ray-data

Streaming execution enables distributed data processing for machine learning workloads that exceed available memory. It handles large datasets by streaming execution across CPU and GPU nodes, efficiently processing terabytes of data without loading everything into memory at once.

Yes, Ray Data integrates seamlessly with PyTorch and TensorFlow for ML pipelines. It supports multi-modal data loading, allowing you to preprocess image data for computer vision model training and convert the results directly into a PyTorch dataset.

Distributed ETL pipelines can be run using this Skill to read and process various data formats including Parquet, CSV, and JSON. It scales data loading and transformation across multiple nodes, distributing the workload across a cluster for faster iteration.

Yes, Ray Data supports streaming execution across both CPU and GPU for batch inference and data preprocessing. It is designed to scale data processing effortlessly across available compute resources for machine learning workloads.

You can read all Parquet files directly from an S3 bucket and convert them into a PyTorch dataset. This Skill enables scalable data loading and transformation, integrating with ML frameworks to prepare data for training pipelines.