raydp-nightly 2021.5.12.dev0

RayDP: Distributed Data Processing on Ray

RayDP

RayDP is a distributed data processing library that provides simple APIs for running Spark on Ray and integrating Spark with distributed deep learning and machine learning frameworks. RayDP makes it simple to build distributed end-to-end data analytics and AI pipeline. Instead of using lots of glue code or an orchestration framework to stitch multiple distributed programs, RayDP allows you to write Spark, PyTorch, Tensorflow, XGBoost code in a single python program with increased productivity and performance. You can build an end-to-end pipeline on a single Ray cluster by using Spark for data preprocessing, RaySGD or Horovod for distributed deep learning, RayTune for hyperparameter tuning and RayServe for model serving.

Spark on Ray

RayDP provides an API for starting a Spark job on Ray in your python program without a need to setup a Spark cluster manually. RayDP supports Ray as a Spark resource manger and runs Spark executors in Ray actors. RayDP utilizes Ray's in-memory object store to efficiently exchange data between Spark and other Ray libraries. You can use Spark to read the input data, process the data using SQL, Spark DataFrame, or Pandas (via Koalas) API, extract and transform features using Spark MLLib, and feed the output to deep learning and machine learning frameworks.

Integrating Spark with Deep Learning and Machine Learning Frameworks

MLDataset API

RayDP provides an API for creating a Ray MLDataset from a Spark dataframe. MLDataset represents a distributed dataset stored in Ray's in-memory object store. It supports transformation on each shard and can be converted to a PyTorch or Tensorflow dataset for distributed training. If you prefer to using Horovod on Ray or RaySGD for distributed training, you can use MLDataset to seamlessly integrate Spark with them.

Estimator API

RayDP also provides high level scikit-learn style Estimator APIs for distributed training. The Estimator APIs allow you to train a deep neural network directly on a Spark DataFrame, leveraging Ray’s ability to scale out across the cluster. The Estimator APIs are wrappers of RaySGD and hide the complexity of converting a Spark DataFrame to a PyTorch/Tensorflow dataset and distributing the training.

Installation

You can install latest RayDP using pip. RayDP requires Ray (>=1.3.0) and PySpark (>=3.0.0). Please also make sure java is installed and JAVA_HOME is set properly.

pip install raydp

Or you can install our nightly build:

pip install raydp-nightly

If you'd like to build and install the latest master, use the following command:

./build.sh
pip install dist/raydp*.whl

Getting Started

To start a Spark job on Ray, you can use the raydp.init_spark API. You can write Spark, PyTorch/Tensorflow, Ray code in the same python program to easily implement an end-to-end pipeline.

Classic Spark Word Count Example

After we use RayDP to initialize a Spark cluster, we can use Spark as usual.

import ray
import raydp

ray.init(address='auto')

spark = raydp.init_spark('word_count',
                         num_executors=2,
                         executor_cores=2,
                         executor_memory='1G')

df = spark.createDataFrame([('look',), ('spark',), ('tutorial',), ('spark',), ('look', ), ('python', )], ['word'])
df.show()
word_count = df.groupBy('word').count()
word_count.show()

raydp.stop_spark()

Integration with PyTorch

However, combined with other ray components, such as RaySGD and RayServe, we can easily build an end-to-end deep learning pipeline. In this example. we show how to use our estimator API, which is a wrapper around RaySGD, to perform data preprocessing using Spark, and train a model using PyTorch.

import ray
import raydp
from raydp.torch import TorchEstimator

ray.init()
spark = raydp.init_spark(app_name="RayDP example",
                         num_executors=2,
                         executor_cores=2,
                         executor_memory="4GB")

# Spark DataFrame Code 
df = spark.read.parquet(…) 
train_df = df.withColumn(…)

# PyTorch Code 
model = torch.nn.Sequential(torch.nn.Linear(2, 1)) 
optimizer = torch.optim.Adam(model.parameters())

# You can use the RayDP Estimator API or libraries like RaySGD for distributed training.
estimator = TorchEstimator(model=model, optimizer=optimizer, ...) 
estimator.fit_on_spark(train_df)

raydp.stop_spark()

Dynamic Executor Allocation

RayDP will support External Shuffle Serivce in the next release(0.3.0). You can also try it out by installing raydp-nightly. To enable it, you can either set spark.shuffle.service.enabled to true in $SPARK_HOME/conf/spark-defaults.conf, or you can provide a config to raydp.init_spark, as shown below:

raydp.init_spark(..., configs={"spark.shuffle.service.enabled": "true"})

The user-provided config will overwrite those specified in spark-defaults.conf.

Similarly, you can also enable Dynamic Executor Allocation this way. However, because Ray does not support object ownership tranferring now(1.3.0), you must use Dynamic Executor Allocation with data persistence. You can write the data frame in spark to HDFS as a parquet as shown below:

ds = RayMLDataset.from_spark(..., fs_directory="hdfs://host:port/your/directory")

More Examples

Not sure how to use RayDP? Check the examples folder. We have added many examples showing how RayDP works together with PyTorch, TensorFlow, XGBoost, Horovod, and so on. If you still cannot find what you want, feel free to post an issue to ask us!