Home/Compare/hyperopt vs rembo

Comparison

hyperopt vs rembo

Verdict

Pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing; pick rembo if rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding.

Markdown twin · hyperopt alternatives · rembo alternatives

GraphCanon updated 2w

hyperopt logo

hyperopt

hyperopt/hyperopt

7.6kpushed Aug 3, 2026
vs
rembo logo

rembo

ziyuw/rembo

117pushed Aug 4, 2013

Trust & integrity

Signalhyperoptrembo
Maintenance
Very active (0d since push)
As of 2w · github_public_v1
Dormant (4747d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Personal account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
rembo
Bayesian optimization in high-dimensions via random embedding.

Stars

hyperopt
7.6k
rembo
117

Forks

hyperopt
1.1k
rembo
25

Open issues

hyperopt
9
rembo
3

Language

hyperopt
Python
rembo
Matlab

Adopt for

hyperopt
Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
rembo
Rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding.

Persona

hyperopt
-
rembo
-

Runtime

hyperopt
-
rembo
-

License

hyperopt
Other
rembo
-

Last pushed

hyperopt
Aug 3, 2026
rembo
Aug 4, 2013

Categories

hyperopt
Model Training
rembo
Model Training

Trust and health

Maintenance

hyperopt
Very active (96%)
rembo
Dormant (18%)

Days since push

hyperopt
0d
rembo
4747d

Open issues (now)

hyperopt
9
rembo
3

Owner type

hyperopt
Organization
rembo
User

Full report

hyperopt
Trust report

Choose hyperopt if…

  • hyperopt is primarily Python; rembo is Matlab.
  • Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization.
  • When you need to optimize machine learning model parameters on a distributed system asynchronously.

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.

Choose rembo if…

  • rembo is primarily Matlab; hyperopt is Python.
  • Tags unique to rembo: bayesian-optimization, high-dimensional space, random embedding.
  • When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions

When NOT to use rembo

  • For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding
  • In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: hyperopt 7.6k · rembo 117 (synced Aug 4, 2026).

Common questions

What is the difference between hyperopt and rembo?
hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. rembo: Bayesian optimization in high-dimensions via random embedding.. See the comparison table for live GitHub stats and shared categories.
When should I choose hyperopt over rembo?
Choose hyperopt over rembo when hyperopt is primarily Python; rembo is Matlab; Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization; When you need to optimize machine learning model parameters on a distributed system asynchronously.
When should I choose rembo over hyperopt?
Choose rembo over hyperopt when rembo is primarily Matlab; hyperopt is Python; Tags unique to rembo: bayesian-optimization, high-dimensional space, random embedding; When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions.
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.
When should I avoid rembo?
For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects
Is hyperopt or rembo more popular on GitHub?
hyperopt has more GitHub stars (7,598 vs 117). Stars measure visibility, not whether either tool fits your constraints.
Are hyperopt and rembo open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to hyperopt or rembo?
GraphCanon lists graph-backed alternatives at hyperopt alternatives and rembo alternatives (hyperopt markdown twin, rembo 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, hyperopt or rembo?
hyperopt: Very active. rembo: Dormant. 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 hyperopt and rembo?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hyperopt trust report; rembo trust report.

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