Ray Datasets: Distributed Data Preprocessing¶

Ray Datasets are the standard way to load and exchange data in Ray libraries and applications. They provide basic distributed data transformations such as maps (map_batches), global and grouped aggregations (GroupedDataset), and shuffling operations (random_shuffle, sort, repartition), and are compatible with a variety of file formats, data sources, and distributed frameworks.

Here’s an overview of the integrations with other processing frameworks, file formats, and supported operations, as well as a glimpse at the Ray Datasets API.

Check our compatibility matrix to see if your favorite format is already supported.

Data Loading and Preprocessing for ML Training¶

Ray Datasets is designed to load and preprocess data for distributed ML training pipelines. Compared to other loading solutions, Datasets are more flexible (e.g., can express higher-quality per-epoch global shuffles) and provides higher overall performance.

Ray Datasets is not intended as a replacement for more general data processing systems. Learn more about how Ray Datasets works with other ETL systems.

Datasets for Parallel Compute¶

Datasets also simplifies general purpose parallel GPU and CPU compute in Ray; for instance, for GPU batch inference. It provides a higher-level API for Ray tasks and actors for such embarrassingly parallel compute, internally handling operations like batching, pipelining, and memory management.

As part of the Ray ecosystem, Ray Datasets can leverage the full functionality of Ray’s distributed scheduler, e.g., using actors for optimizing setup time and GPU scheduling.

Where to Go from Here?¶

As new user of Ray Datasets, you may want to start with our Getting Started guide. If you’ve run your first examples already, you might want to dive into Ray Datasets’ key concepts or our User Guide instead. Advanced users can refer directly to the Ray Datasets API reference for their projects.

Getting Started

Start with our quick start tutorials for working with Datasets and Dataset Pipelines. These concrete examples will give you an idea of how to use Ray Datasets.

Get Started with Ray Datasets

Key Concepts

Understand the key concepts behind Ray Datasets. Learn what Datasets and Dataset Pipelines are and how they get executed in Ray Datasets.

Learn Key Concepts

User Guides

Learn how to create datasets, save datasets, transform datasets, access and exchange datasets, pipeline transformations, load and process data for ML, work with tensor data, or use pipelines.

Start Using Ray Datasets

Examples

Find both simple and scaling-out examples of using Ray Datasets for data processing and ML ingest.

Ray Datasets Examples

Ray Datasets FAQ

Find answers to commonly asked questions in our detailed FAQ.

Ray Datasets FAQ

API

Get more in-depth information about the Ray Datasets API.

Read the API Reference

Other Data Processing Solutions

For running ETL pipelines, check out Spark-on-Ray. For scaling up your data science workloads, check out Dask-on-Ray, Modin, and Mars-on-Ray.

Check Out Other Data Processing Options

Datasource Compatibility¶

Ray Datasets supports reading and writing many file formats. The following compatibility matrices will help you understand which formats are currently available.

If none of these meet your needs, please reach out on Discourse or open a feature request on the Ray GitHub repo, and check out our guide for implementing a custom Datasets datasource if you’re interested in rolling your own integration!

Supported Input Formats¶

Input compatibility matrix¶
Input Type	Read API	Status
CSV File Format	`ray.data.read_csv()`	✅
JSON File Format	`ray.data.read_json()`	✅
Parquet File Format	`ray.data.read_parquet()`	✅
Numpy File Format	`ray.data.read_numpy()`	✅
Text Files	`ray.data.read_text()`	✅
Binary Files	`ray.data.read_binary_files()`	✅
TFRecord Files	`ray.data.read_tfrecords()`	🚧
Python Objects	`ray.data.from_items()`	✅
Spark Dataframe	`ray.data.from_spark()`	✅
Dask Dataframe	`ray.data.from_dask()`	✅
Modin Dataframe	`ray.data.from_modin()`	✅
MARS Dataframe	`ray.data.from_mars()`	✅
Pandas Dataframe Objects	`ray.data.from_pandas()`	✅
NumPy ndarray Objects	`ray.data.from_numpy()`	✅
Arrow Table Objects	`ray.data.from_arrow()`	✅
🤗 (Hugging Face) Dataset	`ray.data.from_huggingface()`	✅
Custom Datasource	`ray.data.read_datasource()`	✅

Supported Output Formats¶

Output compatibility matrix¶
Output Type	Dataset API	Status
CSV File Format	`ds.write_csv()`	✅
JSON File Format	`ds.write_json()`	✅
Parquet File Format	`ds.write_parquet()`	✅
Numpy File Format	`ds.write_numpy()`	✅
Spark Dataframe	`ds.to_spark()`	✅
Dask Dataframe	`ds.to_dask()`	✅
Modin Dataframe	`ds.to_modin()`	✅
MARS Dataframe	`ds.to_mars()`	✅
Arrow Table Objects	`ds.to_arrow_refs()`	✅
Arrow Table Iterator	`ds.iter_batches(batch_format="pyarrow")`	✅
Single Pandas Dataframe	`ds.to_pandas()`	✅
Pandas Dataframe Objects	`ds.to_pandas_refs()`	✅
NumPy ndarray Objects	`ds.to_numpy_refs()`	✅
Pandas Dataframe Iterator	`ds.iter_batches(batch_format="pandas")`	✅
PyTorch Tensor Iterator	`ds.iter_torch_batches()`	✅
TensorFlow Tensor Iterator	`ds.iter_tf_batches()`	✅
Random Access Dataset	`ds.to_random_access_dataset()`	✅
Custom Datasource	`ds.write_datasource()`	✅

Learn More¶

[slides] Talk given at PyData 2021
[blog] Data Ingest in a Third Generation ML Architecture
[blog] Building an end-to-end ML pipeline using Mars and XGBoost on Ray
[blog] Ray Datasets for large-scale machine learning ingest and scoring

Contribute¶

Contributions to Ray Datasets are welcome! There are many potential improvements, including:

Supporting more data sources and transforms.
Integration with more ecosystem libraries.
Adding features such as join().
Performance optimizations.

Ray 3.0.0.dev0