From a5a8e6a0e28d0336184d5d1e99d8c6aa65ebad58 Mon Sep 17 00:00:00 2001 From: jiaruifang Date: Tue, 21 Dec 2021 14:51:13 +0800 Subject: [PATCH] add multiple machine results in README.md --- README.md | 10 ++++++++++ doc/m_node_superpod.png | Bin 0 -> 92488 bytes doc/one_node_perf_a100.png | Bin 116688 -> 113936 bytes examples/run_transformers.sh | 2 +- 4 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 doc/m_node_superpod.png diff --git a/README.md b/README.md index 2e9f121b7..72c7afad4 100644 --- a/README.md +++ b/README.md @@ -25,6 +25,16 @@ We also evaluated PatrickStar v0.4.3 on a single node of A100 SuperPod. It is ab Detail benchmark results on WeChat AI data center as well as NVIDIA SuperPod are posted on this [Google Doc](https://docs.google.com/spreadsheets/d/136CWc_jA_2zC4h1r-6dzD4PrOvp6aw6uCDchEyQv6sE/edit?usp=sharing). + +Scale PatrickStar to multiple machine (node) on SuperPod. +We succeed to train a GPT3-175B on 32 GPU. As far as we known, it is the first work +to run GPT3 on such small GPU cluster. +Microsoft used 10,000 V100 to pertrain GPT3. +Now you can finetune it or even pretrain your own one on 32 A100 GPU, amazing! + +![alt perf](./doc/m_node_superpod.png "performance testing result on multiple Node of SuperNode") + + We've also trained the [CLUE-GPT2](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall) model with PatrickStar, the loss and accuracy curve is shown below: ![CLUE-GPT2](./doc/clue-gpt2-loss-n-acc.png) diff --git a/doc/m_node_superpod.png b/doc/m_node_superpod.png new file mode 100644 index 0000000000000000000000000000000000000000..cd516c0e9e6e3ef2f1ba878f61b2850b24382714 GIT binary patch literal 92488 zcmcG$byOT%8t#qLxCNKSg1ZHRyL)hVC%C&4G(mz(aCZsr1Shy_aCaxSa^{|yx$~WW zKi2A1wYsXhYD?|?zR&a9L@Fsrq976=LO?*ENK1*SKtMouK|nxmfZ%{{tao5SARu7y ztwcqYY(ynRKiYnDQgtvgF_Sd2H*>NwQIQmZfZ&daQPZ;|QhOuN>TE$lIj(BU5$aMb 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