Technology · Framework

PyTorch

Framework · Meta · Catalog tech@1

Deep learning framework.

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 stars

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

  • jrieke/traingenerator

    🧙 A web app to generate template code for machine learning

    Tagged pytorchPython

    1.4K stars

  • AutoArk/TinyEngram

    Research of DeepSeek Engram Architecture based on Qwen-3 and Stable Diffusion series.

    Tagged pytorchPython

    1.4K stars

  • pnnl/neuromancer

    Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

    Tagged pytorchPython

    1.4K stars

  • keyu-tian/SparK

    [ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"

    Tagged pytorchPython

    1.4K stars

  • FireRedTeam/FireRed-Image-Edit

    FireRed-Image-Edit is a powerful image editing foundation model achieving open-source state-of-the-art performance with precise instruction following, high-fidelity generation, superior identity consistency, and seamless multi-element fusion.

    Tagged pytorchPython

    1.4K stars

  • Cysu/open-reid

    Open source person re-identification library in python

    Tagged pytorchPython

    1.4K stars

  • TorchSSL/TorchSSL

    A PyTorch-based library for semi-supervised learning (NeurIPS'21)

    Tagged pytorchPython

    1.4K stars

  • akamaster/pytorch_resnet_cifar10

    Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.

    Tagged pytorchPython

    1.4K stars

  • hiyouga/FastEdit

    🩹Editing large language models within 10 seconds⚡

    Tagged pytorchPython

    1.4K stars

  • zalandoresearch/pytorch-ts

    PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

    Tagged pytorchPython

    1.4K stars

  • bytedance/flux

    A fast communication-overlapping library for tensor/expert parallelism on GPUs.

    Tagged pytorchC++

    1.4K stars

  • sail-sg/poolformer

    PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)

    Tagged pytorchPython

    1.4K 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

    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