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.. testsetup:: *
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.core.datamodule import LightningDataModule
from pytorch_lightning.trainer.trainer import Trainer
import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from torch.utils.data import random_split
.. _3-steps:
####################
Lightning in 3 steps
####################
**In this guide we'll show you how to organize your PyTorch code into Lightning in 3 simple steps.**
Organizing your code with PyTorch Lightning makes your code:
* Keep all the flexibility (this is all pure PyTorch), but removes a ton of boilerplate
* More readable by decoupling the research code from the engineering
* Easier to reproduce
* Less error prone by automating most of the training loop and tricky engineering
* Scalable to any hardware without changing your model
----------
Here's a 2 minute conversion guide for PyTorch projects:
.. raw:: html
<video width="100%" controls autoplay src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/pl_quick_start_full.m4v"></video>
----------
*********************************
Step 0: Install PyTorch Lightning
*********************************
You can install using `pip <https://pypi.org/project/pytorch-lightning/>`_
.. code-block:: bash
pip install pytorch-lightning
Or with `conda <https://anaconda.org/conda-forge/pytorch-lightning>`_ (see how to install conda `here <https://docs.conda.io/projects/conda/en/latest/user-guide/install/>`_):
.. code-block:: bash
conda install pytorch-lightning -c conda-forge
You could also use conda environments
.. code-block:: bash
conda activate my_env
pip install pytorch-lightning
----------
******************************
Step 1: Define LightningModule
******************************
.. code-block::
import os
import torch
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision import transforms
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from torch.utils.data import random_split
class LitModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 10)
def forward(self, x):
x = x.view(x.size(0), -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
return x
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
result = pl.TrainResult(loss)
return result
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
result = pl.EvalResult(checkpoint_on=loss)
result.log('val_loss', loss)
return result
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
result = pl.EvalResult()
result.log('test_loss', loss)
return result
The :class:`~pytorch_lightning.core.LightningModule` holds your research code:
- The Train loop
- The Validation loop
- The Test loop
- The Model + system architecture
- The Optimizer
A :class:`~pytorch_lightning.core.LightningModule` is a :class:`torch.nn.Module` but with added functionality.
It organizes your research code into :ref:`hooks`.
In the snippet above we override the basic hooks, but a full list of hooks to customize can be found under :ref:`hooks`.
You can use your :class:`~pytorch_lightning.core.LightningModule` just like a PyTorch model.
.. code-block:: python
model = LitModel()
model.eval()
y_hat = model(x)
model.anything_you_can_do_with_pytorch()
More details in :ref:`lightning-module` docs.
Convert your PyTorch Module to Lightning
========================================
1. Move your computational code
-------------------------------
Move the model architecture and forward pass to your :class:`~pytorch_lightning.core.LightningModule`.
.. code-block::
class LitModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 10)
def forward(self, x):
x = x.view(x.size(0), -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
return x
2. Move the optimizer(s) and schedulers
---------------------------------------
Move your optimizers to :func:`pytorch_lightning.core.LightningModule.configure_optimizers` hook. Make sure to use the hook parameters (self in this case).
.. code-block::
class LitModel(pl.LightningModule):
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
3. Find the train loop "meat"
-----------------------------
Lightning automates most of the trining for you, the epoch and batch iterations, all you need to keep is the training step logic. This should go into :func:`pytorch_lightning.core.LightningModule.training_step` hook (make sure to use the hook parameters, self in this case):
.. code-block::
class LitModel(pl.LightningModule):
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
return loss
4. Find the val loop "meat"
-----------------------------
Lightning automates the validation (enabling gradients in the train loop and disabling in eval). To add an (optional) validation loop add logic to :func:`pytorch_lightning.core.LightningModule.validation_step` hook (make sure to use the hook parameters, self in this case):
.. testcode::
class LitModel(LightningModule):
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
val_loss = F.cross_entropy(y_hat, y)
return val_loss
5. Find the test loop "meat"
-----------------------------
You might also need an optional test loop. Add the following callback to your :class:`~pytorch_lightning.core.LightningModule`
.. code-block::
class LitModel(pl.LightningModule):
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
result = pl.EvalResult()
result.log('test_loss', loss)
return result
.. note:: The test loop is not automated in Lightning. You will need to specifically call test (this is done so you don't use the test set by mistake).
6. Remove any .cuda() or to.device() calls
------------------------------------------
Your :class:`~pytorch_lightning.core.LightningModule` can automatically run on any hardware!
7. Wrap loss in a TrainResult/EvalResult
----------------------------------------
Instead of returning the loss you can also use :class:`~pytorch_lightning.core.step_result.TrainResult` and :class:`~pytorch_lightning.core.step_result.EvalResult`, plain Dict objects that give you options for logging on every step and/or at the end of the epoch.
It also allows logging to the progress bar (by setting prog_bar=True). Read more in :ref:`result`.
.. code-block::
class LitModel(pl.LightningModule):
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
result = pl.TrainResult(minimize=loss)
# Add logging to progress bar (note that refreshing the progress bar too frequently
# in Jupyter notebooks or Colab may freeze your UI)
result.log('train_loss', loss, prog_bar=True)
return result
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
# Checkpoint model based on validation loss
result = pl.EvalResult(checkpoint_on=loss)
result.log('val_loss', loss)
return result
8. Override default callbacks
-----------------------------
A :class:`~pytorch_lightning.core.LightningModule` handles advances cases by allowing you to override any critical part of training
via :ref:`hooks` that are called on your :class:`~pytorch_lightning.core.LightningModule`.
.. code-block::
class LitModel(pl.LightningModule):
def backward(self, trainer, loss, optimizer, optimizer_idx):
loss.backward()
def optimizer_step(self, epoch, batch_idx,
optimizer, optimizer_idx,
second_order_closure,
on_tpu, using_native_amp, using_lbfgs):
optimizer.step()
For certain train/val/test loops, you may wish to do more than just logging. In this case,
you can also implement `__epoch_end` which gives you the output for each step
Here's the motivating Pytorch example:
.. code-block:: python
validation_step_outputs = []
for batch_idx, batch in val_dataloader():
out = validation_step(batch, batch_idx)
validation_step_outputs.append(out)
validation_epoch_end(validation_step_outputs)
And the lightning equivalent
.. code-block::
class LitModel(pl.LightningModule):
def validation_step(self, batch, batch_idx):
loss = ...
predictions = ...
result = pl.EvalResult(checkpoint_on=loss)
result.log('val_loss', loss)
result.predictions = predictions
def validation_epoch_end(self, validation_step_outputs):
all_val_losses = validation_step_outputs.val_loss
all_predictions = validation_step_outputs.predictions
----------
**********************************
Step 2: Fit with Lightning Trainer
**********************************
.. code-block::
# dataloaders
dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
train, val = random_split(dataset, [55000, 5000])
train_loader = DataLoader(train)
val_loader = DataLoader(val)
# init model
model = LitModel()
# most basic trainer, uses good defaults (auto-tensorboard, checkpoints, logs, and more)
trainer = pl.Trainer()
trainer.fit(model, train_loader, val_loader)
Init :class:`~pytorch_lightning.core.LightningModule`, your PyTorch dataloaders, and then the PyTorch Lightning :class:`~pytorch_lightning.trainer.Trainer`.
The :class:`~pytorch_lightning.trainer.Trainer` will automate:
* The epoch iteration
* The batch iteration
* The calling of optimizer.step()
* :ref:`weights-loading`
* Logging to Tensorboard (see :ref:`loggers` options)
* :ref:`multi-gpu-training` support
* :ref:`tpu`
* :ref:`16-bit` support
All automated code is rigorously tested and benchmarked.
Check out more flags in the :ref:`trainer` docs.
Using CPUs/GPUs/TPUs
====================
It's trivial to use CPUs, GPUs or TPUs in Lightning. There's NO NEED to change your code, simply change the :class:`~pytorch_lightning.trainer.Trainer` options.
.. code-block:: python
# train on 1024 CPUs across 128 machines
trainer = pl.Trainer(
num_processes=8,
num_nodes=128
)
.. code-block:: python
# train on 1 GPU
trainer = pl.Trainer(gpus=1)
.. code-block:: python
# train on 256 GPUs
trainer = pl.Trainer(
gpus=8,
num_nodes=32
)
.. code-block:: python
# Multi GPU with mixed precision
trainer = pl.Trainer(gpus=2, precision=16)
.. code-block:: python
# Train on TPUs
trainer = pl.Trainer(tpu_cores=8)
Without changing a SINGLE line of your code, you can now do the following with the above code:
.. code-block:: python
# train on TPUs using 16 bit precision with early stopping
# using only half the training data and checking validation every quarter of a training epoch
trainer = pl.Trainer(
tpu_cores=8,
precision=16,
early_stop_callback=True,
limit_train_batches=0.5,
val_check_interval=0.25
)
************************
Step 3: Define Your Data
************************
Lightning works with pure PyTorch DataLoaders
.. code-block:: python
train_dataloader = DataLoader(...)
val_dataloader = DataLoader(...)
trainer.fit(model, train_dataloader, val_dataloader)
Optional: DataModule
====================
DataLoader and data processing code tends to end up scattered around.
Make your data code more reusable by organizing
it into a :class:`~pytorch_lightning.core.datamodule.LightningDataModule`
.. code-block:: python
class MNISTDataModule(pl.LightningDataModule):
def __init__(self, batch_size=32):
super().__init__()
self.batch_size = batch_size
# When doing distributed training, Datamodules have two optional arguments for
# granular control over download/prepare/splitting data:
# OPTIONAL, called only on 1 GPU/machine
def prepare_data(self):
MNIST(os.getcwd(), train=True, download=True)
MNIST(os.getcwd(), train=False, download=True)
# OPTIONAL, called for every GPU/machine (assigning state is OK)
def setup(self, stage):
# transforms
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# split dataset
if stage == 'fit':
mnist_train = MNIST(os.getcwd(), train=True, transform=transform)
self.mnist_train, self.mnist_val = random_split(mnist_train, [55000, 5000])
if stage == 'test':
self.mnist_test = MNIST(os.getcwd(), train=False, transform=transform)
# return the dataloader for each split
def train_dataloader(self):
mnist_train = DataLoader(self.mnist_train, batch_size=self.batch_size)
return mnist_train
def val_dataloader(self):
mnist_val = DataLoader(self.mnist_val, batch_size=self.batch_size)
return mnist_val
def test_dataloader(self):
mnist_test = DataLoader(self.mnist_test, batch_size=self.batch_size)
return mnist_test
:class:`~pytorch_lightning.core.datamodule.LightningDataModule` is designed to enable sharing and reusing data splits
and transforms across different projects. It encapsulates all the steps needed to process data: downloading,
tokenizing, processing etc.
Now you can simply pass your :class:`~pytorch_lightning.core.datamodule.LightningDataModule` to
the :class:`~pytorch_lightning.trainer.Trainer`:
.. code-block::
# init model
model = LitModel()
# init data
dm = MNISTDataModule()
# train
trainer = pl.Trainer()
trainer.fit(model, dm)
# test
trainer.test(datamodule=dm)
DataModules are specifically useful for building models based on data. Read more on :ref:`data-modules`.
**********
Learn more
**********
That's it! Once you build your module, data, and call trainer.fit(), Lightning trainer calls each loop at the correct time as needed.
You can then boot up your logger or tensorboard instance to view training logs
.. code-block:: bash
tensorboard --logdir ./lightning_logs
---------------
Advanced Lightning Features
===========================
Once you define and train your first Lightning model, you might want to try other cool features like
- :ref:`loggers`
- `Automatic checkpointing <https://pytorch-lightning.readthedocs.io/en/stable/weights_loading.html>`_
- `Automatic early stopping <https://pytorch-lightning.readthedocs.io/en/stable/early_stopping.html>`_
- `Add custom callbacks <https://pytorch-lightning.readthedocs.io/en/stable/callbacks.html>`_ (self-contained programs that can be reused across projects)
- `Dry run mode <https://pytorch-lightning.readthedocs.io/en/stable/debugging.html#fast-dev-run>`_ (Hit every line of your code once to see if you have bugs, instead of waiting hours to crash on validation ;)
- `Automatically overfit your model for a sanity test <https://pytorch-lightning.readthedocs.io/en/stable/debugging.html?highlight=overfit#make-model-overfit-on-subset-of-data>`_
- `Automatic truncated-back-propagation-through-time <https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.training_loop.html?highlight=truncated#truncated-backpropagation-through-time>`_
- `Automatically scale your batch size <https://pytorch-lightning.readthedocs.io/en/stable/training_tricks.html?highlight=batch%20size#auto-scaling-of-batch-size>`_
- `Automatically find a good learning rate <https://pytorch-lightning.readthedocs.io/en/stable/lr_finder.html>`_
- `Load checkpoints directly from S3 <https://pytorch-lightning.readthedocs.io/en/stable/weights_loading.html#checkpoint-loading>`_
- `Profile your code for speed/memory bottlenecks <https://pytorch-lightning.readthedocs.io/en/stable/profiler.html>`_
- `Scale to massive compute clusters <https://pytorch-lightning.readthedocs.io/en/stable/slurm.html>`_
- `Use multiple dataloaders per train/val/test loop <https://pytorch-lightning.readthedocs.io/en/stable/multiple_loaders.html>`_
- `Use multiple optimizers to do Reinforcement learning or even GANs <https://pytorch-lightning.readthedocs.io/en/stable/optimizers.html?highlight=multiple%20optimizers#use-multiple-optimizers-like-gans>`_
Or read our :ref:`introduction-guide` to learn more!
-------------
Masterclass
===========
Go pro by tunning in to our Masterclass! New episodes every week.
.. image:: _images/general/PTL101_youtube_thumbnail.jpg
:width: 500
:align: center
:alt: Masterclass
:target: https://www.youtube.com/playlist?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2
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