K Means using PyTorch

PyTorch implementation of kmeans for utilizing GPU

Getting Started

import torch
import numpy as np
from kmeans_pytorch import kmeans

# data
data_size, dims, num_clusters = 1000, 2, 3
x = np.random.randn(data_size, dims) / 6
x = torch.from_numpy(x)

# kmeans
cluster_ids_x, cluster_centers = kmeans(
    X=x, num_clusters=num_clusters, distance='euclidean', device=torch.device('cuda:0')
)

see example.ipynb for a more elaborate example

Requirements

• PyTorch version >= 1.0.0
• Python version >= 3.6

Installation

install with pip:

pip install kmeans-pytorch

Installing from source

To install from source and develop locally:

git clone https://github.com/subhadarship/kmeans_pytorch
cd kmeans_pytorch
pip install --editable .

CPU vs GPU

see cpu_vs_gpu.ipynb for a comparison between CPU and GPU

Notes

• useful when clustering large number of samples
• utilizes GPU for faster matrix computations
• support euclidean and cosine distances (for now)

Credits

• This implementation closely follows the style of this
• Documentation is done using the awesome theme jekyllbook

License

MIT