NVIDIA/apex
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch observed · 2026-08-28
Health v2 · maintenance only
77/100
- Activity 99
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3054
- days_rel: n/a
- days_push: 9
- n_releases_24m: 0
Adoption not part of the score
8993 stars · 1525 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
NVIDIA-maintained PyTorch extension providing utilities for easy mixed precision and distributed training. It offers up-to-date CUDA and C++ extensions that eventually make their way into upstream PyTorch.
Use cases
- train deep learning models with mixed precision in pytorch
- speed up pytorch training on nvidia gpus
- run distributed training across multiple gpus
- use fused cuda kernels for faster optimizer steps
- reduce gpu memory usage during model training
When to choose
- you train large models on NVIDIA GPUs and need AMP/fused optimizer performance
- you need cutting-edge training utilities before they land in PyTorch
- you use NVIDIA NGC PyTorch containers and want matching extensions
When to avoid
- you only train on CPU or non-NVIDIA hardware
- you need a stable, simple setup without compiling CUDA extensions
- your use case is fully covered by native PyTorch AMP
Facets
library · maturity active
machine-learning llm-training gpu-computing deep-learning machine-learning gpu-computing python pytorch-extension mixed-precision distributed-training cuda-extensions nvidia linux gpu
1 source
- readme: https://github.com/NVIDIA/apex · fetched 2026-08-28 · 2e7aea0520a7
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| NVIDIA/apex | main | 77 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem