RayDP (Spark on Ray)

RayDP combines your Spark and Ray clusters, making it easy to do large scale data processing using the PySpark API and seemlessly use that data to train your models using TensorFlow and PyTorch.

For more information and examples, see the RayDP Github page: https://github.com/oap_project/raydp

Installing RayDP

RayDP can be installed from PyPI and supports PySpark 3.0 and 3.1.

Note

RayDP requires ray >= 1.2.0

Note

In order to run Spark, the head and worker nodes will need Java installed.

Creating a Spark Session

To create a spark session, call raydp.init_spark

For example,

import raydp

spark = raydp.init_spark(
  app_name = "example",
  num_executors = 10,
  executor_cores = 64,
  memory_per_executor = "256GB"
)

Deep Learning with a Spark DataFrame

Training a Spark DataFrame with TensorFlow

raydp.tf.TFEstimator provides an API for training with TensorFlow.

d = [{'age': 17 , 'grade': 12}]
df = spark.createDataFrame(d).collect()


from tensorflow import keras
model = keras.Sequential([])

estimator = raydp.tf.TFEstimator(
  model = model,
  num_worker = 10,
  feature_columns = ["age"],
  label_column = ["grade"]
)

estimator.fit_on_spark(df, test_df=None)

tensorflow_model = estimator.get_model()

Training a Spark DataFrame with PyTorch

Similarly, raydp.torch.TorchEstimator provides an API for training with PyTorch.

d = [{'age': 17 , 'grade': 12}]
df = spark.createDataFrame(d).collect()


import torch
model = torch.nn.Sequential()

estimator = raydp.tf.TFEstimator(
  model = model,
  num_worker = 10,
  feature_columns = ["age"],
  label_column = ["grade"]
)

estimator.fit_on_spark(df, test_df=None)

pytorch_model = estimator.get_model()