Life & fun · alternatives · 2026

24 alternatives to PyTorch K-Means GPU-friendly, single-file, hierarchical and resampling

I built a small, self-contained K-Means implementation in pure PyTorch: https://gitlab.com/hassonofer/pt_kmeans I was working on dataset sampling and approximate nearest neighbor search, and tried several existing libraries for large-scale K-Means. I couldn't find something that was fast, simple, and would run comfortably on my own workstation without hitting memory limits. Maybe I missed an existing solution, but I ended up writing one that fit my needs. The core insight: Keep your data on CPU (where you have more RAM) and intelligently move only the necessary chunks to…

PyTorch K-Means is a lightweight, single-file implementation of K-Means clustering built entirely in PyTorch. It is designed for users working with large datasets on resource-constrained hardware. The implementation… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
Want something better than k-means? Try BanditPAM▲281
Want something better than k-means? I'm happy to announce our SOTA k-medoids algorithm from NeurIPS 2020, BanditPAM, is now publicly available! `pip install banditpam` or `install.packages("banditpam")` and you're good to go! k-means is one of the most widely-used algorithms to cluster data. However, it has several limitations: a) it requires the use of L2 distance for efficient clustering, which also b) restricts the data you're clustering to be vectors, and c) doesn't require the means to be datapoints in the dataset. Unlike in k-means, the k-medoids problem requires cluster centers to be…
2023 · github.com · its alternatives →
2
I stripped DALL·E Mini to its bare essentials and converted it to Torch▲663
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch - kuprel/min-dalle
2022 · github.com · its alternatives →
3
TensorFlow 1.0▲823
Open-source machine learning library by Google
2017 · tensorflow.org · its alternatives →
4
Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon▲202
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
Apr 2026 · github.com · its alternatives →
5
Weights & Biases▲110
Developer tools for deep learning & machine learning
2019 · wandb.ai · its alternatives →
6BA
Brancher – A user-friendly PyTorch module for probabilistic inference▲96
2019 · brancher.org · its alternatives →
7
PyTorch Lightning▲79
The light PyTorch wrapper for high-performance AI research
2020 · pytorchlightning.ai · its alternatives →
8
Encrypted Deep Learning in PyTorch▲37
Perform data science on data that remains in someone else's server - OpenMined/PySyft
2018 · github.com · its alternatives →
9AG
A GPU-accelerated binary vector index▲65
This is a vector index I built that supports insertion and k-nearest neighbors (k-NN) querying, optimized for GPUs. It operates entirely in CUDA and can process queries on half a billion vectors in under 200 milliseconds. The codebase is structured as a standalone library with an HTTP API for remote access. It’s intended for high-performance search tasks—think similarity search, AI model retrieval, or reinforcement learning replay buffers. The codebase is located at https://github.com/rodlaf/BinaryGPUIndex.
2025 · rlafuente.com · its alternatives →
10
Graphlearn-for-PyTorch, distributed graph learning on PyTorch▲9
Hello HN, We are pleased to introduce you graphlearn-for-pytorch (https://github.com/alibaba/graphlearn-for-pytorch), an open-source distributed graph neural network library based on PyTorch and compatible with PyG. Our library is designed to make it easy for developers to build and train large-scale graph models in a distributed environment. With graphlearn-for-pytorch, you can leverage GPUs to accelerate graph sampling and utilize UVA to reduce the overheads of feature collection. Following a scalable design, graphlearn-for-pytorch supports training GNN models on…
2023 · github.com · its alternatives →
11
Faster R-CNN object detector implemented in PyTorch and TensorFlow 2▲15
Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras. I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from. I also wanted to solidify my understanding of…
2022 · github.com · its alternatives →
12
Machine Learning on Spatiotemporal Data – PyTorch Geometric Temporal▲10
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021) - benedekrozemberczki/pytorch_geometric_temporal
2021 · github.com · its alternatives →
13
TorchGAN – Research Framework for Modelling GANs Based on Pytorch▲56
Research Framework for easy and efficient training of GANs based on Pytorch - torchgan/torchgan
2018 · github.com · its alternatives →
14
Comgra: A library for debugging and understanding neural networks▲66
I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com · its alternatives →
15
TorchTPU▲106
Running PyTorch Natively on TPUs at Google Scale
Apr 2026 · cloud.google.com · its alternatives →
16
Pytorch Text Recognition Tool▲41
Tool which allow you to detect and translate text. - s3nh/text-detector
2019 · github.com · its alternatives →
17
I built a tensor library from scratch in C++/CUDA▲119
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com · its alternatives →
18
BloomSearch – Keyword search with hierarchical Bloom filters▲66
Hey HN! I got nerd-sniped by Bloom Filters this weekend, specifically for searching datasets with high "cardinality" (number of unique items). They're an _amazing_ data structure that, at a fixed size, tracks potential set membership. That means unlike normal b-tree indexes, they don't grow with the number of unique items in the dataset. This makes them great for "needle in a haystack" search (logs, document) as implementations like VictoriaMetrics and Bing's BitFunnel show. I've used them in the past, but they've never been center-stage in my projects. I wanted high cardinality keyword…
2025 · github.com · its alternatives →
19
LeanRL: Fast PyTorch RL with Torch.compile and CUDA Graphs▲53
We're excited to announce that we've open-sourced LeanRL, a lightweight PyTorch reinforcement learning library that provides recipes for fast RL training using torch.compile and CUDA graphs. By leveraging these tools, we've achieved significant speed-ups compared to the original CleanRL implementations - up to 6x faster! Reinforcement learning is notoriously CPU-bound due to the high frequency of small CPU operations. PyTorch's powerful compiler can help alleviate these issues, but comes with its own costs. LeanRL addresses this challenge by providing simple recipes to accelerate your…
2024 · github.com · its alternatives →
20
Autograd.c – A tiny ML framework built from scratch▲85
built a tiny pytorch clone in c after going through prof. vijay janapa reddi's mlsys book: mlsysbook.ai/tinytorch/ perfect for learning how ml frameworks work under the hood :)
Dec 2025 · github.com · its alternatives →
21AR
A realtime dashboard for Keras neural nets▲87
2016 · tryhera.com · its alternatives →
22
Open-Sourcing Google's Lattice Models in PyTorch▲13
We're a group of engineers, AI/ML enthusiasts, and author of this paper https://openreview.net/forum?id=0pxiMpCyBtr who saw a closed door in AI/ML and decided to open it. This project is a PyTorch reimagining of Google's TensorFlow Lattice models, which despite being labeled open-source, were previously open in name only (you have to be a Googler to contribute). Also, side point…TensorFlow is dying https://thenextweb.com/news/why-tensorflow-for-python-is-dyi... Here's the deal: Lattice models excel in making AI interpretable—key for sectors where…
2023 · github.com · its alternatives →
23
DataTorch▲69
Build and share datasets to experiment with ML
2021 · datatorch.io · its alternatives →
24
Keys and Caches▲66
Vibe profile your ML models to get max performance.
2025 · github.com · its alternatives →

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