diff --git a/modelcenter/PP-HelixFold/introduction_en.ipynb b/modelcenter/PP-HelixFold/introduction_en.ipynb index 1f00040af4de2d37aeef627f0aa23d7f84d9c9c5..9f3ad10d28c6a15efb4ff0a303bfbeba063d6bff 100644 --- a/modelcenter/PP-HelixFold/introduction_en.ipynb +++ b/modelcenter/PP-HelixFold/introduction_en.ipynb @@ -7,11 +7,11 @@ "source": [ "## 1. PP-HelixFold Introduction\n", "\n", - "AlphaFold2 is an accurate protein structure prediction pipeline. PP-HelixFold provides an efficient and improved implementation of the complete training and inference pipelines of AlphaFold2 in GPU and DCU. Compared with the computational performance of AlphaFold2 reported in the paper and OpenFold implemented through PyTorch, PP-HelixFold reduces the training time from about 11 days to 7.5 days. Training PP-HelixFold from scratch can achieve competitive accuracy with AlphaFold2.\n", + "AlphaFold2 is an accurate protein structure prediction pipeline. PP-HelixFold provides an efficient and improved implementation of the complete training and inference pipelines of AlphaFold2 in GPU and DCU. Compared with the computational performance of AlphaFold2 reported in the paper and OpenFold implemented through PyTorch, PP-HelixFold reduces the training time from about 11 days originally to 5.12 days, and only 2.89 days when using hybrid parallelism. Training HelixFold from scratch can achieve competitive accuracy with AlphaFold2.\n", "\n", "

\n", - "\n", - "\n", + "\n", + "\n", "

\n", "\n", "\n",