ray-data

Distributed ML data preprocessing handles datasets larger than memory by using streaming execution across a cluster. This approach processes large files like Parquet incrementally, preventing out-of-memory errors during machine learning pipeline transformations.

Ray Data supports distributed ETL pipelines and batch inference by executing data transformations across clusters. It integrates natively with PyTorch, TensorFlow, and HuggingFace to streamline batch processing for machine learning workloads.

You can read and transform Parquet files for a distributed ML pipeline by loading data from storage like S3 and applying transformations such as column conversion. The streaming execution engine processes these transformations efficiently across multiple machines.

Distributed data processing with Ray Data supports GPU acceleration for compute-intensive ML tasks. This capability accelerates data preprocessing and batch inference workloads by utilizing GPU resources across single machines or large clusters.

Preprocessing terabytes of image data for computer vision model training is best handled by distributing image augmentation and resizing tasks across a cluster. This scalable approach manages large datasets efficiently without being constrained by single-machine memory limits.

Distributed ML data processing supports various formats including Parquet, CSV, JSON, and images. This format flexibility allows machine learning pipelines to ingest and transform diverse data types across single machines and large clusters.