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PyTorch
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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
- qfettes/DeepRL-Tutorials
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
1.1K stars
- hkproj/pytorch-stable-diffusion
Stable Diffusion implemented from scratch in PyTorch
1.1K stars
- yjxiong/tsn-pytorch
Temporal Segment Networks (TSN) in PyTorch
1.1K stars
- RL-VIG/LibFewShot
[TPAMI 2023] LibFewShot: A Comprehensive Library for Few-shot Learning.
1.1K stars
- cardwing/Codes-for-Lane-Detection
Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019)
1.1K stars
- ayoolaolafenwa/PixelLib
Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
1.1K stars
- ZhengYinan-AIR/Diffusion-Planner
[ICLR 2025 Oral] The official implementation of "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance"
1.1K stars
- NVIDIA-AI-IOT/trt_pose
Real-time pose estimation accelerated with NVIDIA TensorRT
1.1K stars
- drprojects/superpoint_transformer
Official PyTorch implementation of Superpoint Transformer [ICCV'23], SuperCluster [3DV'24 Oral], and EZ-SP [ICRA'26]
1.1K stars
- open-mmlab/mmflow
OpenMMLab optical flow toolbox and benchmark
1.1K stars
- ternaus/TernausNet
UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
1.1K stars
- chenyuntc/PyTorchText
1st Place Solution for Zhihu Machine Learning Challenge . Implementation of various text-classification models.(知乎看山杯第一名解决方案)
1.1K 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
- composer
Composer is a PyTorch library that enables you to train neural networks faster, at lower cost, and to higher accuracy.
Not published by PyPI - compressai
A PyTorch library and evaluation platform for end-to-end compression research
Not published by PyPI - compressed-tensors
Library for utilization of compressed safetensors of neural network models
Not published by PyPI - concrete-ml
Concrete ML is an open-source set of tools which aims to simplify the use of fully homomorphic encryption (FHE) for data scientists.
Not published by PyPI - concrete-python
A state-of-the-art homomorphic encryption framework
Not published by PyPI - conformer
The convolutional module from the Conformer paper
Not published by PyPI - contextualized-topic-models
Contextualized Topic Models
Not published by PyPI - controlnet-aux
Auxillary models for controlnet
Not published by PyPI - ctgan
Create tabular synthetic data using a conditional GAN
Not published by PyPI - cuequivariance-ops-torch-cu12
cuequivariance-ops-torch - GPU Accelerated Torch Extensions for Equivariant Primitives
Not published by PyPI - cuequivariance-ops-torch-cu13
cuequivariance-ops-torch - GPU Accelerated Torch Extensions for Equivariant Primitives
Not published by PyPI - curated-transformers
A PyTorch library of transformer models and components
Not published by PyPI
Latest news the 3 latest events that mention it
- 13h agoOCT 24, 2026
- 48h agoOCT 22, 2026
- 4mo agoJUL 10, 2026