PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. It makes your code neatly organized and provides lots of useful features, ...
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. - GitHub - PyTorchLightning/pytorch-lightning: The ...
05.01.2022 · Strategy Registry. The Strategy Registry is experimental and subject to change. Lightning includes a registry that holds information about Training strategies and allows for the registration of new custom strategies. The Strategies are assigned strings that identify them, such as "ddp", "deepspeed_stage_2_offload", and so on.
import os import torch import torch.nn as nn import torch.nn.functional as F import torchvision from pl_bolts.datamodules import CIFAR10DataModule from pl_bolts.transforms.dataset_normalizations import cifar10_normalization from pytorch_lightning import LightningModule, Trainer, seed_everything from pytorch_lightning.callbacks import ...
GitHub - PyTorchLightning/lightning-bolts: Toolbox of models, callbacks, and datasets for AI/ML ... Deep Learning components for extending PyTorch Lightning ...
PyTorch Lightning DataModules¶. Author: PL team License: CC BY-SA Generated: 2021-12-04T16:53:01.674205 This notebook will walk you through how to start using Datamodules. With the release of pytorch-lightning version 0.9.0, we have included a new class called LightningDataModule to help you decouple data related hooks from your LightningModule.
GitHub - PyTorchLightning/lightning-tutorials: Collection of Pytorch lightning tutorial form as rich scripts automatically transformed to ipython notebooks.
04.01.2022 · An easy/swift-to-adapt PyTorch-Lighting template. 套壳模板，简单易用，稍改原来Pytorch代码，即可适配Lightning。You can translate your previous Pytorch code much easier using this template, and keep your freedom to edit all the functions as …
06.01.2022 · *Codecov is > 90%+ but build delays may show less PyTorch Lightning is just organized PyTorch Lightning Design Philosophy Continuous Integration How To Use Step 0: Install Install with optional dependencies Conda Install stable 1.5.x Install bleeding-edge - future 1.6 Step 1: Add these imports Step 2: Define a LightningModule (nn.Module subclass) Step 3: …
import os import torch import torch.nn as nn import torch.nn.functional as F import torchvision from pl_bolts.datamodules import CIFAR10DataModule from pl_bolts.transforms.dataset_normalizations import cifar10_normalization from pytorch_lightning import LightningModule, Trainer, seed_everything from pytorch_lightning.callbacks import ...