed trainer = Trainer ( precision = 16 ) Experiment managers from pytorch_lightning import loggers # tensorboard trainer = Trainer ( logger = TensorBoardLogger ( "logs/" )) # weights and biases trainer = Trainer ( logger = loggers. Lightning has over 40+ advanced features designed for professional AI research at scale. 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. 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 and TPUs 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!
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