Ray Train

Ray Train is ranked #9 of 35 in deep learning software on The Geeks Club. It runs on Linux, macOS, Self-hosted, Windows. There is a free plan.

Ray Train plans and pricing

All plans

Ray Train Free Pricing is not stated on the product pages reviewed; Ray is described as open source. ray.io · 4 Oct 2026

Compared on deep learning software

Free plan: Yes
Training mode: both
Deployment targets: multiple
GPU acceleration: Yes
Distributed training: Yes
Supported languages: Python

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Ray Train

Purpose: Ray Train distributes model training compute to worker processes across a Ray cluster.
Framework integrations: Ray Train integrates with PyTorch, PyTorch Lightning, Hugging Face Transformers, XGBoost, JAX, DeepSpeed, TensorFlow and Keras, LightGBM, and Horovod.
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.
Scaling: The homepage says Ray can scale from a laptop to thousands of GPUs and use heterogeneous GPUs and CPUs with independent scaling.
Training workloads: The homepage describes distributed training for generative AI foundation models, time-series models, and traditional machine-learning models such as XGBoost.
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.
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.
Monitoring: Ray Train provides user guides for monitoring and logging metrics during training.
Experiment tracking: Ray Train has an experiment tracking user guide.
Security: Ray supports built-in token authentication starting in version 2.52.0, while its security guidance calls for controlled networks and trusted code.
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.
Support: The Ray site offers a community Slack, forums, and documentation, and says Anyscale offers hands-on training and expert support.
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.
Price from: Free

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Where it ranks on The Geeks Club

Best Deep Learning Software in 2026#9 of 35

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Sources

docs.ray.io/en/latest/train/overview.html· checked 4 Oct 2026
docs.ray.io/en/latest/train/user-guides/data-loadin· checked 4 Oct 2026
ray.io· checked 4 Oct 2026
docs.ray.io/en/latest/train/user-guides/monitoring-· checked 4 Oct 2026
docs.ray.io/en/latest/train/user-guides/experiment-· checked 4 Oct 2026
docs.ray.io/en/master/ray-security/index.html· checked 4 Oct 2026