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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
- iver56/torch-audiomentations
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
1.2K stars
- GaParmar/clean-fid
PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]
1.2K stars
- cure-lab/MagicDrive
[ICLR24] Official implementation of the paper “MagicDrive: Street View Generation with Diverse 3D Geometry Control”
1.2K stars
- yandex-research/rtdl
Research on Tabular Deep Learning: Papers & Packages
1.2K stars
- alexandre01/deepsvg
[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.
1.2K stars
- oneapi-src/oneAPI-samples
Samples for Intel® oneAPI Toolkits
1.2K stars
- sirius-ai/LPRNet_Pytorch
Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.
1.2K stars
- PKU-Alignment/omnisafe
JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
1.2K stars
- chengtan9907/OpenSTL
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
1.2K stars
- lhotse-speech/lhotse
Tools for handling multimodal data in machine learning projects.
1.2K stars
- pkhungurn/talking-head-anime-2-demo
Demo programs for the Talking Head Anime from a Single Image 2: More Expressive project.
1.2K stars
- Sharpiless/Yolov5-Deepsort
最新版本yolov5+deepsort目标检测和追踪，能够显示目标类别，支持5.0版本可训练自己数据集
1.2K 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
- 14h agoOCT 24, 2026
- 2d agoOCT 22, 2026
- 4mo agoJUL 10, 2026