--- title: "horovod vs hyperopt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/horovod-horovod-vs-hyperopt-hyperopt" tools: ["horovod-horovod", "hyperopt-hyperopt"] --- # horovod vs hyperopt *GraphCanon updated Aug 4, 2026* ## Verdict Pick horovod if simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. [horovod](http://horovod.ai) reports 15k GitHub stars, 2.2k forks, and 406 open issues, last pushed Jul 29, 2026. [hyperopt](http://hyperopt.github.io/hyperopt) has 7.6k stars, 1.1k forks, and 9 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [horovod's repository](https://github.com/horovod/horovod) and [hyperopt's repository](https://github.com/hyperopt/hyperopt). | | [horovod](/tools/horovod-horovod.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Tagline | Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. | Distributed Asynchronous Hyperparameter Optimization in Python | | Stars | 14,695 | 7,598 | | Forks | 2,235 | 1,075 | | Open issues | 406 | 9 | | Language | Python | Python | | Adopt for | Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes. | Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. | | Persona | - | - | | Runtime | - | - | | License | Other | Other | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [horovod](/tools/horovod-horovod.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 4d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 406 | 9 | | Full report | [trust report](/tools/horovod-horovod/trust.md) | [trust report](/tools/hyperopt-hyperopt/trust.md) | ## Decision facts: horovod - **Adopt for:** Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes. ## Decision facts: hyperopt - **Adopt for:** Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. ## Choose when ### Choose horovod if… - Tags unique to horovod: deep-learning, distributed-training, keras, mxnet. - When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts. - More GitHub stars (15k vs 7.6k) - visibility, not fit. ### Choose hyperopt if… - Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization. - When you need to optimize machine learning model parameters on a distributed system asynchronously. - More recently updated (last pushed Aug 3, 2026). ## 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 hyperopt - If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. - Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options. ## Common questions ### What is the difference between horovod and hyperopt? horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.. hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose horovod over hyperopt? Choose horovod over hyperopt when Tags unique to horovod: deep-learning, distributed-training, keras, mxnet; When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts; More GitHub stars (15k vs 7.6k) - visibility, not fit. ### When should I choose hyperopt over horovod? Choose hyperopt over horovod when Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization; When you need to optimize machine learning model parameters on a distributed system asynchronously; More recently updated (last pushed Aug 3, 2026). ### 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 hyperopt? If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options. ### Is horovod or hyperopt more popular on GitHub? horovod has more GitHub stars (14,695 vs 7,598). Stars measure visibility, not whether either tool fits your constraints. ### Are horovod and hyperopt open source? Yes - both are open-source projects on GitHub (horovod: Other, hyperopt: Other). ### Where can I find alternatives to horovod or hyperopt? GraphCanon lists graph-backed alternatives at [horovod alternatives](/tools/horovod-horovod/alternatives) and [hyperopt alternatives](/tools/hyperopt-hyperopt/alternatives) ([horovod markdown twin](/tools/horovod-horovod/alternatives.md), [hyperopt markdown twin](/tools/hyperopt-hyperopt/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-hyperopt-hyperopt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, horovod or hyperopt? horovod: Archived. hyperopt: 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 hyperopt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [horovod trust report](/tools/horovod-horovod/trust); [hyperopt trust report](/tools/hyperopt-hyperopt/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/_