from typing import Dict, Callable, Optional, Union, TYPE_CHECKING
from ray.air.config import ScalingConfig, RunConfig, DatasetConfig
from ray.train.trainer import GenDataset
from ray.air.checkpoint import Checkpoint
from ray.train.data_parallel_trainer import DataParallelTrainer
from ray.train.horovod.config import HorovodConfig
from ray.util.annotations import PublicAPI
if TYPE_CHECKING:
from ray.data.preprocessor import Preprocessor
[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:
.. code-block:: python
def train_loop_per_worker():
...
.. code-block:: python
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 ``session.get_dataset_shard("train")`` inside
``train_loop_per_worker``. All the other datasets will not be split and
``session.get_dataset_shard(...)`` will return the the entire Dataset.
Inside the ``train_loop_per_worker`` function, you can use any of the
:ref:`Ray AIR session methods <air-session-ref>`.
.. code-block:: python
def train_loop_per_worker():
# Report intermediate results for callbacks or logging and
# checkpoint data.
session.report(...)
# Returns dict of last saved checkpoint.
session.get_checkpoint()
# Returns the Dataset shard for the given key.
session.get_dataset_shard("my_dataset")
# Returns the total number of workers executing training.
session.get_world_size()
# Returns the rank of this worker.
session.get_world_rank()
# Returns the rank of the worker on the current node.
session.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 ``session.report()``, so that it can be used by
corresponding predictors.
Example:
.. code-block:: python
import ray
import ray.train as train
import ray.train.torch. # Need this to use `train.torch.get_device()`
import horovod.torch as hvd
import torch
import torch.nn as nn
from ray.air import session
from ray.train.horovod import HorovodTrainer
from ray.train.torch import TorchCheckpoint
from ray.air.config import ScalingConfig
# 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 = session.get_dataset_shard("train")
model = NeuralNetwork()
device = train.torch.get_device()
model.to(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()}")
session.report(
{},
checkpoint=TorchCheckpoint.from_state_dict(
model.state_dict()
),
)
train_dataset = ray.data.from_items([{"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 = trainer.fit()
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. If a ``preprocessor`` is provided and has not already been fit,
it will be fit on the training dataset. All datasets will be transformed
by the ``preprocessor`` if one is provided.
preprocessor: A ray.data.Preprocessor to preprocess the
provided datasets.
resume_from_checkpoint: A checkpoint to resume training from.
"""
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[Dict[str, DatasetConfig]] = None,
run_config: Optional[RunConfig] = None,
datasets: Optional[Dict[str, GenDataset]] = None,
preprocessor: Optional["Preprocessor"] = 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,
preprocessor=preprocessor,
resume_from_checkpoint=resume_from_checkpoint,
)