--- title: "accelerate vs hyperopt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-accelerate-vs-hyperopt-hyperopt" tools: ["huggingface-accelerate", "hyperopt-hyperopt"] --- # accelerate vs hyperopt *GraphCanon updated Aug 4, 2026* ## Verdict Pick accelerate if tool: accelerate; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. [accelerate](https://huggingface.co/docs/accelerate) reports 9.8k GitHub stars, 1.4k forks, and 105 open issues, last pushed Jul 30, 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 [accelerate's repository](https://github.com/huggingface/accelerate) and [hyperopt's repository](https://github.com/hyperopt/hyperopt). | | [accelerate](/tools/huggingface-accelerate.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Tagline | A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support. | Distributed Asynchronous Hyperparameter Optimization in Python | | Stars | 9,803 | 7,598 | | Forks | 1,425 | 1,075 | | Open issues | 105 | 9 | | Language | Python | Python | | Adopt for | Tool: accelerate | Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [accelerate](/tools/huggingface-accelerate.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Days since push | 3d | 0d | | Open issues (now) | 105 | 9 | | Full report | [trust report](/tools/huggingface-accelerate/trust.md) | [trust report](/tools/hyperopt-hyperopt/trust.md) | ## Shared compatibility - **Python**: [accelerate](/tools/huggingface-accelerate.md) - Python runtime; [hyperopt](/tools/hyperopt-hyperopt.md) - Python runtime ## Decision facts: accelerate - **Adopt for:** Tool: accelerate ## Decision facts: hyperopt - **Adopt for:** Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. ## Choose when ### 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 ### 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 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 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 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](/tools/huggingface-accelerate/alternatives) and [hyperopt alternatives](/tools/hyperopt-hyperopt/alternatives) ([accelerate markdown twin](/tools/huggingface-accelerate/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/huggingface-accelerate-vs-hyperopt-hyperopt.md) 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](/tools/huggingface-accelerate/trust); [hyperopt trust report](/tools/hyperopt-hyperopt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-accelerate`](/api/graphcanon/graph?tool=huggingface-accelerate) - 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/_