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Deep learning framework.
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- 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
- open-mmlab/mmrazor
OpenMMLab Model Compression Toolbox and Benchmark.
1.7K stars
- takuseno/d3rlpy
An offline deep reinforcement learning library
1.7K stars
- CuriousAI/mean-teacher
A state-of-the-art semi-supervised method for image recognition
1.7K stars
- YuliangXiu/ICON
[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals
1.7K stars
- argusswift/YOLOv4-pytorch
This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO
1.7K stars
- mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
1.7K stars
- Data-Centric-AI-Community/fg-data-synthetic
Synthetic data generators for tabular and time-series data
1.7K stars
- rwightman/efficientdet-pytorch
A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
1.7K stars
- opendilab/LightZero
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
1.6K stars
- kan-bayashi/ParallelWaveGAN
Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch
1.6K stars
- InterDigitalInc/CompressAI
A PyTorch library and evaluation platform for end-to-end compression research
1.6K stars
- ymcui/Chinese-XLNet
Pre-Trained Chinese XLNet（中文XLNet预训练模型）
1.6K 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
- lerobot
🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch
Not published by PyPI - lerobot-robodriver
🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch
Not published by PyPI - lhotse
Data preparation for speech processing models training.
Not published by PyPI - liger-kernel
Efficient Triton kernels for LLM Training
Not published by PyPI - liger-kernel-nightly
Efficient Triton kernels for LLM Training
Not published by PyPI - lightautoml
Fast and customizable framework for automatic ML model creation (AutoML)
Not published by PyPI - lighteval
A lightweight and configurable evaluation package
Not published by PyPI - lightly
A deep learning package for self-supervised learning
Not published by PyPI - lightning
The Deep Learning framework to train, deploy, and ship AI products Lightning fast.
Not published by PyPI - lightning-thunder
Lightning Thunder is a source-to-source compiler for PyTorch, enabling PyTorch programs to run on different hardware accelerators and graph compilers.
Not published by PyPI - lightweight-gan
Lightweight GAN
Not published by PyPI - linear-operator
A linear operator implementation, primarily designed for finite-dimensional positive definite operators (i.e. kernel matrices).
Not published by PyPI
Latest news the 3 latest events that mention it
- 45h agoOCT 24, 2026
- 3d agoOCT 22, 2026
- 4mo agoJUL 10, 2026