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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

  • qfettes/DeepRL-Tutorials

    Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

    Tagged pytorchJupyter Notebook

    1.1K

  • hkproj/pytorch-stable-diffusion

    Stable Diffusion implemented from scratch in PyTorch

    Tagged pytorchJupyter Notebook

    1.1K

  • yjxiong/tsn-pytorch

    Temporal Segment Networks (TSN) in PyTorch

    Tagged pytorchPython

    1.1K

  • RL-VIG/LibFewShot

    [TPAMI 2023] LibFewShot: A Comprehensive Library for Few-shot Learning.

    Tagged pytorchPython

    1.1K

  • cardwing/Codes-for-Lane-Detection

    Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019)

    Tagged pytorchLua

    1.1K

  • ayoolaolafenwa/PixelLib

    Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

    Tagged pytorchPython

    1.1K

  • ZhengYinan-AIR/Diffusion-Planner

    [ICLR 2025 Oral] The official implementation of "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance"

    Tagged pytorchPython

    1.1K

  • NVIDIA-AI-IOT/trt_pose

    Real-time pose estimation accelerated with NVIDIA TensorRT

    Tagged pytorchPython

    1.1K

  • drprojects/superpoint_transformer

    Official PyTorch implementation of Superpoint Transformer [ICCV'23], SuperCluster [3DV'24 Oral], and EZ-SP [ICRA'26]

    Tagged pytorchPython

    1.1K

  • open-mmlab/mmflow

    OpenMMLab optical flow toolbox and benchmark

    Tagged pytorchPython

    1.1K

  • ternaus/TernausNet

    UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset

    Tagged pytorchPython

    1.1K

  • chenyuntc/PyTorchText

    1st Place Solution for Zhihu Machine Learning Challenge . Implementation of various text-classification models.(知乎看山杯第一名解决方案)

    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

  • face-alignment

    Detector 2D or 3D face landmarks from Python

    Depends on torchPyPI

    Not published by PyPI
  • facenet-pytorch

    Pretrained Pytorch face detection and recognition models

    Depends on torchPyPI

    Not published by PyPI
  • facexlib

    Basic face library

    Depends on torchPyPI

    Not published by PyPI
  • fairseq

    Facebook AI Research Sequence-to-Sequence Toolkit

    Depends on torchPyPI

    Not published by PyPI
  • fairseq2-patch

    FAIR Sequence Modeling Toolkit

    Depends on torchPyPI

    Not published by PyPI
  • fairseq2n

    FAIR Sequence Modeling Toolkit (Native)

    Depends on torchPyPI

    Not published by PyPI
  • fairseq2n-patch

    FAIR Sequence Modeling Toolkit (Native)

    Depends on torchPyPI

    Not published by PyPI
  • fast-gauss

    A geometry-shader-based, global CUDA sorted high-performance 3D Gaussian Splatting rasterizer. Can achieve a 5-10x speedup in rendering compared to the vanialla diff-gaussian-rasterization.

    Depends on torchPyPI

    Not published by PyPI
  • fastai

    fastai simplifies training fast and accurate neural nets using modern best practices

    Depends on torchPyPI

    Not published by PyPI
  • faster-qwen3-tts

    Real-time Qwen3-TTS inference with CUDA graphs or GGML

    Depends on torchPyPI

    Not published by PyPI
  • fastvideo

    FastVideo

    Depends on torchPyPI

    Not published by PyPI
  • fastvideo-kernel

    Unified CUDA kernels for FastVideo

    Depends on torchPyPI

    Not published by PyPI

Latest news the 3 latest events that mention it