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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
.. _introduction_guide:
#########################
Step-by-step walk-through
#########################
This guide will walk you through the core pieces of PyTorch Lightning.
We'll accomplish the following:
- Implement an MNIST classifier.
- Use inheritance to implement an AutoEncoder
.. note:: Any DL/ML PyTorch project fits into the Lightning structure. Here we just focus on 3 types
of research to illustrate.
--------------
**************************
From MNIST to AutoEncoders
**************************
Installing Lightning
====================
Lightning is trivial to install. We recommend using conda environments
.. code-block:: bash
conda activate my_env
pip install pytorch-lightning
Or without conda environments, use pip.
.. code-block:: bash
pip install pytorch-lightning
Or conda.
.. code-block:: bash
conda install pytorch-lightning -c conda-forge
The research
============
The Model
---------
The :class:`~pytorch_lightning.core.LightningModule` holds all the core research ingredients:
- The model
- The optimizers
- The train/ val/ test steps
Let's first start with the model. In this case we'll design a 3-layer neural network.
.. testcode::
import torch
from torch.nn import functional as F
from torch import nn
from pytorch_lightning.core.lightning import LightningModule
class LitMNIST(LightningModule):
def __init__(self):
super().__init__()
# mnist images are (1, 28, 28) (channels, width, height)
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 256)
self.layer_3 = torch.nn.Linear(256, 10)
def forward(self, x):
batch_size, channels, width, height = x.size()
# (b, 1, 28, 28) -> (b, 1*28*28)
x = x.view(batch_size, -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
x = F.relu(x)
x = self.layer_3(x)
x = F.log_softmax(x, dim=1)
return x
Notice this is a :class:`~pytorch_lightning.core.LightningModule` instead of a ``torch.nn.Module``. A LightningModule is
equivalent to a pure PyTorch Module except it has added functionality. However, you can use it **EXACTLY** the same as you would a PyTorch Module.
.. testcode::
net = LitMNIST()
x = torch.randn(1, 1, 28, 28)
out = net(x)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: python
torch.Size([1, 10])
Now we add the training_step which has all our training loop logic
.. testcode:: python
class LitMNIST(LightningModule):
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return loss
Data
----
Lightning operates on pure dataloaders. Here's the PyTorch code for loading MNIST.
.. testcode::
:skipif: not TORCHVISION_AVAILABLE
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST
import os
from torchvision import datasets, transforms
# transforms
# prepare transforms standard to MNIST
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
# data
mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform)
mnist_train = DataLoader(mnist_train, batch_size=64)
.. testoutput::
:hide:
:skipif: os.path.isdir(os.path.join(os.getcwd(), 'MNIST')) or not TORCHVISION_AVAILABLE
Downloading ...
Extracting ...
Downloading ...
Extracting ...
Downloading ...
Extracting ...
Processing...
Done!
You can use DataLoaders in 3 ways:
1. Pass DataLoaders to .fit()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Pass in the dataloaders to the `.fit()` function.
.. code-block:: python
model = LitMNIST()
trainer = Trainer()
trainer.fit(model, mnist_train)
2. LightningModule DataLoaders
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For fast research prototyping, it might be easier to link the model with the dataloaders.
.. code-block:: python
class LitMNIST(pl.LightningModule):
def train_dataloader(self):
# transforms
# prepare transforms standard to MNIST
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
# data
mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform)
return DataLoader(mnist_train, batch_size=64)
def val_dataloader(self):
transforms = ...
mnist_val = ...
return DataLoader(mnist_val, batch_size=64)
def test_dataloader(self):
transforms = ...
mnist_test = ...
return DataLoader(mnist_test, batch_size=64)
DataLoaders are already in the model, no need to specify on .fit().
.. code-block:: python
model = LitMNIST()
trainer = Trainer()
trainer.fit(model)
3. DataModules (recommended)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Defining free-floating dataloaders, splits, download instructions and such can get messy.
In this case, it's better to group the full definition of a dataset into a `DataModule` which includes:
- Download instructions
- Processing instructions
- Split instructions
- Train dataloader
- Val dataloader(s)
- Test dataloader(s)
.. testcode:: python
class MyDataModule(LightningDataModule):
def __init__(self):
super().__init__()
self.train_dims = None
self.vocab_size = 0
def prepare_data(self):
# called only on 1 GPU
download_dataset()
tokenize()
build_vocab()
def setup(self):
# called on every GPU
vocab = load_vocab()
self.vocab_size = len(vocab)
self.train, self.val, self.test = load_datasets()
self.train_dims = self.train.next_batch.size()
def train_dataloader(self):
transforms = ...
return DataLoader(self.train, batch_size=64)
def val_dataloader(self):
transforms = ...
return DataLoader(self.val, batch_size=64)
def test_dataloader(self):
transforms = ...
return DataLoader(self.test, batch_size=64)
Using DataModules allows easier sharing of full dataset definitions.
.. code-block:: python
# use an MNIST dataset
mnist_dm = MNISTDatamodule()
model = LitModel(num_classes=mnist_dm.num_classes)
trainer.fit(model, mnist_dm)
# or other datasets with the same model
imagenet_dm = ImagenetDatamodule()
model = LitModel(num_classes=imagenet_dm.num_classes)
trainer.fit(model, imagenet_dm)
.. note:: ``prepare_data()`` is called on only one GPU in distributed training (automatically)
.. note:: ``setup()`` is called on every GPU (automatically)
Models defined by data
^^^^^^^^^^^^^^^^^^^^^^
When your models need to know about the data, it's best to process the data before passing it to the model.
.. code-block:: python
# init dm AND call the processing manually
dm = ImagenetDataModule()
dm.prepare_data()
dm.setup()
model = LitModel(out_features=dm.num_classes, img_width=dm.img_width, img_height=dm.img_height)
trainer.fit(model, dm)
1. use ``prepare_data()`` to download and process the dataset.
2. use ``setup()`` to do splits, and build your model internals
|
An alternative to using a DataModule is to defer initialization of the models modules to the ``setup`` method of your LightningModule as follows:
.. testcode::
class LitMNIST(LightningModule):
def __init__(self):
self.l1 = None
def prepare_data(self):
download_data()
tokenize()
def setup(self, step):
# step is either 'fit' or 'test' 90% of the time not relevant
data = load_data()
num_classes = data.classes
self.l1 = nn.Linear(..., num_classes)
Optimizer
---------
Next we choose what optimizer to use for training our system.
In PyTorch we do it as follows:
.. code-block:: python
from torch.optim import Adam
optimizer = Adam(LitMNIST().parameters(), lr=1e-3)
In Lightning we do the same but organize it under the :func:`~pytorch_lightning.core.LightningModule.configure_optimizers` method.
.. testcode::
class LitMNIST(LightningModule):
def configure_optimizers(self):
return Adam(self.parameters(), lr=1e-3)
.. note:: The LightningModule itself has the parameters, so pass in self.parameters()
However, if you have multiple optimizers use the matching parameters
.. testcode::
class LitMNIST(LightningModule):
def configure_optimizers(self):
return Adam(self.generator(), lr=1e-3), Adam(self.discriminator(), lr=1e-3)
Training step
-------------
The training step is what happens inside the training loop.
.. code-block:: python
for epoch in epochs:
for batch in data:
# TRAINING STEP
# ....
# TRAINING STEP
loss.backward()
optimizer.step()
optimizer.zero_grad()
In the case of MNIST we do the following
.. code-block:: python
for epoch in epochs:
for batch in data:
# ------ TRAINING STEP START ------
x, y = batch
logits = model(x)
loss = F.nll_loss(logits, y)
# ------ TRAINING STEP END ------
loss.backward()
optimizer.step()
optimizer.zero_grad()
In Lightning, everything that is in the training step gets organized under the
:func:`~pytorch_lightning.core.LightningModule.training_step` function in the LightningModule.
.. testcode::
class LitMNIST(LightningModule):
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return loss
Again, this is the same PyTorch code except that it has been organized by the LightningModule.
This code is not restricted which means it can be as complicated as a full seq-2-seq, RL loop, GAN, etc...
----------------
The engineering
===============
Training
--------
So far we defined 4 key ingredients in pure PyTorch but organized the code with the LightningModule.
1. Model.
2. Training data.
3. Optimizer.
4. What happens in the training loop.
|
For clarity, we'll recall that the full LightningModule now looks like this.
.. code-block:: python
class LitMNIST(LightningModule):
def __init__(self):
super().__init__()
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 256)
self.layer_3 = torch.nn.Linear(256, 10)
def forward(self, x):
batch_size, channels, width, height = x.size()
x = x.view(batch_size, -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
x = F.relu(x)
x = self.layer_3(x)
x = F.log_softmax(x, dim=1)
return x
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return loss
Again, this is the same PyTorch code, except that it's organized by the LightningModule.
Logging
^^^^^^^
To log to Tensorboard, your favorite logger, and/or the progress bar, use the
:func:`~~pytorch_lightning.core.lightning.LightningModule.log` method which can be called from
any method in the LightningModule.
.. code-block:: python
def training_step(self, batch, batch_idx):
self.log('my_metric', x)
The :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method has a few options:
- on_step (logs the metric at that step in training)
- on_epoch (automatically accumulates and logs at the end of the epoch)
- prog_bar (logs to the progress bar)
- logger (logs to the logger like Tensorboard)
Depending on where log is called from, Lightning auto-determines the correct mode for you. But of course
you can override the default behavior by manually setting the flags
.. note:: Setting on_epoch=True will accumulate your logged values over the full training epoch.
.. code-block:: python
def training_step(self, batch, batch_idx):
self.log('my_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
You can also use any method of your logger directly:
.. code-block:: python
def training_step(self, batch, batch_idx):
tensorboard = self.logger.experiment
tensorboard.any_summary_writer_method_you_want())
Once your training starts, you can view the logs by using your favorite logger or booting up the Tensorboard logs:
.. code-block:: bash
tensorboard --logdir ./lightning_logs
Which will generate automatic tensorboard logs.
.. figure:: /_images/mnist_imgs/mnist_tb.png
:alt: mnist CPU bar
:width: 500
|
But you can also use any of the :ref:`number of other loggers <loggers>` we support.
Train on CPU
^^^^^^^^^^^^
.. code-block:: python
from pytorch_lightning import Trainer
model = LitMNIST()
trainer = Trainer()
trainer.fit(model, train_loader)
You should see the following weights summary and progress bar
.. figure:: /_images/mnist_imgs/mnist_cpu_bar.png
:alt: mnist CPU bar
Train on GPU
^^^^^^^^^^^^
But the beauty is all the magic you can do with the trainer flags. For instance, to run this model on a GPU:
.. code-block:: python
model = LitMNIST()
trainer = Trainer(gpus=1)
trainer.fit(model, train_loader)
.. figure:: /_images/mnist_imgs/mnist_gpu.png
:alt: mnist GPU bar
Train on Multi-GPU
^^^^^^^^^^^^^^^^^^
Or you can also train on multiple GPUs.
.. code-block:: python
model = LitMNIST()
trainer = Trainer(gpus=8)
trainer.fit(model, train_loader)
Or multiple nodes
.. code-block:: python
# (32 GPUs)
model = LitMNIST()
trainer = Trainer(gpus=8, num_nodes=4, distributed_backend='ddp')
trainer.fit(model, train_loader)
Refer to the :ref:`distributed computing guide for more details <multi_gpu>`.
Train on TPUs
^^^^^^^^^^^^^
Did you know you can use PyTorch on TPUs? It's very hard to do, but we've
worked with the xla team to use their awesome library to get this to work
out of the box!
Let's train on Colab (`full demo available here <https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3>`_)
First, change the runtime to TPU (and reinstall lightning).
.. figure:: /_images/mnist_imgs/runtime_tpu.png
:alt: mnist GPU bar
:width: 400
.. figure:: /_images/mnist_imgs/restart_runtime.png
:alt: mnist GPU bar
:width: 400
|
Next, install the required xla library (adds support for PyTorch on TPUs)
.. code-block:: shell
!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
!python pytorch-xla-env-setup.py --version nightly --apt-packages libomp5 libopenblas-dev
In distributed training (multiple GPUs and multiple TPU cores) each GPU or TPU core will run a copy
of this program. This means that without taking any care you will download the dataset N times which
will cause all sorts of issues.
To solve this problem, make sure your download code is in the ``prepare_data`` method in the DataModule.
In this method we do all the preparation we need to do once (instead of on every gpu).
``prepare_data`` can be called in two ways, once per node or only on the root node
(``Trainer(prepare_data_per_node=False)``).
.. code-block:: python
class MNISTDataModule(LightningDataModule):
def __init__(self, batch_size=64):
super().__init__()
self.batch_size = batch_size
def prepare_data(self):
# download only
MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor())
def setup(self, stage):
# transform
transform=transforms.Compose([transforms.ToTensor()])
MNIST(os.getcwd(), train=True, download=False, transform=transform)
MNIST(os.getcwd(), train=False, download=False, transform=transform)
# train/val split
mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])
# assign to use in dataloaders
self.train_dataset = mnist_train
self.val_dataset = mnist_val
self.test_dataset = mnist_test
def train_dataloader(self):
return DataLoader(self.train_dataset, batch_size=self.batch_size)
def val_dataloader(self):
return DataLoader(self.val_dataset, batch_size=self.batch_size)
def test_dataloader(self):
return DataLoader(self.test_dataset, batch_size=self.batch_size)
The ``prepare_data`` method is also a good place to do any data processing that needs to be done only
once (ie: download or tokenize, etc...).
.. note:: Lightning inserts the correct DistributedSampler for distributed training. No need to add yourself!
Now we can train the LightningModule on a TPU without doing anything else!
.. code-block:: python
dm = MNISTDataModule()
model = LitMNIST()
trainer = Trainer(tpu_cores=8)
trainer.fit(model, dm)
You'll now see the TPU cores booting up.
.. figure:: /_images/mnist_imgs/tpu_start.png
:alt: TPU start
:width: 400
Notice the epoch is MUCH faster!
.. figure:: /_images/mnist_imgs/tpu_fast.png
:alt: TPU speed
:width: 600
----------------
.. include:: hyperparameters.rst
----------------
Validating
----------
For most cases, we stop training the model when the performance on a validation
split of the data reaches a minimum.
Just like the ``training_step``, we can define a ``validation_step`` to check whatever
metrics we care about, generate samples or add more to our logs.
.. code-block:: python
def validation_step(self, batch, batch_idx):
loss = MSE_loss(...)
self.log('val_loss', loss)
Now we can train with a validation loop as well.
.. code-block:: python
from pytorch_lightning import Trainer
model = LitMNIST()
trainer = Trainer(tpu_cores=8)
trainer.fit(model, train_loader, val_loader)
You may have noticed the words **Validation sanity check** logged. This is because Lightning runs 2 batches
of validation before starting to train. This is a kind of unit test to make sure that if you have a bug
in the validation loop, you won't need to potentially wait a full epoch to find out.
.. note:: Lightning disables gradients, puts model in eval mode and does everything needed for validation.
Val loop under the hood
^^^^^^^^^^^^^^^^^^^^^^^
Under the hood, Lightning does the following:
.. code-block:: python
model = Model()
model.train()
torch.set_grad_enabled(True)
for epoch in epochs:
for batch in data:
# ...
# train
# validate
model.eval()
torch.set_grad_enabled(False)
outputs = []
for batch in val_data:
x, y = batch # validation_step
y_hat = model(x) # validation_step
loss = loss(y_hat, x) # validation_step
outputs.append({'val_loss': loss}) # validation_step
total_loss = outputs.mean() # validation_epoch_end
Optional methods
^^^^^^^^^^^^^^^^
If you still need even more fine-grain control, define the other optional methods for the loop.
.. code-block:: python
def validation_step(self, batch, batch_idx):
preds = ...
return preds
def validation_epoch_end(self, val_step_outputs):
for pred in val_step_outputs:
# do something with all the predictions from each validation_step
----------------
Testing
-------
Once our research is done and we're about to publish or deploy a model, we normally want to figure out
how it will generalize in the "real world." For this, we use a held-out split of the data for testing.
Just like the validation loop, we define a test loop
.. code-block:: python
class LitMNIST(LightningModule):
def test_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
self.log('test_loss', loss)
However, to make sure the test set isn't used inadvertently, Lightning has a separate API to run tests.
Once you train your model simply call ``.test()``.
.. code-block:: python
from pytorch_lightning import Trainer
model = LitMNIST()
trainer = Trainer(tpu_cores=8)
trainer.fit(model)
# run test set
result = trainer.test()
print(result)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
--------------------------------------------------------------
TEST RESULTS
{'test_loss': tensor(1.1703, device='cuda:0')}
--------------------------------------------------------------
You can also run the test from a saved lightning model
.. code-block:: python
model = LitMNIST.load_from_checkpoint(PATH)
trainer = Trainer(tpu_cores=8)
trainer.test(model)
.. note:: Lightning disables gradients, puts model in eval mode and does everything needed for testing.
.. warning:: .test() is not stable yet on TPUs. We're working on getting around the multiprocessing challenges.
----------------
Predicting
----------
Again, a LightningModule is exactly the same as a PyTorch module. This means you can load it
and use it for prediction.
.. code-block:: python
model = LitMNIST.load_from_checkpoint(PATH)
x = torch.randn(1, 1, 28, 28)
out = model(x)
On the surface, it looks like ``forward`` and ``training_step`` are similar. Generally, we want to make sure that
what we want the model to do is what happens in the ``forward``. whereas the ``training_step`` likely calls forward from
within it.
.. testcode::
class MNISTClassifier(LightningModule):
def forward(self, x):
batch_size, channels, width, height = x.size()
x = x.view(batch_size, -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
x = F.relu(x)
x = self.layer_3(x)
x = F.log_softmax(x, dim=1)
return x
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
return loss
.. code-block:: python
model = MNISTClassifier()
x = mnist_image()
logits = model(x)
In this case, we've set this LightningModel to predict logits. But we could also have it predict feature maps:
.. testcode::
class MNISTRepresentator(LightningModule):
def forward(self, x):
batch_size, channels, width, height = x.size()
x = x.view(batch_size, -1)
x = self.layer_1(x)
x1 = F.relu(x)
x = self.layer_2(x1)
x2 = F.relu(x)
x3 = self.layer_3(x2)
return [x, x1, x2, x3]
def training_step(self, batch, batch_idx):
x, y = batch
out, l1_feats, l2_feats, l3_feats = self(x)
logits = F.log_softmax(out, dim=1)
ce_loss = F.nll_loss(logits, y)
loss = perceptual_loss(l1_feats, l2_feats, l3_feats) + ce_loss
return loss
.. code-block:: python
model = MNISTRepresentator.load_from_checkpoint(PATH)
x = mnist_image()
feature_maps = model(x)
Or maybe we have a model that we use to do generation
.. testcode::
class LitMNISTDreamer(LightningModule):
def forward(self, z):
imgs = self.decoder(z)
return imgs
def training_step(self, batch, batch_idx):
x, y = batch
representation = self.encoder(x)
imgs = self(representation)
loss = perceptual_loss(imgs, x)
return loss
.. code-block:: python
model = LitMNISTDreamer.load_from_checkpoint(PATH)
z = sample_noise()
generated_imgs = model(z)
How you split up what goes in ``forward`` vs ``training_step`` depends on how you want to use this model for
prediction.
----------------
The non essentials
==================
Extensibility
-------------
Although lightning makes everything super simple, it doesn't sacrifice any flexibility or control.
Lightning offers multiple ways of managing the training state.
Training overrides
^^^^^^^^^^^^^^^^^^
Any part of the training, validation and testing loop can be modified.
For instance, if you wanted to do your own backward pass, you would override the
default implementation
.. testcode::
def backward(self, use_amp, loss, optimizer):
loss.backward()
With your own
.. testcode::
class LitMNIST(LightningModule):
def backward(self, use_amp, loss, optimizer, optimizer_idx):
# do a custom way of backward
loss.backward(retain_graph=True)
Or if you wanted to initialize ddp in a different way than the default one
.. testcode::
def configure_ddp(self, model, device_ids):
# Lightning DDP simply routes to test_step, val_step, etc...
model = LightningDistributedDataParallel(
model,
device_ids=device_ids,
find_unused_parameters=True
)
return model
you could do your own:
.. testcode::
class LitMNIST(LightningModule):
def configure_ddp(self, model, device_ids):
model = Horovod(model)
# model = Ray(model)
return model
Every single part of training is configurable this way.
For a full list look at :ref:`LightningModule <lightning_module>`.
----------------
Callbacks
---------
Another way to add arbitrary functionality is to add a custom callback
for hooks that you might care about
.. testcode::
from pytorch_lightning.callbacks import Callback
class MyPrintingCallback(Callback):
def on_init_start(self, trainer):
print('Starting to init trainer!')
def on_init_end(self, trainer):
print('Trainer is init now')
def on_train_end(self, trainer, pl_module):
print('do something when training ends')
And pass the callbacks into the trainer
.. testcode::
trainer = Trainer(callbacks=[MyPrintingCallback()])
.. testoutput::
:hide:
Starting to init trainer!
Trainer is init now
.. tip::
See full list of 12+ hooks in the :ref:`callbacks`.
----------------
.. include:: child_modules.rst
----------------
.. include:: transfer_learning.rst
----------
*********************
Why PyTorch Lightning
*********************
a. Less boilerplate
===================
Research and production code starts with simple code, but quickly grows in complexity
once you add gpu training, 16-bit, checkpointing, logging, etc...
PyTorch Lightning implements these features for you and tests them rigorously to make sure you can
instead focus on the research idea.
Writing less engineering/bolierplate code means:
- fewer bugs
- faster iteration
- faster prototyping
b. More functionality
=====================
In PyTorch Lightning you leverage code written by hundreds of AI researchers,
research engs and PhDs from the world's top AI labs,
implementing all the latest best practices and SOTA features such as
- GPU, Multi GPU, TPU training
- Multi node training
- Auto logging
- ...
- Gradient accumulation
c. Less error prone
===================
Why re-invent the wheel?
Use PyTorch Lightning to enjoy a deep learning structure that is rigorously tested (500+ tests)
across CPUs/multi-GPUs/multi-TPUs on every pull-request.
We promise our collective team of 20+ from the top labs has thought about training more than you :)
d. Not a new library
====================
PyTorch Lightning is organized PyTorch - no need to learn a new framework.
Switching your model to Lightning is straight forward - here's a 2-minute video on how to do it.
.. raw:: html
<video width="100%" controls autoplay muted playsinline src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/pl_quick_start_full.m4v"></video>
Your projects WILL grow in complexity and you WILL end up engineering more than trying out new ideas...
Defer the hardest parts to Lightning!
----------------
********************
Lightning Philosophy
********************
Lightning structures your deep learning code in 4 parts:
- Research code
- Engineering code
- Non-essential code
- Data code
Research code
=============
In the MNIST generation example, the research code
would be the particular system and how it's trained (ie: A GAN or VAE or GPT).
.. code-block:: python
l1 = nn.Linear(...)
l2 = nn.Linear(...)
decoder = Decoder()
x1 = l1(x)
x2 = l2(x2)
out = decoder(features, x)
loss = perceptual_loss(x1, x2, x) + CE(out, x)
In Lightning, this code is organized into a :ref:`lightning_module`.
Engineering code
================
The Engineering code is all the code related to training this system. Things such as early stopping, distribution
over GPUs, 16-bit precision, etc. This is normally code that is THE SAME across most projects.
.. code-block:: python
model.cuda(0)
x = x.cuda(0)
distributed = DistributedParallel(model)
with gpu_zero:
download_data()
dist.barrier()
In Lightning, this code is abstracted out by the :ref:`trainer`.
Non-essential code
==================
This is code that helps the research but isn't relevant to the research code. Some examples might be:
1. Inspect gradients
2. Log to tensorboard.
|
.. code-block:: python
# log samples
z = Q.rsample()
generated = decoder(z)
self.experiment.log('images', generated)
In Lightning this code is organized into :ref:`callbacks`.
Data code
=========
Lightning uses standard PyTorch DataLoaders or anything that gives a batch of data.
This code tends to end up getting messy with transforms, normalization constants and data splitting
spread all over files.
.. code-block:: python
# data
train = MNIST(...)
train, val = split(train, val)
test = MNIST(...)
# transforms
train_transforms = ...
val_transforms = ...
test_transforms = ...
# dataloader ...
# download with dist.barrier() for multi-gpu, etc...
This code gets specially complicated once you start doing multi-gpu training or needing info about
the data to build your models.
In Lightning this code is organized inside a :ref:`datamodules`.
.. tip:: DataModules are optional but encouraged, otherwise you can use standard DataLoaders
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