Source code for ray.train.tensorflow.tensorflow_trainer

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

from ray.train.tensorflow.config import TensorflowConfig
from ray.train.trainer import GenDataset
from ray.train.data_parallel_trainer import DataParallelTrainer
from ray.air.config import ScalingConfig, RunConfig, DatasetConfig
from ray.air.checkpoint import Checkpoint
from ray.util import PublicAPI

    from import Preprocessor

[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: .. 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>`. .. 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. .. code-block:: python def train_loop_per_worker(): # Report intermediate results for callbacks or logging and # checkpoint data. # Returns dict of last saved checkpoint. session.get_checkpoint() # Returns the Ray 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. To save a model to use for the ``TensorflowPredictor``, you must save it under the "model" kwarg in ``Checkpoint`` passed to ````. Example: .. code-block:: python import tensorflow as tf import ray from ray.air import session, Checkpoint from ray.train.tensorflow import TensorflowTrainer from ray.air.config import ScalingConfig input_size = 1 def build_model(): # toy neural network : 1-layer return tf.keras.Sequential( [tf.keras.layers.Dense( 1, activation="linear", input_shape=(input_size,))] ) def train_loop_for_worker(config): dataset_shard = session.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"]) for epoch in range(config["num_epochs"]): tf_dataset = dataset_shard.to_tf( feature_columns="x", label_columns="y", batch_size=1 ) # You can also use ray.air.integrations.keras.Callback # for reporting and checkpointing instead of reporting manually. {}, checkpoint=Checkpoint.from_dict( dict(epoch=epoch, model=model.get_weights()) ), ) train_dataset = [{"x": x, "y": x + 1} for x in range(32)]) trainer = TensorflowTrainer(scaling_config=ScalingConfig(num_workers=3), datasets={"train": train_dataset}, train_loop_config={"num_epochs": 2}) 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. 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 Ray 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 to preprocess the provided datasets. resume_from_checkpoint: A checkpoint to resume training from. """
[docs] 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[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, ): 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, preprocessor=preprocessor, resume_from_checkpoint=resume_from_checkpoint, )