from typing import Any, Callable, Dict, Optional, Union
from ray.train import Checkpoint, DataConfig, RunConfig, ScalingConfig
from ray.train.data_parallel_trainer import DataParallelTrainer
from ray.train.tensorflow.config import TensorflowConfig
from ray.train.trainer import GenDataset
from ray.util import PublicAPI
[docs]@PublicAPI(stability="beta")
class TensorflowTrainer(DataParallelTrainer):
"""A Trainer for data parallel Tensorflow training.
This Trainer runs the function ``train_loop_per_worker`` on multiple Ray
Actors. These actors already have the necessary TensorFlow process group already
configured for distributed TensorFlow 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>`.
.. warning::
Ray will not automatically set any environment variables or configuration
related to local parallelism / threading
:ref:`aside from "OMP_NUM_THREADS" <omp-num-thread-note>`.
If you desire greater control over TensorFlow threading, use
the ``tf.config.threading`` module (eg.
``tf.config.threading.set_inter_op_parallelism_threads(num_cpus)``)
at the beginning of your ``train_loop_per_worker`` function.
.. testcode::
from ray import train
def train_loop_per_worker():
# Report intermediate results for callbacks or logging and
# checkpoint data.
train.report(...)
# 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.
To save a model to use for the ``TensorflowPredictor``, you must save it under the
"model" kwarg in ``Checkpoint`` passed to ``train.report()``.
Example:
.. testcode::
import os
import tempfile
import tensorflow as tf
import ray
from ray import train
from ray.train import Checkpoint, ScalingConfig
from ray.train.tensorflow import TensorflowTrainer
def build_model():
# toy neural network : 1-layer
return tf.keras.Sequential(
[tf.keras.layers.Dense(
1, activation="linear", input_shape=(1,))]
)
def train_loop_per_worker(config):
dataset_shard = train.get_dataset_shard("train")
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
with strategy.scope():
model = build_model()
model.compile(
optimizer="Adam", loss="mean_squared_error", metrics=["mse"])
tf_dataset = dataset_shard.to_tf(
feature_columns="x",
label_columns="y",
batch_size=1
)
for epoch in range(config["num_epochs"]):
model.fit(tf_dataset)
# Create checkpoint.
checkpoint_dir = tempfile.mkdtemp()
model.save_weights(
os.path.join(checkpoint_dir, "my_checkpoint")
)
checkpoint = Checkpoint.from_directory(checkpoint_dir)
train.report(
{},
checkpoint=checkpoint,
)
train_dataset = ray.data.from_items([{"x": x, "y": x + 1} for x in range(32)])
trainer = TensorflowTrainer(
train_loop_per_worker=train_loop_per_worker,
scaling_config=ScalingConfig(num_workers=3, use_gpu=True),
datasets={"train": train_dataset},
train_loop_config={"num_epochs": 2},
)
result = trainer.fit()
.. testoutput::
:options:+ELLIPSIS
:hide:
...
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.
tensorflow_config: Configuration for setting up the TensorFlow 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,
tensorflow_config: Optional[TensorflowConfig] = 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,
):
if not tensorflow_config:
tensorflow_config = TensorflowConfig()
super(TensorflowTrainer, self).__init__(
train_loop_per_worker=train_loop_per_worker,
train_loop_config=train_loop_config,
backend_config=tensorflow_config,
scaling_config=scaling_config,
dataset_config=dataset_config,
run_config=run_config,
datasets=datasets,
resume_from_checkpoint=resume_from_checkpoint,
metadata=metadata,
)