From 9807b754dffcbf67e3ff4c5396404d38e95ff07e Mon Sep 17 00:00:00 2001 From: cinderellaTiger <114470934+cinderellaTiger@users.noreply.github.com> Date: Mon, 20 Feb 2023 14:57:25 +0800 Subject: [PATCH] Update introduction_cn.ipynb (#5720) --- modelcenter/PP-HelixFold/introduction_cn.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modelcenter/PP-HelixFold/introduction_cn.ipynb b/modelcenter/PP-HelixFold/introduction_cn.ipynb index 96ae4f51..a2b07be9 100644 --- a/modelcenter/PP-HelixFold/introduction_cn.ipynb +++ b/modelcenter/PP-HelixFold/introduction_cn.ipynb @@ -7,11 +7,11 @@ "source": [ "## 1. PP-HelixFold模型简介\n", "\n", - "AlphaFold2是一款高精度的蛋白质结构预测模型。PP-HelixFold基于PaddlePaddle框架在GPU和DCU上完整复现了AlphaFold2的训练和推理流程，并进一步提升模型性能与精度。通过与原版AlphaFold2模型和哥伦比亚大学Mohammed AlQuraishi教授团队基于PyTorch复现的OpenFold模型的性能对比测试显示，PP-HelixFold将训练耗时从约11天减少到7.5天。在性能大幅度提升的同时，PP-HelixFold从头端到端完整训练可以达到AlphaFold2论文媲美的精度。\n", + "AlphaFold2是一款高精度的蛋白质结构预测模型。PP-HelixFold基于PaddlePaddle框架在GPU和DCU上完整复现了AlphaFold2的训练和推理流程，并进一步提升模型性能与精度。通过与原版AlphaFold2模型和哥伦比亚大学Mohammed AlQuraishi教授团队基于PyTorch复现的OpenFold模型的性能对比测试显示，PP-HelixFold将训练耗时从约11天减少到5.12天，在使用混合并行时只需要2.89 天。在性能大幅度提升的同时，PP-HelixFold从头端到端完整训练可以达到AlphaFold2论文媲美的精度。\n", "\n", "

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