![]() *Codecov is > 90%+ but build delays may show less 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. # - # Step 3: Train # - autoencoder = LitAutoEncoder() MNIST( ".", download = True, transform = tv. Return optimizer # - # Step 2: Define data # - dataset = tv. Return loss def configure_optimizers( self): It is independent of forward x, y = batch x = x. Return embedding def training_step( self, batch, batch_idx): # in lightning, forward defines the prediction/inference actions embedding = self. functional as F import lightning as L # - # Step 1: Define a LightningModule # - # A LightningModule (nn.Module subclass) defines a full *system* # (ie: an LLM, diffusion model, autoencoder, or simple image classifier). ![]() # main.py # ! pip install torchvision import torch, torch. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |