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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 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
- open-mmlab/mmpretrain
OpenMMLab Pre-training Toolbox and Benchmark
3.9K stars
- mseitzer/pytorch-fid
Compute FID scores with PyTorch.
3.8K stars
- yuanzhoulvpi2017/zero_nlp
中文nlp解决方案(大模型、数据、模型、训练、推理)
3.8K stars
- predibase/lorax
Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs
3.8K stars
- lightly-ai/lightly
A python library for self-supervised learning on images.
3.8K stars
- IAHispano/Applio
A simple, high-quality voice conversion tool focused on ease of use and performance.
3.8K stars
- lucidrains/stylegan2-pytorch
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
3.8K stars
- polyaxon/polyaxon
AI Infra / AI Orchestration / AI Control Plane
3.7K stars
- christianversloot/machine-learning-articles
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
3.7K stars
- cleardusk/3DDFA
The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.
3.7K stars
- junxiaosong/AlphaZero_Gomoku
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
3.6K stars
- clovaai/stargan-v2
StarGAN v2 - Official PyTorch Implementation (CVPR 2020)
3.6K 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
- manga-ocr
OCR for Japanese manga
Not published by PyPI - marker-pdf
Convert documents to markdown with high speed and accuracy.
Not published by PyPI - mattergen
MatterGen is a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.
Not published by PyPI - mattergen-module-unofficial-akashkesav
MatterGen is a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.
Not published by PyPI - medmnist
[MedMNIST+] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification with Multiple Size Options: 28 (MNIST-Like), 64, 128, and 224
Not published by PyPI - megablocks
MegaBlocks
Not published by PyPI - megatron-core
Megatron Core - a library for efficient and scalable training of transformer based models
Not published by PyPI - megatron-fsdp
**Megatron-FSDP** is an NVIDIA-developed PyTorch extension that provides a high-performance implementation of Fully Sharded Data Parallelism (FSDP)
Not published by PyPI - megatts
MegaTTS 3 - A lightweight and efficient TTS system with ultra high-quality voice cloning
Not published by PyPI - memorisdk
Memori Python SDK
Not published by PyPI - memos
A package for Pensieve (previously known as memos)
Not published by PyPI - metatensor-torch
TorchScript bindings for metatensor
Not published by PyPI
Latest news the 3 latest events that mention it
- 42h agoOCT 24, 2026
- 3d agoOCT 22, 2026
- 4mo agoJUL 10, 2026