.. _train-pytorch: 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 :ref:`workers ` and place data on the correct CPU or GPU device. 3. Configure a :ref:`training function ` to report metrics and save checkpoints. 4. Configure :ref:`scaling ` and CPU or GPU resource requirements for a training job. 5. Launch a distributed training job with a :class:`~ray.train.torch.TorchTrainer` class. Quickstart ---------- For reference, the final code will look something like the following: .. testcode:: :skipif: True 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. :class:`~ray.train.ScalingConfig` defines the number of distributed training workers and whether to use GPUs. 3. :class:`~ray.train.torch.TorchTrainer` launches the distributed training job. Compare a PyTorch training script with and without Ray Train. .. tab-set:: .. tab-item:: PyTorch .. This snippet isn't tested because it doesn't use any Ray code. .. testcode:: :skipif: True 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) .. tab-item:: PyTorch + Ray Train .. code-block:: python :emphasize-lines: 12, 14, 21, 55-58, 59, 63, 66-68, 72-73, 76 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 -------------------------- .. include:: ./common/torch-configure-train_func.rst Set up a model ^^^^^^^^^^^^^^ Use the :func:`ray.train.torch.prepare_model` utility function to: 1. Move your model to the correct device. 2. Wrap it in ``DistributedDataParallel``. .. code-block:: diff -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 ^^^^^^^^^^^^^^^^ .. TODO: Update this to use Ray Data. Use the :func:`ray.train.torch.prepare_data_loader` utility function, which: 1. Adds a :class:`~torch.utils.data.distributed.DistributedSampler` to your :class:`~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 :ref:`data-ingest-torch`. .. code-block:: diff 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: .. testcode:: :skipif: True 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``, :meth:`~ray.train.torch.prepare_data_loader` will not add another one, and will respect the configuration of the existing sampler. .. note:: :class:`~torch.utils.data.distributed.DistributedSampler` does not work with a ``DataLoader`` that wraps :class:`~torch.utils.data.IterableDataset`. If you want to work with an dataset iterator, consider using :ref:`Ray Data ` instead of PyTorch DataLoader since it provides performant streaming data ingestion for large scale datasets. See :ref:`data-ingest-torch` for more details. Report checkpoints and metrics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To monitor progress, you can report intermediate metrics and checkpoints using the :func:`ray.train.report` utility function. .. code-block:: diff +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 :ref:`train-monitoring-and-logging` and :ref:`train-checkpointing`. .. include:: ./common/torch-configure-run.rst Next steps ---------- After you have converted your PyTorch training script to use Ray Train: * See :ref:`User Guides ` to learn more about how to perform specific tasks. * Browse the :doc:`Examples ` for end-to-end examples of how to use Ray Train. * Dive into the :ref:`API Reference ` for more details on the classes and methods used in this tutorial.