pytorch-lightning
PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.
Decision gist · record as of 2026-08-14
Yes. PyTorch Lightning is a mature, actively maintained framework (31287 stars, recent release) with zero known vulnerabilities, permissive licensing, and low install friction. It's worth installing if you want to reduce training boilerplate and gain automatic support for distributed training, mixed precision, and experiment management without learning a new deep-learning abstraction. If you're writing simple single-GPU scripts, the overhead may not justify it; for research or production workflows involving scaling, logging, or checkpointing, it's a clear win.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and torch as a runtime dependency.
- Low friction install with a pure-Python wheel.
- Active maintenance with a recent release (79 days ago) and strong repository signals (31287 stars, last commit 2026-08-09).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for both research and production deployments.
last release 2026-05-27 (79 days) · last repo commit 2026-08-09 · 31,287 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 11,232,102 downloads/mo, #1,406 on PyPI
Alternatives
Verify before relying
pip install pytorch-lightning
import pytorch_lightning as pl
from torch.utils.data import DataLoader
class LitModel(pl.LightningModule):
def training_step(self, batch, batch_idx):
# your training logic
return loss
trainer = pl.Trainer()
trainer.fit(model, DataLoader(train_data), DataLoader(val_data))- Whether the 40+ advanced features mentioned in the description are all stable or some remain experimental.
- Performance overhead compared to raw PyTorch for simple single-GPU training workflows.
- Compatibility guarantees across the PyTorch versions tested (1.12, 1.13, 2.0, 2.1).
What it is and what it does
PyTorch Lightning is a structured wrapper around PyTorch that enforces separation of research code (the LightningModule), engineering code (handled by the Trainer), and auxiliary concerns like logging and callbacks. It lets you write a model once and then scale it to multiple GPUs, TPUs, or CPUs by changing only Trainer configuration—no changes to your model code required. The package handles distributed training setup, mixed-precision training, checkpointing, early stopping, and integration with experiment loggers (TensorBoard, Weights & Biases, MLflow, and others) automatically.
The design philosophy is to eliminate boilerplate: you define training_step, validation_step, and configure_optimizers in your LightningModule, organize data into DataLoaders or a LightningDataModule, and let the Trainer orchestrate the rest. It's built on top of torch, tqdm, PyYAML, fsspec, torchmetrics, packaging, typing-extensions, and lightning-utilities, all of which are standard dependencies in the PyTorch ecosystem. The package is actively maintained, has no known vulnerabilities, and supports Python 3.10 through 3.13.
Use it for
- Train a model on a single GPU, then scale to 8 GPUs or 256 GPUs across multiple nodes without modifying model code.
- Manage experiment tracking and logging across multiple runs with built-in logger integrations.
- Implement early stopping and model checkpointing with minimal boilerplate code.
- Export trained models to TorchScript or ONNX for production inference.
- Run the same training code on TPUs, GPUs, or CPUs by changing only the Trainer accelerator parameter.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
PyTorch Lightning is a mature, actively maintained framework (31287 stars, recent release) with zero known vulnerabilities, permissive licensing, and low install friction. It's worth installing if you want to reduce training boilerplate and gain automatic support for distributed training, mixed precision, and experiment management without learning a new deep-learning abstraction. If you're writing simple single-GPU scripts, the overhead may not justify it; for research or production workflows involving scaling, logging, or checkpointing, it's a clear win.
Install
pytorch-lightning on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance with a recent release (79 days ago) and strong repository signals (31287 stars, last commit 2026-08-09). Depends on torch and eight other runtime packages, all standard in the ML ecosystem.
Requires Python >=3.10 and torch as a runtime dependency.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for both research and production deployments.
Quickstart
pip install pytorch-lightning
import pytorch_lightning as pl
from torch.utils.data import DataLoader
class LitModel(pl.LightningModule):
def training_step(self, batch, batch_idx):
# your training logic
return loss
trainer = pl.Trainer()
trainer.fit(model, DataLoader(train_data), DataLoader(val_data))Verify before relying
- Whether the 40+ advanced features mentioned in the description are all stable or some remain experimental.
- Performance overhead compared to raw PyTorch for simple single-GPU training workflows.
- Compatibility guarantees across the PyTorch versions tested (1.12, 1.13, 2.0, 2.1).
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagestorchtqdmPyYAMLfsspectorchmetricspackagingtyping-extensionslightning-utilities |
| Maintenance | Actively maintained 79 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 11,232,102 / month, #1,406 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: Information Analysis |
Evidence: pytorch_lightning-2.6.5-py3-none-any.whl
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See also lightning · nv-one-logger-pytorch-lightning-integration · trainer · coqui-tts-trainer · litdata · agentlightning · pytorch-forecasting · accelerate · torchrunx · torchinfo