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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
- facebookresearch/large_concept_model
Large Concept Models: Language modeling in a sentence representation space
2.4K stars
- r9y9/wavenet_vocoder
WaveNet vocoder
2.4K stars
- jik876/hifi-gan
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
2.4K stars
- isl-org/Open3D-ML
An extension of Open3D to address 3D Machine Learning tasks
2.4K stars
- moskomule/senet.pytorch
PyTorch implementation of SENet
2.3K stars
- dvmazur/mixtral-offloading
Run Mixtral-8x7B models in Colab or consumer desktops
2.3K stars
- labmlai/labml
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
2.3K stars
- ufoym/imbalanced-dataset-sampler
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
2.3K stars
- OAID/TengineKit
TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes & Hand Detection & Hand Landmarks & Body Detection & Body Landmarks & Iris Landmarks & Yolov5 SDK On Mobile.
2.3K stars
- hzwer/ICCV2019-LearningToPaint
ICCV2019 - Learning to Paint With Model-based Deep Reinforcement Learning
2.3K stars
- IDEA-Research/detrex
detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.
2.3K stars
- beir-cellar/beir
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
2.3K 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
- open-dataflow
Modern Data Centric AI system for Large Language Models
Not published by PyPI - openai-whisper
Robust Speech Recognition via Large-Scale Weak Supervision
Not published by PyPI - opencompass
A comprehensive toolkit for large model evaluation
Not published by PyPI - opennmt-py
A python implementation of OpenNMT
Not published by PyPI - openunmix
PyTorch-based music source separation toolkit
Not published by PyPI - opt-einsum-fx
Einsum optimization using opt_einsum and PyTorch FX
Not published by PyPI - optimum
Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
Not published by PyPI - optimum-intel
Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
Not published by PyPI - optimum-nvidia
Optimum Nvidia is the interface between the Hugging Face Transformers and NVIDIA GPUs. "
Not published by PyPI - optimum-quanto
A pytorch quantization backend for optimum.
Not published by PyPI - orbit-ml
Orbit is a package for Bayesian time series modeling and inference.
Not published by PyPI - orion-ml
Orion is a machine learning library built for unsupervised time series anomaly detection.
Not published by PyPI
Latest news the 3 latest events that mention it
- 2d agoOCT 24, 2026
- 4d agoOCT 22, 2026
- 4mo agoJUL 10, 2026