Distributed TensorFlow¶
Warning
This is an older version of Ray SGD. A newer, more light-weight version of Ray SGD (named Ray Train) is in alpha as of Ray 1.7. See the documentation here. To migrate from v1 to v2 you can follow the migration guide.
RaySGD’s TFTrainer simplifies distributed model training for Tensorflow. The TFTrainer is a wrapper around MultiWorkerMirroredStrategy with a Python API to easily incorporate distributed training into a larger Python application, as opposed to write custom logic of setting environments and starting separate processes.
Under the hood, TFTrainer will create replicas of your model (controlled by num_replicas), each of which is managed by a Ray actor.
Tip
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With Ray:
Wrap your training with this:
ray.init(args.address)
trainer = TFTrainer(
model_creator=model_creator,
data_creator=data_creator,
num_replicas=4,
use_gpu=True,
verbose=True,
config={
"fit_config": {
"steps_per_epoch": num_train_steps,
},
"evaluate_config": {
"steps": num_eval_steps,
}
})
Then, start a Ray cluster.
ray up CLUSTER.yaml
python train.py --address="localhost:<PORT>"
Before, with Tensorflow:
In your training program, insert the following, and customize for each worker:
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["localhost:12345", "localhost:23456"]
},
'task': {'type': 'worker', 'index': 0}
})
...
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
with strategy.scope():
multi_worker_model = model_creator()
And on each machine, launch a separate process that contains the index of the worker and information about all other nodes of the cluster.
TFTrainer Example¶
Below is an example of using Ray’s TFTrainer. Under the hood, TFTrainer will create replicas of your model (controlled by num_replicas) which are each managed by a worker.
import argparse
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np
import ray
from ray import tune
from ray.util.sgd.tf.tf_trainer import TFTrainer, TFTrainable
NUM_TRAIN_SAMPLES = 1000
NUM_TEST_SAMPLES = 400
def create_config(batch_size):
return {
# todo: batch size needs to scale with # of workers
"batch_size": batch_size,
"fit_config": {
"steps_per_epoch": NUM_TRAIN_SAMPLES // batch_size
},
"evaluate_config": {
"steps": NUM_TEST_SAMPLES // batch_size,
}
}
def linear_dataset(a=2, size=1000):
x = np.random.rand(size)
y = x / 2
x = x.reshape((-1, 1))
y = y.reshape((-1, 1))
return x, y
def simple_dataset(config):
batch_size = config["batch_size"]
x_train, y_train = linear_dataset(size=NUM_TRAIN_SAMPLES)
x_test, y_test = linear_dataset(size=NUM_TEST_SAMPLES)
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
train_dataset = train_dataset.shuffle(NUM_TRAIN_SAMPLES).repeat().batch(
batch_size)
test_dataset = test_dataset.repeat().batch(batch_size)
return train_dataset, test_dataset
def simple_model(config):
model = Sequential([Dense(10, input_shape=(1, )), Dense(1)])
model.compile(
optimizer="sgd",
loss="mean_squared_error",
metrics=["mean_squared_error"])
return model
def train_example(num_replicas=1, batch_size=128, use_gpu=False):
trainer = TFTrainer(
model_creator=simple_model,
data_creator=simple_dataset,
num_replicas=num_replicas,
use_gpu=use_gpu,
verbose=True,
config=create_config(batch_size))
# model baseline performance
start_stats = trainer.validate()
print(start_stats)
# train for 2 epochs
trainer.train()
trainer.train()
# model performance after training (should improve)
end_stats = trainer.validate()
print(end_stats)
# sanity check that training worked
dloss = end_stats["validation_loss"] - start_stats["validation_loss"]
dmse = (end_stats["validation_mean_squared_error"] -
start_stats["validation_mean_squared_error"])
print(f"dLoss: {dloss}, dMSE: {dmse}")
if dloss > 0 or dmse > 0:
print("training sanity check failed. loss increased!")
else:
print("success!")
def tune_example(num_replicas=1, use_gpu=False):
config = {
"model_creator": simple_model,
"data_creator": simple_dataset,
"num_replicas": num_replicas,
"use_gpu": use_gpu,
"trainer_config": create_config(batch_size=128)
}
analysis = tune.run(
TFTrainable,
num_samples=2,
config=config,
stop={"training_iteration": 2},
verbose=1)
return analysis.get_best_config(metric="validation_loss", mode="min")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
parser.add_argument(
"--address",
required=False,
type=str,
help="the address to use for Ray")
parser.add_argument(
"--server-address",
type=str,
default=None,
required=False,
help="The address of server to connect to if using "
"Ray Client.")
parser.add_argument(
"--num-replicas",
"-n",
type=int,
default=1,
help="Sets number of replicas for training.")
parser.add_argument(
"--use-gpu",
action="store_true",
default=False,
help="Enables GPU training")
parser.add_argument(
"--tune", action="store_true", default=False, help="Tune training")
args, _ = parser.parse_known_args()
if args.smoke_test:
ray.init(num_cpus=2)
elif args.server_address:
ray.init(f"ray://{args.server_address}")
else:
ray.init(address=args.address)
if args.tune:
tune_example(num_replicas=args.num_replicas, use_gpu=args.use_gpu)
else:
train_example(num_replicas=args.num_replicas, use_gpu=args.use_gpu)