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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 signals

No 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

    Official repositoryPython

    103.6K

Members 794repositories tagged with one of its aliases, most-starred first

  • WongKinYiu/ScaledYOLOv4

    Scaled-YOLOv4: Scaling Cross Stage Partial Network

    Tagged pytorchPython

    2K

  • IgorSusmelj/pytorch-styleguide

    An unofficial styleguide and best practices summary for PyTorch

    Tagged pytorchPython

    2K

  • open-mmlab/mmgeneration

    MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

    Tagged pytorchPython

    2K

  • WongKinYiu/yolor

    implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)

    Tagged pytorchPython

    2K

  • clementchadebec/benchmark_VAE

    Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

    Tagged pytorchPython

    2K

  • haitongli/knowledge-distillation-pytorch

    A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

    Tagged pytorchPython

    2K

  • apple-aiml-research/ml-cvnets

    CVNets: A library for training computer vision networks

    Tagged pytorchPython

    2K

  • albertpumarola/GANimation

    GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]

    Tagged pytorchPython

    2K

  • hkchengrex/XMem

    [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

    Tagged pytorchPython

    2K

  • davda54/sam

    SAM: Sharpness-Aware Minimization (PyTorch)

    Tagged pytorchPython

    2K

  • guanshuicheng/invoice

    Collaboration with wangxupeng(https://github.com/wangxupeng)

    Tagged torchC

    2K

  • adobe-research/custom-diffusion

    Custom Diffusion: Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023)

    Tagged pytorchPython

    2K

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

    Listed in the catalogPyPI

    Not published by PyPI

Ecosystem 582packages that depend on it, most-adopted first

  • accelerate

    Accelerate

    Depends on torchPyPI

    Not published by PyPI
  • acvl-utils

    Super cool utilities that we just love to use

    Depends on torchPyPI

    Not published by PyPI
  • adam-atan2-pytorch

    Adam-atan2 for Pytorch

    Depends on torchPyPI

    Not published by PyPI
  • adaptive-classifier

    A flexible, adaptive classification system for dynamic text classification

    Depends on torchPyPI

    Not published by PyPI
  • ai-agents

    An Open-source Framework for Autonomous Language Agents

    Depends on torchPyPI

    Not published by PyPI
  • ai-dynamo-vllm

    A high-throughput and memory-efficient inference and serving engine for LLMs

    Depends on torchPyPI

    Not published by PyPI
  • ai-gradio

    A Python package for creating Gradio applications with AI models

    Depends on torchPyPI

    Not published by PyPI
  • ai2-olmo-core

    Core training module for the Open Language Model (OLMo)

    Depends on torchPyPI

    Not published by PyPI
  • aiconfig-extension-hugging-face

    An extension for using Hugging Face tasks to parse models for AIConfig.

    Depends on torchPyPI

    Not published by PyPI
  • aiconfig-extension-llama-guard

    An extension for using LLama Guard with aiconfig

    Depends on torchPyPI

    Not published by PyPI
  • aideml

    Autonomous AI for Data Science and Machine Learning

    Depends on torchPyPI

    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.

    Depends on torchPyPI

    Not published by PyPI

Latest news the 3 latest events that mention it