Side by side
ONNX Runtime vs Ray Train
Both stand on deep learning software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Free plan AndroidiPhoneLinux+4
ONNX Runtime offers broad framework, language, and deployment support for machine-learning workloads. Windows developers should note the guidance to use WinML for new projects rather than DirectML.
Visit ONNX Runtime Free plan LinuxMacSelf-hosted+1
Ray Train brings distributed training, data processing, and experiment tracking into the Ray cluster workflow. Its security guidance recommends separate clusters when workloads need isolation.
Visit Ray TrainThe facts
| ONNX Runtime | Ray Train | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | Android, iPhone, Linux, Mac, Self-hosted, Web, Windows | Linux, Mac, Self-hosted, Windows |
| Lowest paid plan | Free or on request | Free or on request |
| Plans published | 1 | 1 |
| Maker | Not published | Not published |
| Website | onnxruntime.ai | ray.io |
| On deep learning software | #18 of 35 | #22 of 35 |
ONNX Runtime: what it does well
- Free, open-source runtime under MIT license
- Execution Providers enable hardware-specific acceleration
- Supports multiple model frameworks and languages
- Web and mobile inference options
- Custom builds can reduce package size
What to know first
- Nightly builds have limited support
- Nightly builds are discouraged for production
- DirectML is in sustained engineering
Ray Train: what it does well
- Integrates with PyTorch, XGBoost, JAX, and others.
- Supports Ray Data and framework-native datasets.
- Includes guides for metrics and experiment tracking.
- Supports distributed training on CPUs or GPUs.
- Open source and listed as free.
What to know first
- No job isolation within a cluster.
- No developer access controls within a cluster.
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