Ray Train
ray.io
Spec sheet
- Purpose
- Ray Train distributes model training compute to worker processes across a Ray cluster.Read 4 Oct 2026 · docs.ray.io
- 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.Read 4 Oct 2026 · docs.ray.io
- Security
- Ray supports built-in token authentication starting in version 2.52.0, while its security guidance calls for controlled networks and trusted code.Read 4 Oct 2026 · docs.ray.io
- Support
- The Ray site offers a community Slack, forums, and documentation, and says Anyscale offers hands-on training and expert support.Read 4 Oct 2026 · ray.io
- 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.Read 4 Oct 2026 · docs.ray.io
- Experiment tracking
- Ray Train has an experiment tracking user guide.Read 4 Oct 2026 · docs.ray.io
- Framework integrations
- Ray Train integrates with PyTorch, PyTorch Lightning, Hugging Face Transformers, XGBoost, JAX, DeepSpeed, TensorFlow and Keras, LightGBM, and Horovod.Read 4 Oct 2026 · docs.ray.io
- Monitoring
- Ray Train provides user guides for monitoring and logging metrics during training.Read 4 Oct 2026 · docs.ray.io
- 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.Read 4 Oct 2026 · docs.ray.io
- Scaling
- The homepage says Ray can scale from a laptop to thousands of GPUs and use heterogeneous GPUs and CPUs with independent scaling.Read 4 Oct 2026 · ray.io
- 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.Read 4 Oct 2026 · docs.ray.io
- Training workloads
- The homepage describes distributed training for generative AI foundation models, time-series models, and traditional machine-learning models such as XGBoost.Read 4 Oct 2026 · ray.io
- 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.Read 4 Oct 2026 · docs.ray.io
Plans and prices
Ray Train Free Pricing is not stated on the product pages reviewed; Ray is described as open source. Read 4 Oct 2026 · ray.io
Where Specifiction places it
- Deep Learning Software#25 of 357.4