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https://github.com/danielenricocahall/elephas

Distributed Deep learning with Keras & Spark
https://github.com/danielenricocahall/elephas

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Distributed Deep learning with Keras & Spark

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# Elephas: Distributed Deep Learning with Keras & Spark 



![Elephas](https://raw.githubusercontent.com/danielenricocahall/elephas/master/elephas-logo.png)



## 



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Elephas is an extension of [Keras](http://keras.io), which allows you to run distributed deep learning models at 

scale with [Spark](http://spark.apache.org). Elephas currently supports a number of 

applications, including:



- [Data-parallel training of deep learning models](#basic-spark-integration)

- [Distributed inference and evaluation of deep learning models](#distributed-inference-and-evaluation)

- [~~Distributed training of ensemble models~~](#distributed-training-of-ensemble-models)  (removed as of 3.0.0)

- [~~Distributed hyper-parameter optimization~~](#distributed-hyper-parameter-optimization)  (removed as of 3.0.0)

- [Distributed training and inference with Hugging Face models](#hugging-face-models-training-and-inference) (added as 6.0.0)



Schematically, elephas works as follows.



![Elephas](https://raw.githubusercontent.com/danielenricocahall/elephas/master/elephas.gif)



Table of content:

* [Elephas: Distributed Deep Learning with Keras & Spark](#elephas-distributed-deep-learning-with-keras-&-spark-)

  * [Introduction](#introduction)

  * [Getting started](#getting-started)

  * [Basic Spark integration](#basic-spark-integration)

  * [Distributed Inference and Evaluation](#distributed-inference-and-evaluation)

  * [Spark MLlib integration](#spark-mllib-integration)

  * [Spark ML integration](#spark-ml-integration)

  * [Hadoop integration](#hadoop-integration)

  * [Distributed hyper-parameter optimization](#distributed-hyper-parameter-optimization)

  * [Distributed training of ensemble models](#distributed-training-of-ensemble-models)

  * [Discussion](#discussion)

  * [Literature](#literature)



## Introduction

Elephas brings deep learning with [Keras](http://keras.io) to [Spark](http://spark.apache.org). 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](https://github.com/danielenricocahall/elephas/blob/master/examples/Spark_ML_Pipeline.ipynb).



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](https://github.com/fchollet/keras/blob/master/README.md). 

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

```python

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

```python

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Dropout, Activation

from tensorflow.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)

```python

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 tensorflow.keras.



```python

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

```bash

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.



## Distributed Inference and Evaluation



The `SparkModel` can also be used for distributed inference (prediction) and evaluation. Similar to the `fit` method,  the `predict` and `evaluate` methods

conform to the Keras Model API. 



```python

from elephas.spark_model import SparkModel



# create/train the model, similar to the previous section (Basic Spark Integration)

model = ...

spark_model = SparkModel(model, ...)

spark_model.fit(...)



x_test, y_test = ... # load test data



predictions = spark_model.predict(x_test) # perform inference

evaluation = spark_model.evaluate(x_test, y_test) # perform evaluation/scoring

```

The paradigm is identical to the data parallelism in training, as the model is serialized and shipped to the workers and used to evaluate a chunk of the testing data. The predict method will take either a numpy array or an RDD.



## 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



```python

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



```python

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.

```python

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.



```python

prediction = fitted_model.transform(test_df)

pnl = prediction.select("label", "prediction")

pnl.show(100)

import numpy as np

prediction_and_label = pnl.rdd.map(lambda row: (row.label, float(np.argmax(row.prediction))))



metrics = MulticlassMetrics(prediction_and_label)

print(metrics.weightedPrecision)

print(metrics.weightedRecall)

```



If the model utilizes custom activation function, layer, or loss function, that will need to be supplied using the `set_custom_objects` method:



```python

def custom_activation(x):

    ...

class CustomLayer(Layer):

    ...

model = Sequential()

model.add(CustomLayer(...))



estimator = ElephasEstimator(model, epochs=epochs, batch_size=batch_size)

estimator.set_custom_objects({'custom_activation': custom_activation, 'CustomLayer': CustomLayer})

```



## Hadoop Integration



In addition to saving locally, models may be saved directly into a network-accessible Hadoop cluster.



```python

spark_model.save('/absolute/file/path/model.h5', to_hadoop=True)

```



Models saved on a network-accessible Hadoop cluster may be loaded as follows.



```python

from elephas.spark_model import load_spark_model



spark_model = load_spark_model('/absolute/file/path/model.h5', from_hadoop=True)

```



## Distributed hyper-parameter optimization



**UPDATE**: As of 3.0.0, Hyper-parameter optimization features have been removed, since Hyperas is no longer active and was causing versioning compatibility issues. To use these features, install version 2.1 or below.



Hyper-parameter optimization with elephas is based on [hyperas](https://github.com/maxpumperla/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.



```python

from hyperopt import STATUS_OK

from hyperas.distributions import choice, uniform



def data():

    from tensorflow.keras.datasets import mnist

    from tensorflow.keras.utils import to_categorical

    (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 = to_categorical(y_train, nb_classes)

    y_test = 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 tensorflow.keras.models import Sequential

    from tensorflow.keras.layers import Dense, Dropout, Activation

    from tensorflow.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_json()}

```



Once the basic setup is defined, running the minimization is done in just a few lines of code:



```python

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

**UPDATE**: As of 3.0.0, Hyper-parameter optimization features have been removed, since Hyperas is no longer active and was causing versioning compatibility issues. To use these features, install version 2.1 or below.



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



```python

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.



## Hugging Face Models Training and Inference

As of 6.0.0, Elephas now supports distributed training (and inference) with [HuggingFace](https://huggingface.co/) models (using the Tensorflow/Keras backend), currently for text classification and causal langugage modeling only, and in the `"synchronous"` training mode. In future releases, we hope to expand this to other types of models and the `"asynchronous"` and `"hogwild"` training modes. This can be accomplished using the `SparkHFModel`:



```python 

from elephas.spark_model import SparkHFModel

from elephas.utils.rdd_utils import to_simple_rdd

from sklearn.datasets import fetch_20newsgroups

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

from tensorflow.keras.optimizers import SGD

batch_size = ...

epochs = ...

num_workers = ...



newsgroups = fetch_20newsgroups(subset='train')

x = newsgroups.data

y = newsgroups.target



encoder = LabelEncoder()

y_encoded = encoder.fit_transform(y)



x_train, x_test, y_train, y_test = train_test_split(x, y_encoded, test_size=0.2)



model_name = 'albert-base-v2'



# Note: the expectation is that text data is being supplied - tokenization is handled during training

rdd = to_simple_rdd(spark_context, x_train, y_train)



model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(np.unique(y_encoded)))

tokenizer = AutoTokenizer.from_pretrained(model_name)

tokenizer_kwargs = {'padding': True, 'truncation': True, ...}



model.compile(optimizer=SGD(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

spark_model = SparkHFModel(model, num_workers=num_workers, mode="synchronous", tokenizer=tokenizer, tokenizer_kwargs=tokenizer_kwargs, loader=TFAutoModelForSequenceClassification)



spark_model.fit(rdd, epochs=epochs, batch_size=batch_size)



predictions = spark_model.predict(spark_context.parallelize(x_test))

```

More examples can be seen in the `examples` directory, namely `"hf_causal_modeling.py"` and `"hf_text_classification.py"`.



The computational model is the same as for Keras models, except the model is serialized and deserialized differently due to differences in the HuggingFace API. 



To use this capability, just install this package with the `huggingface` extra:



```bash

pip install elephas[huggingface]

```



## 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](http://research.google.com/archive/large_deep_networks_nips2012.html).



[2] F. Niu, B. Recht, C. Re, S.J. Wright [HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent](http://arxiv.org/abs/1106.5730)



[3] C. Noel, S. Osindero. [Dogwild! — Distributed Hogwild for CPU & GPU](http://stanford.edu/~rezab/nips2014workshop/submits/dogwild.pdf)



## Maintainers / Contributions



This great project was started by Max Pumperla, and is currently maintained by Daniel Cahall (https://github.com/danielenricocahall). If you have any questions, please feel free to open up an issue or send an email to [email protected]. If you want to contribute, feel free to submit a PR, or start a conversation about how we can go about implementing something.



## Star History



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