Comparison
horovod vs hyperopt
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.
Markdown twin · horovod alternatives · hyperopt alternatives
GraphCanon updated Oct 2, 2026
14views this month
Trust & integrity
| Signal | horovod | hyperopt |
|---|---|---|
| Maintenance | Archived (8d since push) As of Sep 26, 2026 · github_public_v1 | Very active (3d since push) As of Oct 2, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 26, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 2, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | No lockfile (source not queried) As of Aug 2, 2026 · deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Published findings As of Jul 26, 2026 · openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- horovod
- Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
- hyperopt
- Distributed Asynchronous Hyperparameter Optimization in Python
Stars
- horovod
- 15k
- hyperopt
- 7.6k
Forks
- horovod
- 2.2k
- hyperopt
- 1.1k
Open issues
- horovod
- 407
- hyperopt
- 10
Language
- horovod
- Python
- hyperopt
- Python
Adopt for
- horovod
- Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.
- hyperopt
- Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
Persona
- horovod
- -
- hyperopt
- -
Runtime
- horovod
- -
- hyperopt
- -
License
- horovod
- Other
- hyperopt
- Other
Last pushed
- horovod
- Sep 17, 2026
- hyperopt
- Sep 28, 2026
Categories
- horovod
- Model Training
- hyperopt
- Model Training
Trust and health
Maintenance
- horovod
- Archived (8%)
- hyperopt
- Very active (96%)
Days since push
- horovod
- 8d
- hyperopt
- 3d
Archived on GitHub
- horovod
- Yes
- hyperopt
- No
Open issues (now)
- horovod
- 407
- hyperopt
- 10
Stars delta
- horovod
- -13 (30d)
- hyperopt
- -3 (30d)
deps.dev advisories
- horovod
- No lockfile (source not queried)
- hyperopt
- Not queried
OpenSSF Scorecard
- horovod
- Published findings
- hyperopt
- Not queried
Full report
- horovod
- Trust report
- hyperopt
- Trust report
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.
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.
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 Sep 28, 2026).
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (horovod/horovod) · observed Sep 26, 2026
- GitHub forks (horovod/horovod) · observed Sep 26, 2026
- Last push (horovod/horovod) · observed Sep 17, 2026
- License file (Other) · observed Sep 26, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (hyperopt/hyperopt) · observed Oct 2, 2026
- GitHub forks (hyperopt/hyperopt) · observed Oct 2, 2026
- Last push (hyperopt/hyperopt) · observed Sep 28, 2026
- License file (Other) · observed Oct 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: horovod 15k · hyperopt 7.6k (synced Sep 26, 2026).
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 Sep 28, 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 (14,682 vs 7,595). 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 and hyperopt alternatives (horovod markdown twin, hyperopt markdown twin), 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 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; hyperopt trust report.