accelerate
Accelerate
Decision gist · record as of 2026-08-14
Yes. Accelerate is production-stable, actively maintained, permissively licensed, and solves a real pain point—distributed training boilerplate—for PyTorch users. The low install friction and minimal API surface make it a practical choice for anyone moving from single-GPU to multi-device training. No known vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch and Python >=3.10.0; distributed setups may need MPI or torchrun infrastructure.
- Low friction install with a pure Python wheel.
- Actively maintained with recent releases; 9818 repository stars and production-stable status suggest solid community backing.
License · maintenance · safety
Apache (permissive) — Apache License (permissive) means you can use this freely in commercial and private projects with minimal restrictions—just retain license notices.
last release 2026-06-11 (64 days) · last repo commit 2026-08-10 · 9,818 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 28,010,594 downloads/mo, #843 on PyPI
Alternatives
Verify before relying
pip install accelerate
from accelerate import Accelerator
import torch
accelerator = Accelerator()
model = torch.nn.Linear(10, 5)
optimizer = torch.optim.Adam(model.parameters())
model, optimizer = accelerator.prepare(model, optimizer)
# In training loop:
accelerator.backward(loss)- Whether the package handles automatic gradient accumulation or if users must implement it manually.
- Specific performance overhead of the abstraction layer compared to raw torch.distributed.
- Compatibility with custom training loop patterns beyond the documented examples.
What it is and what it does
Accelerate is a PyTorch training wrapper that removes the boilerplate needed for distributed and mixed-precision training. It sits between your training loop and PyTorch's device/distributed APIs, letting you write once and run on any hardware configuration—single CPU, single GPU, multi-GPU, TPU, or with fp8/fp16/bf16 precision—without rewriting your training code.
The core workflow is simple: instantiate an Accelerator object, call prepare() on your model/optimizer/dataloader, and replace loss.backward() with accelerator.backward(). The package also provides an optional CLI tool (accelerate config and accelerate launch) for configuring and launching training without manually writing torch.distributed.run commands. It depends on torch, numpy, packaging, psutil, pyyaml, huggingface_hub, and safetensors, making it a relatively lightweight addition to an existing PyTorch project.
Use it for
- Run the same training script locally on CPU for debugging, then on multi-GPU without code changes.
- Launch distributed training across multiple machines using the CLI without learning torch.distributed APIs.
- Enable mixed-precision training (fp16, bf16) by passing a flag to Accelerator instead of manually casting.
- Train on TPU in Colab or Kaggle notebooks using the notebook_launcher function.
- Integrate DeepSpeed for large-model training by passing a DeepSpeedPlugin to Accelerator.
- Simplify device placement by letting Accelerator handle tensor movement automatically.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Accelerate is production-stable, actively maintained, permissively licensed, and solves a real pain point—distributed training boilerplate—for PyTorch users. The low install friction and minimal API surface make it a practical choice for anyone moving from single-GPU to multi-device training. No known vulnerabilities.
Install
accelerate on PyPI
Before you install
Low friction install with a pure Python wheel. Actively maintained with recent releases; 9818 repository stars and production-stable status suggest solid community backing.
Requires torch and Python >=3.10.0; distributed setups may need MPI or torchrun infrastructure.
License in practice
Apache License (permissive) means you can use this freely in commercial and private projects with minimal restrictions—just retain license notices.
Quickstart
pip install accelerate
from accelerate import Accelerator
import torch
accelerator = Accelerator()
model = torch.nn.Linear(10, 5)
optimizer = torch.optim.Adam(model.parameters())
model, optimizer = accelerator.prepare(model, optimizer)
# In training loop:
accelerator.backward(loss)Verify before relying
- Whether the package handles automatic gradient accumulation or if users must implement it manually.
- Specific performance overhead of the abstraction layer compared to raw torch.distributed.
- Compatibility with custom training loop patterns beyond the documented examples.
Package facts
| License | Apache permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesnumpypackagingpsutilpyyamltorchhuggingface_hubsafetensors |
| Maintenance | Actively maintained 64 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 28,010,594 / month, #843 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 :: 3.10Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: accelerate-1.14.0-py3-none-any.whl
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