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https://github.com/danielenricocahall/elephas
Distributed Deep learning with Keras & Spark
https://github.com/danielenricocahall/elephas
Last synced: 3 months ago
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Distributed Deep learning with Keras & Spark
- Host: GitHub
- URL: https://github.com/danielenricocahall/elephas
- Owner: danielenricocahall
- License: mit
- Fork: true (maxpumperla/elephas)
- Created: 2020-12-27T21:55:38.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-01T16:44:37.000Z (3 months ago)
- Last Synced: 2024-08-02T03:14:28.851Z (3 months ago)
- Language: Python
- Homepage:
- Size: 6.97 MB
- Stars: 17
- Watchers: 1
- Forks: 5
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-list - Elephas - Distributed Deep learning with Keras & Spark. (Deep Learning Framework / Deployment & Distribution)
README
# Elephas: Distributed Deep Learning with Keras & Spark
##
[](https://github.com/danielenricocahall/elephas/actions/workflows/ci.yaml/badge.svg)
[](https://github.com/danielenricocahall/elephas/blob/master/LICENSE)
[](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11-blue)
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.
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 classification only and in the `"synchronous"` training mode. In future releases, we hope to expand this to other types of models (e.g; generative) 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)
model.compile(optimizer=SGD(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
spark_model = SparkHFModel(model, num_workers=num_workers, mode="synchronous", tokenizer=tokenizer)
spark_model.fit(rdd, epochs=epochs, batch_size=batch_size)
# Run inference on trained Spark model
predictions = spark_model.predict(spark_context.parallelize(x_test))
```
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.
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