Elephas: Distributed Deep Learning with Keras & Spark
Elephas is an extension of Keras, which allows you to run distributed deep learning models at
scale with Spark. Elephas currently supports a number of
applications, including:
- Data-parallel training of deep learning models
- Distributed hyper-parameter optimization
- Distributed training of ensemble models
Schematically, elephas works as follows.
Table of content:
Introduction
Elephas brings deep learning with Keras to Spark. Elephas intends to
keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which
can be run on massive data sets. For an introductory example, see the following
iPython notebook.
ἐλέφας is Greek for ivory and an accompanying project to κέρας, meaning horn. If this seems weird mentioning, like
a bad dream, you should confirm it actually is at the
Keras documentation.
Elephas also means elephant, as in stuffed yellow elephant.
Elephas implements a class of data-parallel algorithms on top of Keras, using Spark's RDDs and data frames.
Keras Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted
model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the
driver. The "master" model on the driver is updated by an optimizer, which takes gradients either synchronously or
asynchronously.
Getting started
Just install elephas from PyPI with, Spark will be installed through pyspark for you.
pip install elephas
That's it, you should now be able to run Elephas examples.
Basic Spark integration
After installing both Elephas, you can train a model as follows. First, create a local pyspark context
from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName('Elephas_App').setMaster('local[8]')
sc = SparkContext(conf=conf)
Next, you define and compile a Keras model
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(128, input_dim=784))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer=SGD())
and create an RDD from numpy arrays (or however you want to create an RDD)
from elephas.utils.rdd_utils import to_simple_rdd
rdd = to_simple_rdd(sc, x_train, y_train)
The basic model in Elephas is the SparkModel. You initialize a SparkModel by passing in a compiled Keras model,
an update frequency and a parallelization mode. After that you can simply fit the model on your RDD. Elephas fit
has the same options as a Keras model, so you can pass epochs, batch_size etc. as you're used to from Keras.
from elephas.spark_model import SparkModel
spark_model = SparkModel(model, frequency='epoch', mode='asynchronous')
spark_model.fit(rdd, epochs=20, batch_size=32, verbose=0, validation_split=0.1)
Your script can now be run using spark-submit
spark-submit --driver-memory 1G ./your_script.py
Increasing the driver memory even further may be necessary, as the set of parameters in a network may be very large
and collecting them on the driver eats up a lot of resources. See the examples folder for a few working examples.
Spark MLlib integration
Following up on the last example, to use Spark's MLlib library with Elephas, you create an RDD of LabeledPoints for
supervised training as follows
from elephas.utils.rdd_utils import to_labeled_point
lp_rdd = to_labeled_point(sc, x_train, y_train, categorical=True)
Training a given LabeledPoint-RDD is very similar to what we've seen already
from elephas.spark_model import SparkMLlibModel
spark_model = SparkMLlibModel(model, frequency='batch', mode='hogwild')
spark_model.train(lp_rdd, epochs=20, batch_size=32, verbose=0, validation_split=0.1,
categorical=True, nb_classes=nb_classes)
Spark ML integration
To train a model with a SparkML estimator on a data frame, use the following syntax.
df = to_data_frame(sc, x_train, y_train, categorical=True)
test_df = to_data_frame(sc, x_test, y_test, categorical=True)
estimator = ElephasEstimator(model, epochs=epochs, batch_size=batch_size, frequency='batch', mode='asynchronous',
categorical=True, nb_classes=nb_classes)
fitted_model = estimator.fit(df)
Fitting an estimator results in a SparkML transformer, which we can use for predictions and other evaluations by
calling the transform method on it.
prediction = fitted_model.transform(test_df)
pnl = prediction.select("label", "prediction")
pnl.show(100)
prediction_and_label= pnl.rdd.map(lambda row: (row.label, row.prediction))
metrics = MulticlassMetrics(prediction_and_label)
print(metrics.precision())
print(metrics.recall())
Distributed hyper-parameter optimization
Hyper-parameter optimization with elephas is based on hyperas, a convenience
wrapper for hyperopt and keras. Each Spark worker executes a number of trials, the results get collected and the best
model is returned. As the distributed mode in hyperopt (using MongoDB), is somewhat difficult to configure and error
prone at the time of writing, we chose to implement parallelization ourselves. Right now, the only available
optimization algorithm is random search.
The first part of this example is more or less directly taken from the hyperas documentation. We define data and model
as functions, hyper-parameter ranges are defined through braces. See the hyperas documentation for more on how
this works.
from __future__ import print_function
from hyperopt import STATUS_OK
from hyperas.distributions import choice, uniform
def data():
from keras.datasets import mnist
from keras.utils import np_utils
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
nb_classes = 10
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
return x_train, y_train, x_test, y_test
def model(x_train, y_train, x_test, y_test):
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([256, 512, 1024])}}))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms)
model.fit(x_train, y_train,
batch_size={{choice([64, 128])}},
nb_epoch=1,
show_accuracy=True,
verbose=2,
validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, show_accuracy=True, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model.to_yaml()}
Once the basic setup is defined, running the minimization is done in just a few lines of code:
from elephas.hyperparam import HyperParamModel
from pyspark import SparkContext, SparkConf
# Create Spark context
conf = SparkConf().setAppName('Elephas_Hyperparameter_Optimization').setMaster('local[8]')
sc = SparkContext(conf=conf)
# Define hyper-parameter model and run optimization
hyperparam_model = HyperParamModel(sc)
hyperparam_model.minimize(model=model, data=data, max_evals=5)
Distributed training of ensemble models
Building on the last section, it is possible to train ensemble models with elephas by means of running hyper-parameter
optimization on large search spaces and defining a resulting voting classifier on the top-n performing models.
With data and model defined as above, this is a simple as running
result = hyperparam_model.best_ensemble(nb_ensemble_models=10, model=model, data=data, max_evals=5)
In this example an ensemble of 10 models is built, based on optimization of at most 5 runs on each of the Spark workers.
Discussion
Premature parallelization may not be the root of all evil, but it may not always be the best idea to do so. Keep in
mind that more workers mean less data per worker and parallelizing a model is not an excuse for actual learning.
So, if you can perfectly well fit your data into memory and you're happy with training speed of the model consider
just using keras.
One exception to this rule may be that you're already working within the Spark ecosystem and want to leverage what's
there. The above SparkML example shows how to use evaluation modules from Spark and maybe you wish to further process
the outcome of an elephas model down the road. In this case, we recommend to use elephas as a simple wrapper by setting
num_workers=1.
Note that right now elephas restricts itself to data-parallel algorithms for two reasons. First, Spark simply makes it
very easy to distribute data. Second, neither Spark nor Theano make it particularly easy to split up the actual model
in parts, thus making model-parallelism practically impossible to realize.
Having said all that, we hope you learn to appreciate elephas as a pretty easy to setup and use playground for
data-parallel deep-learning algorithms.
Literature
[1] J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin, QV. Le, MZ. Mao, M’A. Ranzato, A. Senior, P. Tucker, K. Yang, and AY. Ng. Large Scale Distributed Deep Networks.
[2] F. Niu, B. Recht, C. Re, S.J. Wright HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
[3] C. Noel, S. Osindero. Dogwild! — Distributed Hogwild for CPU & GPU