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PyTorch
Framework · Meta · Catalog tech@1
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
- kuangliu/pytorch-cifar
95.47% on CIFAR10 with PyTorch
6.4K stars
- mindee/doctr
docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning. Ongoing development and maintenance by t2k.
6.4K stars
- yl4579/StyleTTS2
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
6.4K stars
- aymericdamien/TopDeepLearning
A list of popular github projects related to deep learning
6.4K stars
- KevinMusgrave/pytorch-metric-learning
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
6.3K stars
- ufoym/deepo
Setup and customize deep learning environment in seconds.
6.3K stars
- aimhubio/aim
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
6.3K stars
- RangiLyu/nanodet
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
6.3K stars
- kserve/kserve
Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes
6.1K stars
- vllm-project/semantic-router
A programmable Mixture-of-Models router for heterogeneous LLM inference
6K stars
- microsoft/MMdnn
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
5.8K stars
- NVIDIA/DALI
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
5.8K 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
- accelerate
Accelerate
Not published by PyPI - acvl-utils
Super cool utilities that we just love to use
Not published by PyPI - adam-atan2-pytorch
Adam-atan2 for Pytorch
Not published by PyPI - adaptive-classifier
A flexible, adaptive classification system for dynamic text classification
Not published by PyPI - ai-agents
An Open-source Framework for Autonomous Language Agents
Not published by PyPI - ai-dynamo-vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - ai-gradio
A Python package for creating Gradio applications with AI models
Not published by PyPI - ai2-olmo-core
Core training module for the Open Language Model (OLMo)
Not published by PyPI - aiconfig-extension-hugging-face
An extension for using Hugging Face tasks to parse models for AIConfig.
Not published by PyPI - aiconfig-extension-llama-guard
An extension for using LLama Guard with aiconfig
Not published by PyPI - aideml
Autonomous AI for Data Science and Machine Learning
Not published by PyPI - airllm
AirLLM runs 70B large language models on a single 4GB GPU without quantization, distillation or pruning. Kimi K3 2.8T on under 4GB, Qwen3.8-Flash-Next 125B on 6GB, DeepSeek-V3 671B on ~12GB. Train Qwen3.8-Flash-Next under 6GB.
Not published by PyPI
Latest news the 3 latest events that mention it
- 16h agoOCT 24, 2026
- 2d agoOCT 22, 2026
- 4mo agoJUL 10, 2026