Ray Train - Distributed Training Orchestration

Quick start

Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.

Installation:

pip install -U "ray[train]"

Basic PyTorch training (single node):

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

# Define training function
def train_func(config):
    # Your normal PyTorch code
    model = nn.Linear(10, 1)
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

    # Prepare for distributed (Ray handles device placement)
    model = train.torch.prepare_model(model)

    for epoch in range(10):
        # Your training loop
        output = model(torch.randn(32, 10))
        loss = output.sum()
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

        # Report metrics (logged automatically)
        train.report({"loss": loss.item(), "epoch": epoch})

# Run distributed training
trainer = TorchTrainer(
    train_func,
    scaling_config=ScalingConfig(
        num_workers=4,  # 4 GPUs/workers
        use_gpu=True
    )
)

result = trainer.fit()
print(f"Final loss: {result.metrics['loss']}")

That's it! Ray handles:

• Distributed coordination
• GPU allocation
• Fault tolerance
• Checkpointing
• Metric aggregation

Common workflows

Workflow 1: Scale existing PyTorch code

Original single-GPU code:

model = MyModel().cuda()
optimizer = torch.optim.Adam(model.parameters())

for epoch in range(epochs):
    for batch in dataloader:
        loss = model(batch)
        loss.backward()
        optimizer.step()

Ray Train version (scales to multi-GPU/multi-node):

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

def train_func(config):
    model = MyModel()
    optimizer = torch.optim.Adam(model.parameters())