Contents

RaySGD: Distributed Training Wrappers

RaySGD is a lightweight library for distributed deep learning, providing thin wrappers around PyTorch and TensorFlow native modules for data parallel training.

The main features are:

  • Ease of use: Scale PyTorch’s native DistributedDataParallel and TensorFlow’s tf.distribute.MirroredStrategy without needing to monitor individual nodes.

  • Composability: RaySGD is built on top of the Ray Actor API, enabling seamless integration with existing Ray applications such as RLlib, Tune, and Ray.Serve.

  • Scale up and down: Start on single CPU. Scale up to multi-node, multi-CPU, or multi-GPU clusters by changing 2 lines of code.

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Getting Started

You can start a TorchTrainer with the following:

import ray
from ray.util.sgd import TorchTrainer
from ray.util.sgd.torch import TrainingOperator
from ray.util.sgd.torch.examples.train_example import LinearDataset

import torch
from torch.utils.data import DataLoader

class CustomTrainingOperator(TrainingOperator):
    def setup(self, config):
        # Load data.
        train_loader = DataLoader(LinearDataset(2, 5), config["batch_size"])
        val_loader = DataLoader(LinearDataset(2, 5), config["batch_size"])

        # Create model.
        model = torch.nn.Linear(1, 1)

        # Create optimizer.
        optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)

        # Create loss.
        loss = torch.nn.MSELoss()

        # Register model, optimizer, and loss.
        self.model, self.optimizer, self.criterion = self.register(
            models=model,
            optimizers=optimizer,
            criterion=loss)

        # Register data loaders.
        self.register_data(train_loader=train_loader, validation_loader=val_loader)


ray.init()

trainer1 = TorchTrainer(
    training_operator_cls=CustomTrainingOperator,
    num_workers=2,
    use_gpu=False,
    config={"batch_size": 64})

stats = trainer1.train()
print(stats)
trainer1.shutdown()
print("success!")

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Contributing to RLlib Distributed PyTorch