Home/Compare/accelerate vs hyperopt

Comparison

accelerate vs hyperopt

Verdict

Pick accelerate if tool: accelerate; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.

Markdown twin · accelerate alternatives · hyperopt alternatives

GraphCanon updated 3w

accelerate logo

accelerate

huggingface/accelerate

9.8kpushed Jul 30, 2026
vs
hyperopt logo

hyperopt

hyperopt/hyperopt

7.6kpushed Aug 3, 2026

Trust & integrity

Signalacceleratehyperopt
Maintenance
Very active (3d since push)
As of 3w · github_public_v1
Very active (0d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Organization account
As of 3w · github_public_v1
Not a fork · Organization account
As of 3w · 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

accelerate
A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.
hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python

Stars

accelerate
9.8k
hyperopt
7.6k

Forks

accelerate
1.4k
hyperopt
1.1k

Open issues

accelerate
105
hyperopt
9

Language

accelerate
Python
hyperopt
Python

Adopt for

accelerate
Tool: accelerate
hyperopt
Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.

Persona

accelerate
-
hyperopt
-

Runtime

accelerate
-
hyperopt
-

License

accelerate
Apache-2.0
hyperopt
Other

Last pushed

accelerate
Jul 30, 2026
hyperopt
Aug 3, 2026

Categories

accelerate
Inference & Serving, Model Training
hyperopt
Model Training

Trust and health

Days since push

accelerate
3d
hyperopt
0d

Open issues (now)

accelerate
105
hyperopt
9

Full report

accelerate
Trust report
hyperopt
Trust report

Shared compatibility

  • Python · accelerate: Python runtime · hyperopt: Python runtime

Choose accelerate if…

  • License: accelerate is Apache-2.0, hyperopt is Other.
  • Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch.
  • Also covers Inference & Serving.
  • Easy mixed-precision support for PyTorch models

When NOT to use accelerate

  • Non-PyTorch projects do not benefit from this tool
  • Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow
  • Limited to Python environments compatible with PyTorch 1.10.0+

Choose hyperopt if…

  • License: hyperopt is Other, accelerate is Apache-2.0.
  • 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.

Explore

Sources

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

GitHub stars on cards: accelerate 9.8k · hyperopt 7.6k (synced Aug 3, 2026).

Common questions

What is the difference between accelerate and hyperopt?
accelerate: A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.. hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. See the comparison table for live GitHub stats and shared categories.
When should I choose accelerate over hyperopt?
Choose accelerate over hyperopt when License: accelerate is Apache-2.0, hyperopt is Other; Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch; Also covers Inference & Serving; Easy mixed-precision support for PyTorch models.
When should I choose hyperopt over accelerate?
Choose hyperopt over accelerate when License: hyperopt is Other, accelerate is Apache-2.0; 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 avoid accelerate?
Non-PyTorch projects do not benefit from this tool Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow Limited to Python environments compatible with PyTorch 1.10.0+
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 accelerate or hyperopt more popular on GitHub?
accelerate has more GitHub stars (9,803 vs 7,598). Stars measure visibility, not whether either tool fits your constraints.
Are accelerate and hyperopt open source?
Yes - both are open-source projects on GitHub (accelerate: Apache-2.0, hyperopt: Other).
Where can I find alternatives to accelerate or hyperopt?
GraphCanon lists graph-backed alternatives at accelerate alternatives and hyperopt alternatives (accelerate 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, accelerate or hyperopt?
accelerate: Very active. 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 accelerate and hyperopt?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: accelerate trust report; hyperopt trust report.

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