Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more.Designed with multi-billion parameter models in mind.All the device logic boilerplate is handled for you.Use state-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box.Easily switch from running on CPU to GPU (Apple Silicon, CUDA, …), TPU, multi-GPU or even multi-node training. loss.backward() + fabric.backward(loss) input, target = input.to(device), target.to(device) + dataloader = tup_dataloaders(dataloader) + model, optimizer = tup(model, optimizer)ĭataloader = DataLoader(PyTorchDataset(.). + fabric = L.Fabric(accelerator="cuda", devices=8, strategy="ddp") + fabric.launch() - device = "cuda" if _available() else "cpu DataLoader( val))įrom import DataLoader, Dataset # - # Step 3: Train # - autoencoder = LitAutoEncoder() getcwd(), 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, difussion model, autoencoder, or simple image classifier). # main.py # ! pip install torchvision import os, torch, torch.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |