doc/source/train/getting-started-pytorch-lightning.rst
.. _train-pytorch-lightning:
This tutorial walks through the process of converting an existing PyTorch Lightning script to use Ray Train.
Learn how to:
training function <train-overview-training-function> to report metrics and save checkpoints.scaling <train-overview-scaling-config> and CPU or GPU resource requirements for a training job.~ray.train.torch.TorchTrainer.For reference, the final code is as follows:
.. testcode:: :skipif: True
from ray.train.torch import TorchTrainer
from ray.train import ScalingConfig
def train_func():
# Your PyTorch Lightning training code here.
scaling_config = ScalingConfig(num_workers=2, use_gpu=True)
trainer = TorchTrainer(train_func, scaling_config=scaling_config)
result = trainer.fit()
train_func is the Python code that executes on each distributed training worker.~ray.train.ScalingConfig defines the number of distributed training workers and whether to use GPUs.~ray.train.torch.TorchTrainer launches the distributed training job.Compare a PyTorch Lightning training script with and without Ray Train.
.. tab-set::
.. tab-item:: PyTorch Lightning + Ray Train
.. code-block:: python
:emphasize-lines: 11-12, 38, 52-57, 59, 63, 66-73
import os
import tempfile
import torch
from torch.utils.data import DataLoader
from torchvision.models import resnet18
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor, Normalize, Compose
import lightning.pytorch as pl
import ray.train.lightning
from ray.train.torch import TorchTrainer
# Model, Loss, Optimizer
class ImageClassifier(pl.LightningModule):
def __init__(self):
super(ImageClassifier, self).__init__()
self.model = resnet18(num_classes=10)
self.model.conv1 = torch.nn.Conv2d(
1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
)
self.criterion = torch.nn.CrossEntropyLoss()
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
outputs = self.forward(x)
loss = self.criterion(outputs, y)
self.log("loss", loss, on_step=True, prog_bar=True)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.model.parameters(), lr=0.001)
def train_func():
# Data
transform = Compose([ToTensor(), Normalize((0.28604,), (0.32025,))])
data_dir = os.path.join(tempfile.gettempdir(), "data")
train_data = FashionMNIST(root=data_dir, train=True, download=True, transform=transform)
train_dataloader = DataLoader(train_data, batch_size=128, shuffle=True)
# Training
model = ImageClassifier()
# [1] Configure PyTorch Lightning Trainer.
trainer = pl.Trainer(
max_epochs=10,
devices="auto",
accelerator="auto",
strategy=ray.train.lightning.RayDDPStrategy(),
plugins=[ray.train.lightning.RayLightningEnvironment()],
callbacks=[ray.train.lightning.RayTrainReportCallback()],
# [1a] Optionally, disable the default checkpointing behavior
# in favor of the `RayTrainReportCallback` above.
enable_checkpointing=False,
)
trainer = ray.train.lightning.prepare_trainer(trainer)
trainer.fit(model, train_dataloaders=train_dataloader)
# [2] Configure scaling and resource requirements.
scaling_config = ray.train.ScalingConfig(num_workers=2, use_gpu=True)
# [3] Launch distributed training job.
trainer = TorchTrainer(
train_func,
scaling_config=scaling_config,
# [3a] If running in a multi-node cluster, this is where you
# should configure the run's persistent storage that is accessible
# across all worker nodes.
# run_config=ray.train.RunConfig(storage_path="s3://..."),
)
result: ray.train.Result = trainer.fit()
# [4] Load the trained model.
with result.checkpoint.as_directory() as checkpoint_dir:
model = ImageClassifier.load_from_checkpoint(
os.path.join(
checkpoint_dir,
ray.train.lightning.RayTrainReportCallback.CHECKPOINT_NAME,
),
)
.. tab-item:: PyTorch Lightning
.. This snippet isn't tested because it doesn't use any Ray code.
.. testcode::
:skipif: True
import torch
from torchvision.models import resnet18
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor, Normalize, Compose
from torch.utils.data import DataLoader
import lightning.pytorch as pl
# Model, Loss, Optimizer
class ImageClassifier(pl.LightningModule):
def __init__(self):
super(ImageClassifier, self).__init__()
self.model = resnet18(num_classes=10)
self.model.conv1 = torch.nn.Conv2d(
1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
)
self.criterion = torch.nn.CrossEntropyLoss()
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
outputs = self.forward(x)
loss = self.criterion(outputs, y)
self.log("loss", loss, on_step=True, prog_bar=True)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.model.parameters(), lr=0.001)
# Data
transform = Compose([ToTensor(), Normalize((0.28604,), (0.32025,))])
train_data = FashionMNIST(root='./data', train=True, download=True, transform=transform)
train_dataloader = DataLoader(train_data, batch_size=128, shuffle=True)
# Training
model = ImageClassifier()
trainer = pl.Trainer(max_epochs=10)
trainer.fit(model, train_dataloaders=train_dataloader)
.. include:: ./common/torch-configure-train_func.rst
Ray Train sets up your distributed process group on each worker. You only need to make a few changes to your Lightning Trainer definition.
.. code-block:: diff
import lightning.pytorch as pl
-from pl.strategies import DDPStrategy
-from pl.plugins.environments import LightningEnvironment
+import ray.train.lightning
def train_func():
...
model = MyLightningModule(...)
datamodule = MyLightningDataModule(...)
trainer = pl.Trainer(
- devices=[0, 1, 2, 3],
- strategy=DDPStrategy(),
- plugins=[LightningEnvironment()],
+ devices="auto",
+ accelerator="auto",
+ strategy=ray.train.lightning.RayDDPStrategy(),
+ plugins=[ray.train.lightning.RayLightningEnvironment()]
)
+ trainer = ray.train.lightning.prepare_trainer(trainer)
trainer.fit(model, datamodule=datamodule)
The following sections discuss each change.
Configure the distributed strategy ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Ray Train offers several sub-classed distributed strategies for Lightning. These strategies retain the same argument list as their base strategy classes. Internally, they configure the root device and the distributed sampler arguments.
~ray.train.lightning.RayDDPStrategy~ray.train.lightning.RayFSDPStrategy~ray.train.lightning.RayDeepSpeedStrategy.. code-block:: diff
import lightning.pytorch as pl
-from pl.strategies import DDPStrategy
+import ray.train.lightning
def train_func():
...
trainer = pl.Trainer(
...
- strategy=DDPStrategy(),
+ strategy=ray.train.lightning.RayDDPStrategy(),
...
)
...
Configure the Ray cluster environment plugin ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Ray Train also provides a :class:~ray.train.lightning.RayLightningEnvironment class
as a specification for the Ray Cluster. This utility class configures the worker's
local, global, and node rank and world size.
.. code-block:: diff
import lightning.pytorch as pl
-from pl.plugins.environments import LightningEnvironment
+import ray.train.lightning
def train_func():
...
trainer = pl.Trainer(
...
- plugins=[LightningEnvironment()],
+ plugins=[ray.train.lightning.RayLightningEnvironment()],
...
)
...
Configure parallel devices ^^^^^^^^^^^^^^^^^^^^^^^^^^
In addition, Ray TorchTrainer has already configured the correct
CUDA_VISIBLE_DEVICES for you. One should always use all available
GPUs by setting devices="auto" and acelerator="auto".
.. code-block:: diff
import lightning.pytorch as pl
def train_func():
...
trainer = pl.Trainer(
...
- devices=[0,1,2,3],
+ devices="auto",
+ accelerator="auto",
...
)
...
Report checkpoints and metrics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To persist your checkpoints and monitor training progress, add a
:class:ray.train.lightning.RayTrainReportCallback utility callback to your Trainer.
.. code-block:: diff
import lightning.pytorch as pl
from ray.train.lightning import RayTrainReportCallback
def train_func():
...
trainer = pl.Trainer(
...
- callbacks=[...],
+ callbacks=[..., RayTrainReportCallback()],
)
...
Reporting metrics and checkpoints to Ray Train enables you to support :ref:fault-tolerant training <train-fault-tolerance> and :ref:hyperparameter optimization <train-tune>.
Note that the :class:ray.train.lightning.RayTrainReportCallback class only provides a simple implementation, and can be :ref:further customized <train-dl-saving-checkpoints>.
Prepare your Lightning Trainer ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Finally, pass your Lightning Trainer into
:meth:~ray.train.lightning.prepare_trainer to validate
your configurations.
.. code-block:: diff
import lightning.pytorch as pl
import ray.train.lightning
def train_func():
...
trainer = pl.Trainer(...)
+ trainer = ray.train.lightning.prepare_trainer(trainer)
...
.. include:: ./common/torch-configure-run.rst
After you have converted your PyTorch Lightning training script to use Ray Train:
User Guides <train-user-guides> to learn more about how to perform specific tasks.Examples <examples> for end-to-end examples of how to use Ray Train.API Reference <train-api> for more details on the classes and methods from this tutorial.Ray Train is tested with pytorch_lightning versions 1.6.5 and 2.1.2. For full compatibility, use pytorch_lightning>=1.6.5 .
Earlier versions aren't prohibited but may result in unexpected issues. If you run into any compatibility issues, consider upgrading your PyTorch Lightning version or
file an issue <https://github.com/ray-project/ray/issues>_.
.. note::
If you are using Lightning 2.x, please use the import path `lightning.pytorch.xxx` instead of `pytorch_lightning.xxx`.
.. _lightning-trainer-migration-guide:
Ray 2.4 introduced the LightningTrainer, and exposed a
LightningConfigBuilder to define configurations for pl.LightningModule
and pl.Trainer.
It then instantiates the model and trainer objects and runs a pre-defined training function in a black box.
This version of the LightningTrainer API was constraining and limited your ability to manage the training functionality.
Ray 2.7 introduced the newly unified :class:~ray.train.torch.TorchTrainer API, which offers
enhanced transparency, flexibility, and simplicity. This API is more aligned
with standard PyTorch Lightning scripts, ensuring users have better
control over their native Lightning code.
.. tab-set::
.. tab-item:: (Deprecating) LightningTrainer
.. This snippet isn't tested because it raises a hard deprecation warning.
.. testcode::
:skipif: True
from ray.train.lightning import LightningConfigBuilder, LightningTrainer
config_builder = LightningConfigBuilder()
# [1] Collect model configs
config_builder.module(cls=MyLightningModule, lr=1e-3, feature_dim=128)
# [2] Collect checkpointing configs
config_builder.checkpointing(monitor="val_accuracy", mode="max", save_top_k=3)
# [3] Collect pl.Trainer configs
config_builder.trainer(
max_epochs=10,
accelerator="gpu",
log_every_n_steps=100,
)
# [4] Build datasets on the head node
datamodule = MyLightningDataModule(batch_size=32)
config_builder.fit_params(datamodule=datamodule)
# [5] Execute the internal training function in a black box
ray_trainer = LightningTrainer(
lightning_config=config_builder.build(),
scaling_config=ScalingConfig(num_workers=4, use_gpu=True),
run_config=RunConfig(
checkpoint_config=CheckpointConfig(
num_to_keep=3,
checkpoint_score_attribute="val_accuracy",
checkpoint_score_order="max",
),
)
)
result = ray_trainer.fit()
# [6] Load the trained model from an opaque Lightning-specific checkpoint.
lightning_checkpoint = result.checkpoint
model = lightning_checkpoint.get_model(MyLightningModule)
.. tab-item:: (New API) TorchTrainer
.. This snippet isn't tested because it runs with 4 GPUs, and CI is only run with 1.
.. testcode::
:skipif: True
import os
import lightning.pytorch as pl
import ray.train
from ray.train.torch import TorchTrainer
from ray.train.lightning import (
RayDDPStrategy,
RayLightningEnvironment,
RayTrainReportCallback,
prepare_trainer
)
def train_func():
# [1] Create a Lightning model
model = MyLightningModule(lr=1e-3, feature_dim=128)
# [2] Report Checkpoint with callback
ckpt_report_callback = RayTrainReportCallback()
# [3] Create a Lighting Trainer
trainer = pl.Trainer(
max_epochs=10,
log_every_n_steps=100,
# New configurations below
devices="auto",
accelerator="auto",
strategy=RayDDPStrategy(),
plugins=[RayLightningEnvironment()],
callbacks=[ckpt_report_callback],
)
# Validate your Lightning trainer configuration
trainer = prepare_trainer(trainer)
# [4] Build your datasets on each worker
datamodule = MyLightningDataModule(batch_size=32)
trainer.fit(model, datamodule=datamodule)
# [5] Explicitly define and run the training function
ray_trainer = TorchTrainer(
train_func,
scaling_config=ray.train.ScalingConfig(num_workers=4, use_gpu=True),
run_config=ray.train.RunConfig(
checkpoint_config=ray.train.CheckpointConfig(
num_to_keep=3,
checkpoint_score_attribute="val_accuracy",
checkpoint_score_order="max",
),
)
)
result = ray_trainer.fit()
# [6] Load the trained model from a simplified checkpoint interface.
checkpoint: ray.train.Checkpoint = result.checkpoint
with checkpoint.as_directory() as checkpoint_dir:
print("Checkpoint contents:", os.listdir(checkpoint_dir))
checkpoint_path = os.path.join(checkpoint_dir, "checkpoint.ckpt")
model = MyLightningModule.load_from_checkpoint(checkpoint_path)