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PyTorch
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Deep learning framework.
- Categories
- Matched as
- pytorch
- torch
Momentum
Each signal is ranked as a percentile among the technologies that have it. A signal without enough history is not counted, and never shown as zero. How momentum works
Momentum
Collecting0 of 5 signalsNo score yet. Momentum is published only when at least 2 of the 5 signals have history. PyTorch has 0, so there is no single number: each signal is shown on its own below. It is not a zero, and nothing is estimated in its place.
Snapshot OCT 24, 2026 · Scoring tech@1
- Official repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.103,616 stars now
- Member repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.0 new members in 30 days
- Package adoption
- Not published by its registries
- Its packages’ registries publish no adoption figure, so there is nothing to grow.
- Open models
- No official models
- No official model on Hugging Face, so this signal does not apply.
- News
- Collecting
- News counts once there are 3 events across the last 60 days; it has 2.2 in the last 30 days · 0 in the 30 before
Adoption by registry
The growth of PyTorch’s own packages, one row per registry in that registry’s own unit. Registries count different things over different windows, so they are never added together.
No registry has two observations of PyTorch’s own packages about 30 days apart yet, so there is no growth to show. Nothing is estimated in its place.
News activity
- Events · 7 days
- 2published events that mention it
- Events · 30 days
- 2published events that mention it
- Previous 30 days
- 0events in the 30 days before
Momentum history
Momentum history starts OCT 8, 2026. A chart appears once two daily snapshots carry a score; none does so far.
Repositories
Official 1
- pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
103.6K stars
Members 794repositories tagged with one of its aliases, most-starred first
- elliottwu/unsup3d
(CVPR'20 Oral) Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
1.2K stars
- lartpang/PyTorchTricks
Some tricks of pytorch... :star:
1.2K stars
- lightaime/deep_gcns_torch
Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
1.2K stars
- SHI-Labs/Neighborhood-Attention-Transformer
Neighborhood Attention Transformer, arxiv 2022 / CVPR 2023. Dilated Neighborhood Attention Transformer, arxiv 2022
1.2K stars
- HyperGAN/HyperGAN
Composable GAN framework with api and user interface
1.2K stars
- jankrepl/deepdow
Portfolio optimization with deep learning.
1.2K stars
- MediaBrain-SJTU/MING
明医 (MING)：中文医疗问诊大模型
1.2K stars
- PyRetri/PyRetri
Open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥
1.2K stars
- fbcotter/pytorch_wavelets
Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet
1.2K stars
- tjiiv-cprg/EPro-PnP
[CVPR 2022 Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
1.2K stars
- hendrycks/robustness
Corruption and Perturbation Robustness (ICLR 2019)
1.2K stars
- ermongroup/SDEdit
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
1.2K stars
Packages
“Is” packages are PyTorch itself; ecosystem packages depend on them. The two are counted separately and never added together. Adoption is each registry’s own figure, with its percentile within that registry.
Is 1the technology itself, most-adopted first
- torch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Not published by PyPI
Ecosystem 582packages that depend on it, most-adopted first
- pytorch-ignite
A lightweight library to help with training neural networks in PyTorch.
Not published by PyPI - pytorch-kinematics
Robot kinematics implemented in pytorch
Not published by PyPI - pytorch-lightning
PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.
Not published by PyPI - pytorch-memlab
A lab to do simple and accurate memory experiments on pytorch
Not published by PyPI - pytorch-metric-learning
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Not published by PyPI - pytorch-msssim
Fast and differentiable MS-SSIM and SSIM for pytorch.
Not published by PyPI - pytorch-optimizer
optimizer & lr scheduler & objective function collections in PyTorch
Not published by PyPI - pytorch-ranger
Ranger - a synergistic optimizer using RAdam (Rectified Adam) and LookAhead in one codebase
Not published by PyPI - pytorch-revgrad
A pytorch module (and function) to reverse gradients.
Not published by PyPI - pytorch-seed
RNG seeding and context management for pytorch
Not published by PyPI - pytorch-tabular
A standard framework for using Deep Learning for tabular data
Not published by PyPI - pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
Not published by PyPI
Latest news the 3 latest events that mention it
- 32h agoOCT 24, 2026
- 3d agoOCT 22, 2026
- 4mo agoJUL 10, 2026