Ray Train
Ray Train distributes model-training compute among worker processes in a Ray cluster.
What Ray Train is
Ray Train distributes model-training compute among worker processes in a Ray cluster. A training function, workers, CPU or GPU resource settings, and a Trainer are used to execute distributed jobs. The project describes training workloads for generative AI foundation models, time-series models, and traditional machine-learning models such as XGBoost. It integrates with PyTorch, PyTorch Lightning, Hugging Face Transformers, XGBoost, JAX, DeepSpeed, TensorFlow and Keras, LightGBM, and Horovod. Ray Data supports streaming data loading and preprocessing, while framework-native options include PyTorch Dataset and Hugging Face Dataset. Ray Data can distribute heavy preprocessing across CPU nodes, and Ray Train can split data among workers during a job. Guides cover metrics monitoring, logging, and experiment tracking. Ray describes scaling from a laptop to thousands of GPUs, with heterogeneous CPU and GPU resources that can scale independently. Ray is open source and supports Linux, macOS, Windows, and self-hosted use. Built-in token authentication is supported starting in version 2.52.0, but the security guidance calls for controlled networks and trusted code; Ray does not isolate jobs or provide developer access controls within a cluster.
Good
- Distributes training across worker processes.
- Integrates with major machine-learning frameworks.
- Supports Ray Data and framework-native datasets.
- Guides cover monitoring, logging, and experiment tracking.
- Available for Linux, macOS, Windows, and self-hosted use.
Watch out
- No job isolation within a cluster.
- No developer access controls within a cluster.
- Security guidance calls for controlled networks and trusted code.
Who it suits
Ray Train suits developers running local single-node or remote multi-node clusters who need distributed model training. It is relevant to teams using supported machine-learning frameworks and CPU or GPU resources.
Verdict
Ray Train provides a distributed training workflow with framework integrations, data preprocessing options, and guides for tracking runs. Cluster security requires attention: jobs are not isolated, and the project recommends separate clusters when workload isolation is needed.
Plans and prices
From Ray Train's own pricing page, checked 4 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Ray Train | Free | Pricing is not stated on the product pages reviewed; Ray is described as open source. |
The facts
Each one read from Ray Train's own pages, with the date it was checked.
What it does
- 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.checked 4 Oct 2026
- Experiment tracking
- Ray Train has an experiment tracking user guide.checked 4 Oct 2026
- Framework integrations
- Ray Train integrates with PyTorch, PyTorch Lightning, Hugging Face Transformers, XGBoost, JAX, DeepSpeed, TensorFlow and Keras, LightGBM, and Horovod.checked 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.checked 4 Oct 2026
- Monitoring
- Ray Train provides user guides for monitoring and logging metrics during training.checked 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.checked 4 Oct 2026
- Purpose
- Ray Train distributes model training compute to worker processes across a Ray cluster.checked 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.checked 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.checked 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.checked 4 Oct 2026
- Support
- The Ray site offers a community Slack, forums, and documentation, and says Anyscale offers hands-on training and expert support.checked 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.checked 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.checked 4 Oct 2026
Alternatives to Ray Train
- 1
Paperspace Gradient
Free planOpen sourceWebAPIExtensionLinux8.3From $8 a month - 2
Apache TVM
Free planOpen sourceAndroidiOSWebAPI7.8Free plan - 3
Lightning AI Studios
Free planWebAPILinuxmacOS7.8From $20 a month - 4
ONNX Runtime
Free planAndroidiOSWebLinux7.2Free plan - 5
Ludwig
Free planAPILinuxmacOSSelf-hosted6.7Free plan - 6
MegEngine
Free planAndroidiOSLinuxmacOS6.7Free plan