Ray Train

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On the record

Data integration

Ray Train integrates with Ray Data for streaming data loading and preprocessing, and also supports framework-native data utilities such as PyTorch Dataset and Hugging Face Dataset.docs.ray.io · read 4 Oct 2026

Experiment tracking

Ray Train has an experiment tracking user guide.docs.ray.io · read 4 Oct 2026

Framework integrations

Ray Train integrates with PyTorch, PyTorch Lightning, Hugging Face Transformers, XGBoost, JAX, DeepSpeed, TensorFlow and Keras, LightGBM, and Horovod.docs.ray.io · read 4 Oct 2026

Intended users

Ray’s security documentation describes Ray developers running local single-node clusters or remote multi-node clusters on infrastructure provided by platform providers.docs.ray.io · read 4 Oct 2026

Monitoring

Ray Train provides user guides for monitoring and logging metrics during training.docs.ray.io · read 4 Oct 2026

Preprocessing

Ray Data can distribute heavy preprocessing across CPU nodes so it does not bottleneck GPU training, and Ray Train can split data across workers on the fly.docs.ray.io · read 4 Oct 2026

Purpose

Ray Train distributes model training compute to worker processes across a Ray cluster.docs.ray.io · read 4 Oct 2026

Scaling

The homepage says Ray can scale from a laptop to thousands of GPUs and use heterogeneous GPUs and CPUs with independent scaling.ray.io · read 4 Oct 2026

Security

Ray supports built-in token authentication starting in version 2.52.0, while its security guidance calls for controlled networks and trusted code.docs.ray.io · read 4 Oct 2026

Security limitation

Ray does not provide isolation between jobs or access controls for developers within a cluster; its security guidance recommends separate clusters where workload isolation is required.docs.ray.io · read 4 Oct 2026

Support

The Ray site offers a community Slack, forums, and documentation, and says Anyscale offers hands-on training and expert support.ray.io · read 4 Oct 2026

Training workloads

The homepage describes distributed training for generative AI foundation models, time-series models, and traditional machine-learning models such as XGBoost.ray.io · read 4 Oct 2026

Workers and resources

Ray Train uses a training function, workers, a scaling configuration with CPU or GPU resources, and a Trainer to execute a distributed training job.docs.ray.io · read 4 Oct 2026

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