Bolts¶
PyTorch Lightning Bolts, is our official collection of prebuilt models across many research domains.
pip install lightning-bolts
In bolts we have:
A collection of pretrained state-of-the-art models.
A collection of models designed to bootstrap your research.
A collection of callbacks, transforms, full datasets.
All models work on CPUs, TPUs, GPUs and 16-bit precision.
Quality control¶
The Lightning community builds bolts and contributes them to Bolts. The lightning team guarantees that contributions are:
Rigorously Tested (CPUs, GPUs, TPUs).
Rigorously Documented.
Standardized via PyTorch Lightning.
Optimized for speed.
Checked for correctness.
Example 1: Pretrained, prebuilt models¶
from pl_bolts.models import VAE, GPT2, ImageGPT, PixelCNN
from pl_bolts.models.self_supervised import AMDIM, CPCV2, SimCLR, MocoV2
from pl_bolts.models import LinearRegression, LogisticRegression
from pl_bolts.models.gans import GAN
from pl_bolts.callbacks import PrintTableMetricsCallback
from pl_bolts.datamodules import FashionMNISTDataModule, CIFAR10DataModule, ImagenetDataModule
Example 2: Extend for faster research¶
Bolts are contributed with benchmarks and continuous-integration tests. This means you can trust the implementations and use them to bootstrap your research much faster.
from pl_bolts.models import ImageGPT
from pl_bolts.self_supervised import SimCLR
class VideoGPT(ImageGPT):
def training_step(self, batch, batch_idx):
x, y = batch
x = _shape_input(x)
logits = self.gpt(x)
simclr_features = self.simclr(x)
# -----------------
# do something new with GPT logits + simclr_features
# -----------------
loss = self.criterion(logits.view(-1, logits.size(-1)), x.view(-1).long())
self.log("loss", loss)
return loss
Example 3: Callbacks¶
We also have a collection of callbacks.
from pl_bolts.callbacks import PrintTableMetricsCallback
import pytorch_lightning as pl
trainer = pl.Trainer(callbacks=[PrintTableMetricsCallback()])
# loss│train_loss│val_loss│epoch
# ──────────────────────────────
# 2.2541470527648926│2.2541470527648926│2.2158432006835938│0