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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

  • milesial/Pytorch-UNet

    PyTorch implementation of the U-Net for image semantic segmentation with high quality images

    Tagged pytorchPython

    11.7K

  • kornia/kornia

    🐍 Geometric Computer Vision Library for Spatial AI

    Tagged pytorchPython

    11.4K

  • wandb/wandb

    The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

    Tagged pytorchPython

    11.3K

  • QuentinFuxa/WhisperLiveKit

    Real-time, local speech-to-text with streaming ASR, speaker diarization, translation, and OpenAI/Deepgram-compatible APIs.

    Tagged pytorchPython

    11.1K

  • thu-ml/tianshou

    An elegant PyTorch deep reinforcement learning library.

    Tagged pytorchPython

    11K

  • autogluon/autogluon

    Fast and Accurate ML in 3 Lines of Code

    Tagged pytorchPython

    10.8K

  • Megvii-BaseDetection/YOLOX

    YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

    Tagged pytorchPython

    10.6K

  • ultralytics/yolov3

    PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

    Tagged pytorchPython

    10.6K

  • bigscience-workshop/petals

    🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading

    Tagged pytorchPython

    10.6K

  • vwxyzjn/cleanrl

    High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

    Tagged pytorchPython

    10.5K

  • snakers4/silero-vad

    Silero VAD: pre-trained enterprise-grade Voice Activity Detector

    Tagged pytorchPython

    10.3K

  • satellite-image-deep-learning/techniques

    Techniques for deep learning with satellite & aerial imagery

    Tagged pytorch

    10.3K

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

  • auto-round

    Repository of AutoRound: Advanced Weight-Only Quantization Algorithm for LLMs

    Depends on torchPyPI

    Not published by PyPI
  • auto-round-kernel

    Auto Round Kernel binary package

    Depends on torchPyPI

    Not published by PyPI
  • auto-round-lib

    Auto Round Kernel binary package

    Depends on torchPyPI

    Not published by PyPI
  • auto-round-nightly

    Repository of AutoRound: Advanced Weight-Only Quantization Algorithm for LLMs

    Depends on torchPyPI

    Not published by PyPI
  • autogluon-multimodal

    Fast and Accurate ML in 3 Lines of Code

    Depends on torchPyPI

    Not published by PyPI
  • autogluon-timeseries

    Fast and Accurate ML in 3 Lines of Code

    Depends on torchPyPI

    Not published by PyPI
  • avalanche-lib

    Avalanche: a Comprehensive Framework for Continual Learning Research

    Depends on torchPyPI

    Not published by PyPI
  • axolotl

    LLM Trainer

    Depends on torchPyPI

    Not published by PyPI
  • b10-xgrammar

    Efficient, Flexible and Portable Structured Generation

    Depends on torchPyPI

    Not published by PyPI
  • backgroundremover

    Background remover from image and video using AI

    Depends on torchPyPI

    Not published by PyPI
  • batchgeneratorsv2

    Batchgenerators but better

    Depends on torchPyPI

    Not published by PyPI
  • beat-this

    Beat This! beat tracker

    Depends on torchPyPI

    Not published by PyPI

Latest news the 3 latest events that mention it