CannyLab/tsne-cuda

Health v2 · maintenance only

Adoption not part of the score

1957 stars · 138 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A CUDA-accelerated implementation of the FIt-SNE t-SNE algorithm with Python bindings, offering up to 1200x speedup over scikit-learn. It exposes an sklearn-compatible API for fast dimensionality reduction on large datasets.

Use cases

• visualize mnist embeddings with t-SNE on gpu
• fast t-SNE for millions of points
• dimensionality reduction of high-dimensional embeddings
• cluster visualization of cifar-10 features
• speed up sklearn TSNE with cuda
• barnes-hut t-SNE in python

When to choose

• you need t-SNE on large datasets (100k+ points) and have an NVIDIA GPU
• you want an sklearn-compatible TSNE drop-in replacement
• embedding quality comparable to reference implementations matters but runtime must be seconds

When to avoid

• you have no CUDA-capable GPU
• you need UMAP or other manifold methods
• you work only on CPU or with tiny datasets where sklearn suffices

Facets

library · maturity stable

machine-learning data-visualization gpu-computing machine-learning data-visualization data-science windows python tsne cuda dimensionality-reduction barnes-hut python-bindings sklearn-api linux gpu

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