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ot-triton-lab/flash-sinkhorn
FlashSinkhorn: IO-Aware Entropic Optimal Transport in PyTorch + Triton. Streaming Sinkhorn with O(nd) memory.
187 19 +0/wk
GitHub
cuda entropic-optimal-transport flash-attention flashsinkhorn gpu machine-learning optimal-transport pytorch sinkhorn triton
Trend
0
Star & Fork Trend (14 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
ot-triton-lab/flash-sinkhorn has +0 stars this period . Velocity data will be available after more historical data is collected.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | flash-sinkhorn | compling_nlp_hse_course | rse-grand-challenge | agentic-ai-apis |
|---|---|---|---|---|
| Stars | 187 | 187 | 187 | 187 |
| Forks | 19 | 78 | 59 | 65 |
| Weekly Growth | +0 | +0 | +0 | +90 |
| Language | Python | Jupyter Notebook | Python | JavaScript |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | N/A | Apache-2.0 | N/A |
Capability Radar vs compling_nlp_hse_course
flash-sinkhorn
compling_nlp_hse_course
Maintenance Activity 100
Last code push 3 days ago.
Community Engagement 51
Fork-to-star ratio: 10.2%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.