--- title: "horovod vs ray" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/horovod-horovod-vs-ray-project-ray" tools: ["horovod-horovod", "ray-project-ray"] --- # horovod vs ray *GraphCanon updated Sep 26, 2026* ## Verdict Pick horovod if simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes; pick ray if ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. [horovod](http://horovod.ai) reports 15k GitHub stars, 2.2k forks, and 407 open issues, last pushed Sep 17, 2026. [ray](https://ray.io) has 44k stars, 8.1k forks, and 3.6k open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [horovod's repository](https://github.com/horovod/horovod) and [ray's repository](https://github.com/ray-project/ray). | | [horovod](/tools/horovod-horovod.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Tagline | Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. | Unified framework for scaling AI and Python applications | | Stars | 14,682 | 43,863 | | Forks | 2,232 | 8,052 | | Open issues | 407 | 3,590 | | Language | Python | Python | | Adopt for | Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes. | Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [horovod](/tools/horovod-horovod.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 8d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 407 | 3.6k | | Stars delta | -13 (30d) | +337 (30d) | | Open issues delta | +1 (30d) | +107 (30d) | | Full report | [trust report](/tools/horovod-horovod/trust.md) | [trust report](/tools/ray-project-ray/trust.md) | ## Decision facts: horovod - **Adopt for:** Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes. ## Decision facts: ray - **Adopt for:** Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. ## Choose when ### Choose horovod if… - License: horovod is Other, ray is Apache-2.0. - Tags unique to horovod: distributed training, keras, mxnet, pytorch. - When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts. ### Choose ray if… - License: ray is Apache-2.0, horovod is Other. - Tags unique to ray: data-science, deployment, distributed, hyperparameter-optimization. - Also covers Developer Tools, Evaluation & Observability, Inference & Serving. - When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ## When NOT to use horovod - Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility. - Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch. ## When NOT to use ray - If your project is limited to single-node development and does not require scaling across multiple machines or clusters. - When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU - If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ## Common questions ### What is the difference between horovod and ray? horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.. ray: Unified framework for scaling AI and Python applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose horovod over ray? Choose horovod over ray when License: horovod is Other, ray is Apache-2.0; Tags unique to horovod: distributed training, keras, mxnet, pytorch; When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts. ### When should I choose ray over horovod? Choose ray over horovod when License: ray is Apache-2.0, horovod is Other; Tags unique to ray: data-science, deployment, distributed, hyperparameter-optimization; Also covers Developer Tools, Evaluation & Observability, Inference & Serving; When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ### When should I avoid horovod? Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility. Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch. ### When should I avoid ray? If your project is limited to single-node development and does not require scaling across multiple machines or clusters. When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ### Is horovod or ray more popular on GitHub? ray has more GitHub stars (43,863 vs 14,682). Stars measure visibility, not whether either tool fits your constraints. ### Are horovod and ray open source? Yes - both are open-source projects on GitHub (horovod: Other, ray: Apache-2.0). ### Where can I find alternatives to horovod or ray? GraphCanon lists graph-backed alternatives at [horovod alternatives](/tools/horovod-horovod/alternatives) and [ray alternatives](/tools/ray-project-ray/alternatives) ([horovod markdown twin](/tools/horovod-horovod/alternatives.md), [ray markdown twin](/tools/ray-project-ray/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/horovod-horovod-vs-ray-project-ray.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, horovod or ray? horovod: Archived. ray: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for horovod and ray? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [horovod trust report](/tools/horovod-horovod/trust); [ray trust report](/tools/ray-project-ray/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=horovod-horovod`](/api/graphcanon/graph?tool=horovod-horovod) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_