pytorch_optimizer
optimizer & lr scheduler & objective function collections in PyTorch
Decision gist · record as of 2026-08-14
Yes. The package has low install friction, active maintenance, a permissive license, no known vulnerabilities, and solves a real friction point in PyTorch training—accessing a broad range of optimizers and schedulers through a consistent API. Install it if you want to experiment with modern training methods or need access to research optimizer variants without implementing them yourself.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8 and PyTorch >=1.10.
- Optional integrations (bitsandbytes, q-galore-torch, torchao) must be installed separately if needed.
- Low install friction with only numpy and torch as runtime dependencies.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions.
last release 2026-05-23 (83 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 163,663 downloads/mo, #10,566 on PyPI
Alternatives
Verify before relying
pip install pytorch-optimizer
from pytorch_optimizer import AdamP
model = YourModel()
optimizer = AdamP(model.parameters(), lr=1e-3)- Whether the 100+ optimizers, 10+ schedulers, and 10+ loss functions cover the specific training methods your project requires
- Performance characteristics and convergence behavior compared to using PyTorch's built-in optimizers directly
- Compatibility with your specific PyTorch version and hardware setup (CPU vs GPU)
What it is and what it does
pytorch-optimizer bundles a large collection of optimizer implementations, learning rate schedulers, and loss functions for PyTorch, all accessible through a unified API. Instead of implementing or hunting down individual optimizer variants, you can import them by name or class and use them like standard PyTorch optimizers. The package includes both classic methods and recent research variants, with practical features like foreach support, Lookahead integration, and Gradient Centralization built in.
It's designed for practitioners who want to experiment with modern training techniques without rewriting boilerplate code. The package supports optional ecosystem integrations and has been tested and actively maintained. You can discover available components programmatically, load optimizers by name, or use a factory function to configure them with additional features in one call.
Use it for
- Quickly experiment with different optimizer variants during model development without implementing each one from scratch
- Build training pipelines that support multiple optimizer choices through a consistent loader API
- Apply practical training enhancements like Lookahead or Gradient Centralization to any optimizer in the collection
- Access recent research optimizers (AdamP, DualAdam, etc.) alongside classic methods in a single package
- Integrate with optional ecosystem tools like bitsandbytes for quantized training when needed
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package has low install friction, active maintenance, a permissive license, no known vulnerabilities, and solves a real friction point in PyTorch training—accessing a broad range of optimizers and schedulers through a consistent API. Install it if you want to experiment with modern training methods or need access to research optimizer variants without implementing them yourself.
Install
pytorch-optimizer on PyPI
Before you install
Low install friction with only numpy and torch as runtime dependencies. Actively maintained with a recent release.
Requires Python >=3.8 and PyTorch >=1.10. Optional integrations (bitsandbytes, q-galore-torch, torchao) must be installed separately if needed.
License in practice
Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions.
Quickstart
pip install pytorch-optimizer
from pytorch_optimizer import AdamP
model = YourModel()
optimizer = AdamP(model.parameters(), lr=1e-3)Verify before relying
- Whether the 100+ optimizers, 10+ schedulers, and 10+ loss functions cover the specific training methods your project requires
- Performance characteristics and convergence behavior compared to using PyTorch's built-in optimizers directly
- Compatibility with your specific PyTorch version and hardware setup (CPU vs GPU)
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpytorch |
| Maintenance | Actively maintained 83 days since the last release |
| First released | |
| Downloads | 163,663 / month, #10,566 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: pytorch_optimizer-3.10.1-py3-none-any.whl
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See also pyswarms · pytorch_revgrad · torch-optimizer · schedulefree · pytorch-ranger · torchtnt · lion-pytorch · prodigyopt · fvcore · flashoptim