Ray Data Quickstart#
Get started with Ray Data’s Dataset
abstraction for distributed data processing.
This guide introduces you to the core capabilities of Ray Data:
Datasets#
Ray Data’s main abstraction is a Dataset
, which
represents a distributed collection of data. Datasets are specifically designed for machine learning workloads
and can efficiently handle data collections that exceed a single machine’s memory.
Loading data#
Create datasets from various sources including local files, Python objects, and cloud storage services like S3 or GCS. Ray Data seamlessly integrates with any filesystem supported by Arrow.
import ray
# Load a CSV dataset directly from S3
ds = ray.data.read_csv("s3://anonymous@air-example-data/iris.csv")
# Preview the first record
ds.show(limit=1)
{'sepal length (cm)': 5.1, 'sepal width (cm)': 3.5, 'petal length (cm)': 1.4, 'petal width (cm)': 0.2, 'target': 0}
To learn more about creating datasets from different sources, read Loading data.
Transforming data#
Apply user-defined functions (UDFs) to transform datasets. Ray automatically parallelizes these transformations across your cluster for better performance.
from typing import Dict
import numpy as np
# Define a transformation to compute a "petal area" attribute
def transform_batch(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
vec_a = batch["petal length (cm)"]
vec_b = batch["petal width (cm)"]
batch["petal area (cm^2)"] = vec_a * vec_b
return batch
# Apply the transformation to our dataset
transformed_ds = ds.map_batches(transform_batch)
# View the updated schema with the new column
# .materialize() will execute all the lazy transformations and
# materialize the dataset into object store memory
print(transformed_ds.materialize())
MaterializedDataset(
num_blocks=...,
num_rows=150,
schema={
sepal length (cm): double,
sepal width (cm): double,
petal length (cm): double,
petal width (cm): double,
target: int64,
petal area (cm^2): double
}
)
To explore more transformation capabilities, read Transforming data.
Consuming data#
Access dataset contents through convenient methods like take_batch()
and
iter_batches()
. You can also pass datasets directly to Ray Tasks or Actors
for distributed processing.
# Extract the first 3 rows as a batch for processing
print(transformed_ds.take_batch(batch_size=3))
{'sepal length (cm)': array([5.1, 4.9, 4.7]),
'sepal width (cm)': array([3.5, 3. , 3.2]),
'petal length (cm)': array([1.4, 1.4, 1.3]),
'petal width (cm)': array([0.2, 0.2, 0.2]),
'target': array([0, 0, 0]),
'petal area (cm^2)': array([0.28, 0.28, 0.26])}
For more details on working with dataset contents, see Iterating over Data and Saving Data.
Saving data#
Export processed datasets to a variety of formats and storage locations using methods
like write_parquet()
, write_csv()
, and more.
import os
# Save the transformed dataset as Parquet files
transformed_ds.write_parquet("/tmp/iris")
# Verify the files were created
print(os.listdir("/tmp/iris"))
['..._000000.parquet', '..._000001.parquet']
For more information on saving datasets, see Saving data.