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Get Started with Distributed Training using PyTorch

This tutorial walks through the process of converting an existing PyTorch script to use Ray Train.

Learn how to:

  1. Configure a model to run distributed and on the correct CPU/GPU device.
  2. Configure a dataloader to shard data across the workers <train-overview-worker> and place data on the correct CPU or GPU device.
  3. Configure a training function <train-overview-training-function> to report metrics and save checkpoints.
  4. Configure scaling <train-overview-scaling-config> and CPU or GPU resource requirements for a training job.
  5. Launch a distributed training job with a ~ray.train.torch.TorchTrainer class.

Quickstart

For reference, the final code will look something like the following:

from ray.train.torch import TorchTrainer from ray.train import ScalingConfig

def train_func():

# Your PyTorch training code here. ...

scaling_config = ScalingConfig(num_workers=2, use_gpu=True) trainer = TorchTrainer(train_func, scaling_config=scaling_config) result = trainer.fit()

  1. train_func is the Python code that executes on each distributed training worker.
  2. ~ray.train.ScalingConfig defines the number of distributed training workers and whether to use GPUs.
  3. ~ray.train.torch.TorchTrainer launches the distributed training job.

Compare a PyTorch training script with and without Ray Train.

PyTorch

import os import tempfile

import torch from torch.nn import CrossEntropyLoss from torch.optim import Adam from torch.utils.data import DataLoader from torchvision.models import resnet18 from torchvision.datasets import FashionMNIST from torchvision.transforms import ToTensor, Normalize, Compose

# Model, Loss, Optimizer model = resnet18(num_classes=10) model.conv1 = torch.nn.Conv2d( 1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False ) model.to("cuda") criterion = CrossEntropyLoss() optimizer = Adam(model.parameters(), lr=0.001)

# Data transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))]) train_data = FashionMNIST(root='./data', train=True, download=True, transform=transform) train_loader = DataLoader(train_data, batch_size=128, shuffle=True)

# Training for epoch in range(10): for images, labels in train_loader: images, labels = images.to("cuda"), labels.to("cuda") outputs = model(images) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step()

metrics = {"loss": loss.item(), "epoch": epoch} checkpoint_dir = tempfile.mkdtemp() checkpoint_path = os.path.join(checkpoint_dir, "model.pt") torch.save(model.state_dict(), checkpoint_path) print(metrics)

PyTorch + Ray Train

import os
import tempfile

import torch
from torch.nn import CrossEntropyLoss
from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision.models import resnet18
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor, Normalize, Compose

import ray.train.torch

def train_func():
    # Model, Loss, Optimizer
    model = resnet18(num_classes=10)
    model.conv1 = torch.nn.Conv2d(
        1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
    )
    # [1] Prepare model.
    model = ray.train.torch.prepare_model(model)
    # model.to("cuda")  # This is done by `prepare_model`
    criterion = CrossEntropyLoss()
    optimizer = Adam(model.parameters(), lr=0.001)

    # Data
    transform = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
    data_dir = os.path.join(tempfile.gettempdir(), "data")
    train_data = FashionMNIST(root=data_dir, train=True, download=True, transform=transform)
    train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
    # [2] Prepare dataloader.
    train_loader = ray.train.torch.prepare_data_loader(train_loader)

    # Training
    for epoch in range(10):
        if ray.train.get_context().get_world_size() > 1:
            train_loader.sampler.set_epoch(epoch)

        for images, labels in train_loader:
            # This is done by `prepare_data_loader`!
            # images, labels = images.to("cuda"), labels.to("cuda")
            outputs = model(images)
            loss = criterion(outputs, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        # [3] Report metrics and checkpoint.
        metrics = {"loss": loss.item(), "epoch": epoch}
        with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
            torch.save(
                model.module.state_dict(),
                os.path.join(temp_checkpoint_dir, "model.pt")
            )
            ray.train.report(
                metrics,
                checkpoint=ray.train.Checkpoint.from_directory(temp_checkpoint_dir),
            )
        if ray.train.get_context().get_world_rank() == 0:
            print(metrics)

# [4] Configure scaling and resource requirements.
scaling_config = ray.train.ScalingConfig(num_workers=2, use_gpu=True)

# [5] Launch distributed training job.
trainer = ray.train.torch.TorchTrainer(
    train_func,
    scaling_config=scaling_config,
    # [5a] 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 = trainer.fit()

# [6] Load the trained model.
with result.checkpoint.as_directory() as checkpoint_dir:
    model_state_dict = torch.load(os.path.join(checkpoint_dir, "model.pt"))
    model = resnet18(num_classes=10)
    model.conv1 = torch.nn.Conv2d(
        1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
    )
    model.load_state_dict(model_state_dict)

Set up a training function

Set up a model

Use the ray.train.torch.prepare_model utility function to:

  1. Move your model to the correct device.
  2. Wrap it in DistributedDataParallel.
-from torch.nn.parallel import DistributedDataParallel
+import ray.train.torch

 def train_func():

     ...

     # Create model.
     model = ...

     # Set up distributed training and device placement.
-    device_id = ... # Your logic to get the right device.
-    model = model.to(device_id or "cpu")
-    model = DistributedDataParallel(model, device_ids=[device_id])
+    model = ray.train.torch.prepare_model(model)

     ...

Set up a dataset

Use the ray.train.torch.prepare_data_loader utility function, which:

  1. Adds a ~torch.utils.data.distributed.DistributedSampler to your ~torch.utils.data.DataLoader.
  2. Moves the batches to the right device.

Note that this step isn't necessary if you're passing in Ray Data to your Trainer. See data-ingest-torch.

from torch.utils.data import DataLoader

+import ray.train.torch

def train_func():

...

dataset = ...

data_loader = DataLoader(dataset, batch_size=worker_batch_size, shuffle=True)

  • data_loader = ray.train.torch.prepare_data_loader(data_loader)

    for epoch in range(10):

  • if ray.train.get_context().get_world_size() > 1:
  • data_loader.sampler.set_epoch(epoch)

for X, y in data_loader:

  • X = X.to_device(device)
  • y = y.to_device(device)

...

Tip

Keep in mind that DataLoader takes in a batch_size which is the batch size for each worker. The global batch size can be calculated from the worker batch size (and vice-versa) with the following equation:

global_batch_size = worker_batch_size * ray.train.get_context().get_world_size()

Note

If you already manually set up your DataLoader with a DistributedSampler, ~ray.train.torch.prepare_data_loader will not add another one, and will respect the configuration of the existing sampler.

Note

~torch.utils.data.distributed.DistributedSampler does not work with a DataLoader that wraps ~torch.utils.data.IterableDataset. If you want to work with an dataset iterator, consider using Ray Data <data> instead of PyTorch DataLoader since it provides performant streaming data ingestion for large scale datasets.

See data-ingest-torch for more details.

Report checkpoints and metrics

To monitor progress, you can report intermediate metrics and checkpoints using the ray.train.report utility function.

+import os
+import tempfile

+import ray.train

 def train_func():

     ...

     with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
        torch.save(
            model.state_dict(), os.path.join(temp_checkpoint_dir, "model.pt")
        )

+       metrics = {"loss": loss.item()}  # Training/validation metrics.

        # Build a Ray Train checkpoint from a directory
+       checkpoint = ray.train.Checkpoint.from_directory(temp_checkpoint_dir)

        # Ray Train will automatically save the checkpoint to persistent storage,
        # so the local `temp_checkpoint_dir` can be safely cleaned up after.
+       ray.train.report(metrics=metrics, checkpoint=checkpoint)

     ...

For more details, see train-monitoring-and-logging and train-checkpointing.

Next steps

After you have converted your PyTorch training script to use Ray Train:

  • See User Guides <train-user-guides> to learn more about how to perform specific tasks.
  • Browse the Examples <examples> for end-to-end examples of how to use Ray Train.
  • Dive into the API Reference <train-api> for more details on the classes and methods used in this tutorial.