RayDP: Distributed data processing library that provides simple APIs for running Spark on Ray and integrating Spark with distributed deep learning and machine learning frameworks.





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RayDP is a distributed data processing library that provides simple APIs for running Spark/MPI 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.


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:

pip install dist/raydp*.whl

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.

Classic Spark Word Count Example

To start a Spark job on Ray, you can use the raydp.init_spark API. After we use RayDP to initialize a Spark cluster, we can use Spark as usual.

import ray
import raydp

# connect to ray cluster

# create a Spark cluster with specified resource requirements
spark = raydp.init_spark('word_count',

# normal data processesing with Spark
df = spark.createDataFrame([('look',), ('spark',), ('tutorial',), ('spark',), ('look', ), ('python', )], ['word'])
word_count = df.groupBy('word').count()

# stop the spark cluster

Dynamic Resource Allocation

RayDP now supports External Shuffle Serivce. To enable it, you can either set spark.shuffle.service.enabled to true in 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. By default Spark will load spark-defaults.conf from $SPARK_HOME/conf, you can also modify this location by setting SPARK_CONF_DIR.

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")

Spark Submit

RayDP provides a substitute for spark-submit in Apache Spark. You can run your java or scala application on RayDP cluster by using bin/raydp-submit. You can add it to PATH for convenience. When using raydp-submit, you should specify number of executors, number of cores and memory each executor by Spark properties, such as --conf spark.executor.cores=1, --conf spark.executor.instances=1 and --conf spark.executor.memory=500m. raydp-submit only supports Ray cluster. Spark standalone, Apache Mesos, Apache Yarn are not supported, please use traditional spark-submit in that case. For the same reason, you do not need to specify --master in the command. Besides, RayDP does not support cluster as deploy-mode.

Integrating Spark with Deep Learning and Machine Learning Frameworks

Combined with other ray components, such as RaySGD and RayServe, we can easily build an end-to-end deep learning pipeline.

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.

import ray
import raydp
from raydp.torch import TorchEstimator

spark = raydp.init_spark(app_name="RayDP example",
# 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, ...) 


MPI on Ray

RayDP also provides a simple API to running MPI job on top of Ray. Currently, we support three types of MPI: intel_mpi, openmpi and MPICH. You can refer doc/mpi.md for more details.

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!

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