From 627f651423fb33670dc3aec4092d0077f51b2d63 Mon Sep 17 00:00:00 2001 From: Daniel Date: Wed, 7 Feb 2024 18:02:12 +0800 Subject: [PATCH] readme image links --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index b8a8b12af..f1bf4260c 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # **cu-cat** -drawing +cu_cat ****cu-cat**** is an end-to-end gpu Python library that encodes categorical variables into machine-learnable numerics. It is a cuda @@ -28,11 +28,11 @@ We have routinely experienced boosts of 2x on smaller datasets to 10x and more a There is an inflection point when overhead of transing data to GPU is offset by speed boost, as we can see here. The axis represent unique features being inferred. -![image](examples/cucat_V_dirty.png) +![small](https://github.com/graphistry/cu-cat/blob/e2bae616f84aab8e6d5e173fc5363370d7680dc6/examples/cucat_V_dirty.png?raw=true) As we can see, with scale the divergence in speed is obvious. -![image](examples/big_cucat_V_dirty.png) +![cu_cat scaling](https://github.com/graphistry/cu-cat/blob/e2bae616f84aab8e6d5e173fc5363370d7680dc6/examples/big_cucat_V_dirty.png?raw=true) However, this graph does not mean to imply the trend goes on forever, as currently **cu-cat** is single GPU and cannot batch (as the transfer cost is too much for our current needs), and thus each dataset, and indeed GPU + GPU memory, is unique, and thus these plots are meant merely for demonstrative purposes. GPU = colab T4 + 15gb mem and colab CPU + 12gb memory