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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
- huggingface/transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
166.9K stars
- AUTOMATIC1111/stable-diffusion-webui
Stable Diffusion web UI
165.2K stars
- Comfy-Org/ComfyUI
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface. The fastest local inference engine in the world.
135.9K stars
- vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
93.1K stars
- labmlai/annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
67.5K stars
- keras-team/keras
Deep Learning for humans
64.3K stars
- ultralytics/ultralytics
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
62.2K stars
- CorentinJ/Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
60.2K stars
- ultralytics/yolov5
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
58.1K stars
- roboflow/supervision
We write your reusable computer vision tools. 💜
51.1K stars
- coqui-ai/TTS
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
46.1K stars
- ray-project/ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
44K 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
- garak
LLM vulnerability scanner
Not published by PyPI - gdinopy
open-set object detector
Not published by PyPI - geoai-py
A Python package for using Artificial Intelligence (AI) with geospatial data
Not published by PyPI - geomloss
Geometric loss functions between point clouds, images and volumes.
Not published by PyPI - geoopt
Unofficial implementation for “Riemannian Adaptive Optimization Methods” ICLR2019 and more
Not published by PyPI - gfpgan
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration
Not published by PyPI - giga-train
GigaTrain: An Efficient and Scalable Training Framework for AI Models
Not published by PyPI - gliner
Generalist model for NER (Extract any entity types from texts)
Not published by PyPI - gliner-py
Generalist model for NER (Extract any entity types from texts). Fork of the original project to relax the strict Transformers version requirement.
Not published by PyPI - gptqmodel
Production ready LLM model compression/quantization toolkit with hw accelerated inference support for both cpu/gpu via HF, vLLM, and SGLang.
Not published by PyPI - grad-cam
Many Class Activation Map methods implemented in Pytorch for classification, segmentation, object detection and more
Not published by PyPI - groundingdino-iscas
open-set object detector
Not published by PyPI
Latest news the 3 latest events that mention it
- 17h agoOCT 24, 2026
- 2d agoOCT 22, 2026
- 4mo agoJUL 10, 2026