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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 signalsNo 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
103.6K stars
Members 794repositories tagged with one of its aliases, most-starred first
- ZhaoJ9014/face.evoLVe
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
3.6K stars
- pytorch/rl
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
3.6K stars
- zhaoyingjun/chatbot
Chatbot继续沿着LLM前进，近期更新小参数量SLM的和训练脚本，支持本地训练。新增ChatAgent,实现各种有实际场景价值的Agent实现。
3.6K stars
- NVIDIA/TransformerEngine
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
3.6K stars
- foolwood/SiamMask
[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation
3.6K stars
- jmschrei/pomegranate
Fast, flexible and easy to use probabilistic modelling in Python.
3.5K stars
- microsoft/hummingbird
Hummingbird compiles trained ML models into tensor computation for faster inference.
3.5K stars
- cszn/KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR
3.5K stars
- podgorskiy/ALAE
[CVPR2020] Adversarial Latent Autoencoders
3.5K stars
- zjhellofss/KuiperInfer
校招、秋招、春招、实习好项目！带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step
3.5K stars
- huggingface/optimum
🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools
3.5K stars
- kuprel/min-dalle
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
3.5K 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
Not published by PyPI
Ecosystem 582packages that depend on it, most-adopted first
- jkp-depth-anything-v2
Depth Anything V2: Robust Monocular Depth Estimation
Not published by PyPI - julius
Nice DSP sweets: resampling, FFT Convolutions. All with PyTorch, differentiable and with CUDA support.
Not published by PyPI - khoj
Your Second Brain
Not published by PyPI - kittenml
Text-to-speech with voice cloning and expression control, plus ultra-lightweight models that run on CPU
Not published by PyPI - kokoro
TTS
Not published by PyPI - kornia
Open Source Differentiable Computer Vision Library for PyTorch
Not published by PyPI - kronos-model-arch
Kronos: A Foundation Model for the Language of Financial Markets
Not published by PyPI - labml-nn
🧑🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, diffusion, etc. 🧠
Not published by PyPI - lahgtna-omnivoice
OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models
Not published by PyPI - laya
Fast, non-autoregressive System 1 decision engine with calibrated probabilities
Not published by PyPI - lazyllm-magic-pdf
A practical tool for converting PDF to Markdown
Not published by PyPI - learnbyvideo-whisper
Robust Speech Recognition via Large-Scale Weak Supervision
Not published by PyPI
Latest news the 3 latest events that mention it
- 39h agoOCT 24, 2026
- 3d agoOCT 22, 2026
- 4mo agoJUL 10, 2026