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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 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

  • THUDM/SwissArmyTransformer

    SwissArmyTransformer is a flexible and powerful library to develop your own Transformer variants.

    Tagged pytorchPython

    1.1K

  • datadreamer-dev/DataDreamer

    DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models.   🤖💤

    Tagged pytorchPython

    1.1K

  • microsoft/Windows-Machine-Learning

    Samples and Tools for Windows ML.

    Tagged pytorchC++

    1.1K

  • AdaptiveMotorControlLab/CEBRA

    Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA

    Tagged pytorchPython

    1.1K

  • bearpaw/pytorch-pose

    A PyTorch toolkit for 2D Human Pose Estimation.

    Tagged pytorchPython

    1.1K

  • ndif-team/nnsight

    The nnsight package enables interpreting and manipulating the internals of deep learned models.

    Tagged pytorchPython

    1.1K

  • ryfeus/lambda-packs

    Precompiled packages for AWS Lambda

    Tagged pytorchPython

    1.1K

  • vietnh1009/Super-mario-bros-A3C-pytorch

    Asynchronous Advantage Actor-Critic (A3C) algorithm for Super Mario Bros

    Tagged pytorchPython

    1.1K

  • gordicaleksa/pytorch-original-transformer

    My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.

    Tagged pytorchJupyter Notebook

    1.1K

  • snap-research/EfficientFormer

    EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]

    Tagged pytorchPython

    1.1K

  • Res2Net/Res2Net-PretrainedModels

    (ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"

    Tagged pytorchPython

    1.1K

  • mit-han-lab/gan-compression

    [CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs

    Tagged pytorchPython

    1.1K

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

  • deepspeed

    DeepSpeed library

    Depends on torchPyPI

    Not published by PyPI
  • deepspeed-mii

    deepspeed mii

    Depends on torchPyPI

    Not published by PyPI
  • demucs

    Music source separation in the waveform domain.

    Depends on torchPyPI

    Not published by PyPI
  • denoising-diffusion-pytorch

    Denoising Diffusion Probabilistic Models - Pytorch

    Depends on torchPyPI

    Not published by PyPI
  • depthflow

    🌊 Images to 3D parallax effect videos

    Depends on torchPyPI

    Not published by PyPI
  • descript-audio-codec

    A high-quality general neural audio codec.

    Depends on torchPyPI

    Not published by PyPI
  • descript-audio-codec-unofficial

    A high-quality general neural audio codec.

    Depends on torchPyPI

    Not published by PyPI
  • descript-audiotools

    Utilities for handling audio.

    Depends on torchPyPI

    Not published by PyPI
  • detoxify

    A python library for detecting toxic comments

    Depends on torchPyPI

    Not published by PyPI
  • di-engine

    Decision AI Engine

    Depends on torchPyPI

    Not published by PyPI
  • diart

    A python framework to build AI for real-time speech

    Depends on torchPyPI

    Not published by PyPI
  • diffq

    Differentiable quantization framework for PyTorch.

    Depends on torchPyPI

    Not published by PyPI

Latest news the 3 latest events that mention it