From d41b40281b39afa5c6d3dc4031865cc46e6a20cc Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 13 Apr 2017 13:44:39 -0700 Subject: [PATCH] fix according to comments --- doc/design/dist/README.md | 19 +++++++++---------- doc/design/dist/src/arch.graffle | Bin 3834 -> 0 bytes doc/design/dist/src/arch.png | Bin 52097 -> 0 bytes 3 files changed, 9 insertions(+), 10 deletions(-) delete mode 100644 doc/design/dist/src/arch.graffle delete mode 100644 doc/design/dist/src/arch.png diff --git a/doc/design/dist/README.md b/doc/design/dist/README.md index 321398ce16..8f5bc98125 100644 --- a/doc/design/dist/README.md +++ b/doc/design/dist/README.md @@ -2,25 +2,24 @@ ## Objective -We want Paddle to support training on the general-purpose cluster. The cluster runs Paddle, the web server (e.g., Nginx), the log collector (e.g., fluentd), the distributed queue service (e.g., Kafka), the log joiner and other data processors written using Storm, Spark, and Hadoop MapReduce on the same cluster. As illustrated in the following graph: +In [this slides](https://www.slideshare.net/cxwangyi/paddlepaddle-a-complete-solution-for-businesses), we explained that we'd like PaddlePaddle running on general-purpose clusters like those managed by Kubernetes, so to address demands for AI from both Internet and non-Internet industries. - +This poses technical challenges to PaddlePaddle: -This poses new challenges for Paddle, +1. Support fault-recovery. +1. Support both offline and online training. +1. [Serverless computing](https://en.wikipedia.org/wiki/Serverless_computing) of distributed training. -- Paddle need to be fault tolerant. -- Input training data can be online data from real time logs or batch data from distributed file system. -- User needs a simple way to train model on Paddle cloud. Complexities such as job scheduling should be hidden from user. ## Training Job A training job will be created once user asks Paddle cloud to train a model. The training job is made up of different processes that collaboratively consume data and produce a trained model. There are three kinds of processes: -- Master process -- Trainer process -- Parameter server process +1. the *master process*, which dispatches tasks to +1. one or more *trainer processes*, which run distributed training and synchronize gradients/models via +1. one or more *parameter server processes*, where each holds a shard of the global model. -One training job will only have one master process, typically multiple trainer processes and parameter server processes. Their relation is illustrated in the following graph: +Their relation is illustrated in the following graph: diff --git a/doc/design/dist/src/arch.graffle b/doc/design/dist/src/arch.graffle deleted file mode 100644 index f3a0693d51a0e05371877ce0b5aeabc66287a072..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3834 zcmVxDg;zHy!!X#;p^(wi9nNp4ovY zNaC6z6_T>!X7b+`Aa&tYkyP1^T=+qh#e)lh^PTe@@POR=`SZ{>ZbcM(Ver!qG{KG` z0ype>LI06GB=%aH6L_k)1!gs&dN;2VIrm^;_10rm%e-f zFAebZrSSzL;+P0fOq}%7c3v9Lp_U1lgQ=B$;;&x|7p7`E@OfOV_+r3EV&0`vnd8HG zf$8<2{H0}L7se>@D@zSj>bor2X) zrP3E8?#e1=@{!>FYb@0%{W2Am|0umbU5WdY;gK>k&Frz#1f za``JB^#AnA!iQcw^7;LR%l%4=(C`A|Cqo^|#eVU8ad|(g6yXv%W!Y2*4?}l66hTr( z^laF0W!xsZ8!MwzV*!fFSb|p$Ul7{Rq&Bl&8jfueVmS!f7-E7cb`X2 zt#4y%IylLt_?%SB{dsgzBkhNGRnc=1r#e}d&H;Bmcy>YQCXy#!umGI-dgc|eq79hH zo`tcOAbe&n*^ng~2R-Piq?RZeH`MgI6}-O%}YS?viWROxs1K2auHDcLxjIDv$^MDQ9r-_D;Ki5gUKk`v$G`4@>t5i= zqdVYHED~V65&o@6Bwu+a#^T4XQr1;ECxw5!jzkw()^$G;B2ZsC{#eMPl)4`AsC%7! 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