Source code for ray.train.horovod.horovod_trainer

from typing import Any, Callable, Dict, Optional, Union

from ray.air.config import RunConfig, ScalingConfig
from ray.train import Checkpoint, DataConfig
from ray.train.data_parallel_trainer import DataParallelTrainer
from ray.train.horovod.config import HorovodConfig
from ray.train.trainer import GenDataset
from ray.util.annotations import PublicAPI

[docs]@PublicAPI(stability="beta") class HorovodTrainer(DataParallelTrainer): """A Trainer for data parallel Horovod training. This Trainer runs the function ``train_loop_per_worker`` on multiple Ray Actors. These actors already have the necessary Horovod setup already configured for distributed Horovod training. The ``train_loop_per_worker`` function is expected to take in either 0 or 1 arguments: .. testcode:: def train_loop_per_worker(): ... .. testcode:: def train_loop_per_worker(config: Dict): ... If ``train_loop_per_worker`` accepts an argument, then ``train_loop_config`` will be passed in as the argument. This is useful if you want to tune the values in ``train_loop_config`` as hyperparameters. If the ``datasets`` dict contains a training dataset (denoted by the "train" key), then it will be split into multiple dataset shards that can then be accessed by ``ray.train.get_dataset_shard("train")`` inside ``train_loop_per_worker``. All the other datasets will not be split and ``ray.train.get_dataset_shard(...)`` will return the the entire Dataset. Inside the ``train_loop_per_worker`` function, you can use any of the :ref:`Ray Train loop methods <train-loop-api>`. .. testcode:: from ray import train def train_loop_per_worker(): # Report intermediate results for callbacks or logging and # checkpoint data. # Returns dict of last saved checkpoint. train.get_checkpoint() # Returns the Dataset shard for the given key. train.get_dataset_shard("my_dataset") # Returns the total number of workers executing training. train.get_context().get_world_size() # Returns the rank of this worker. train.get_context().get_world_rank() # Returns the rank of the worker on the current node. train.get_context().get_local_rank() Any returns from the ``train_loop_per_worker`` will be discarded and not used or persisted anywhere. You could use ``TensorflowPredictor`` or ``TorchPredictor`` in conjunction with HorovodTrainer. You must save the model under the "model" kwarg in the ``Checkpoint`` passed to ````, so that it can be used by corresponding predictors. Example: .. testcode:: :skipif: True import os import tempfile import ray import horovod.torch as hvd import torch import torch.nn as nn from ray import train import ray.train.torch # Need this to use `train.torch.get_device()` from ray.train import Checkpoint, ScalingConfig from ray.train.horovod import HorovodTrainer # If using GPUs, set this to True. use_gpu = False input_size = 1 layer_size = 15 output_size = 1 num_epochs = 3 class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() self.layer1 = nn.Linear(input_size, layer_size) self.relu = nn.ReLU() self.layer2 = nn.Linear(layer_size, output_size) def forward(self, input): return self.layer2(self.relu(self.layer1(input))) def train_loop_per_worker(): hvd.init() dataset_shard = train.get_dataset_shard("train") model = NeuralNetwork() device = train.torch.get_device() loss_fn = nn.MSELoss() lr_scaler = 1 optimizer = torch.optim.SGD(model.parameters(), lr=0.1 * lr_scaler) # Horovod: wrap optimizer with DistributedOptimizer. optimizer = hvd.DistributedOptimizer( optimizer, named_parameters=model.named_parameters(), op=hvd.Average, ) for epoch in range(num_epochs): model.train() for batch in dataset_shard.iter_torch_batches( batch_size=32, dtypes=torch.float ): inputs, labels = torch.unsqueeze(batch["x"], 1), batch["y"] outputs = model(inputs) loss = loss_fn(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() print(f"epoch: {epoch}, loss: {loss.item()}") # Save a model checkpoint at the end of each epoch with tempfile.TemporaryDirectory() as temp_checkpoint_dir: ckpt_path = os.path.join(temp_checkpoint_dir, ""), ckpt_path) {"loss": loss.item(), "epoch": epoch}, checkpoint=Checkpoint.from_directory(temp_checkpoint_dir), ) train_dataset =[{"x": x, "y": x + 1} for x in range(32)]) scaling_config = ScalingConfig(num_workers=3, use_gpu=use_gpu) trainer = HorovodTrainer( train_loop_per_worker=train_loop_per_worker, scaling_config=scaling_config, datasets={"train": train_dataset}, ) result = Args: train_loop_per_worker: The training function to execute. This can either take in no arguments or a ``config`` dict. train_loop_config: Configurations to pass into ``train_loop_per_worker`` if it accepts an argument. horovod_config: Configuration for setting up the Horovod backend. If set to None, use the default configuration. This replaces the ``backend_config`` arg of ``DataParallelTrainer``. scaling_config: Configuration for how to scale data parallel training. dataset_config: Configuration for dataset ingest. run_config: Configuration for the execution of the training run. datasets: Any Datasets to use for training. Use the key "train" to denote which dataset is the training dataset. resume_from_checkpoint: A checkpoint to resume training from. metadata: Dict that should be made available via `ray.train.get_context().get_metadata()` and in `checkpoint.get_metadata()` for checkpoints saved from this Trainer. Must be JSON-serializable. """ def __init__( self, train_loop_per_worker: Union[Callable[[], None], Callable[[Dict], None]], *, train_loop_config: Optional[Dict] = None, horovod_config: Optional[HorovodConfig] = None, scaling_config: Optional[ScalingConfig] = None, dataset_config: Optional[DataConfig] = None, run_config: Optional[RunConfig] = None, datasets: Optional[Dict[str, GenDataset]] = None, metadata: Optional[Dict[str, Any]] = None, resume_from_checkpoint: Optional[Checkpoint] = None, ): super().__init__( train_loop_per_worker=train_loop_per_worker, train_loop_config=train_loop_config, backend_config=horovod_config or HorovodConfig(), scaling_config=scaling_config, dataset_config=dataset_config, run_config=run_config, datasets=datasets, resume_from_checkpoint=resume_from_checkpoint, metadata=metadata, )