Ray Data
The distributed data processing component of the Ray ecosystem. It employs a streaming execution engine and DAG pipeline architecture, supports parallel data preprocessing across nodes, and is optimized for large-scale multimodal AI training data processing.
Timeline (last 90 days)
Ray Data uses a streaming execution engine to build data reading, transformation, and model training into a directed acyclic graph (DAG) pipeline, enabling asynchronous prefetching of the next batch of data while the GPU is computing
Ray Data's advantage over PyTorch's IterableDataset lies in decoupling data processing compute from training compute, allowing the two to scale elastically and independently
All Facts (2)
Ray Data uses a streaming execution engine to build data reading, transformation, and model training into a directed acyclic graph (DAG) pipeline, enabling asynchronous prefetching of the next batch of data while the GPU is computing
50%UnverifiedRay Data's advantage over PyTorch's IterableDataset lies in decoupling data processing compute from training compute, allowing the two to scale elastically and independently
50%