From a54962d36c5bd4e880ea37ead2826583509168ec Mon Sep 17 00:00:00 2001 From: Yancey1989 Date: Tue, 17 Apr 2018 10:27:34 +0800 Subject: [PATCH] update by comment --- doc/fluid/design/dist_train/async_update.md | 8 ++++---- .../dist_train/src/async_pserver.graffle | Bin 11035 -> 10525 bytes .../design/dist_train/src/async_pserver.png | Bin 169986 -> 169996 bytes 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/fluid/design/dist_train/async_update.md b/doc/fluid/design/dist_train/async_update.md index 344a44bde8..0318ef33c2 100644 --- a/doc/fluid/design/dist_train/async_update.md +++ b/doc/fluid/design/dist_train/async_update.md @@ -28,10 +28,10 @@ the parameter `w1` as an example to introduce the steps: 1. For each gradient variables, they may distribute on different GPU card and aggregate them while they are all calculated. 1. Split the gradient variable into multiple blocks according to the number of PServer -instances and then sent them. +instances and then send them. 1. PServer would run an `Optimize Block` using a specified optimize algorithm to update the specified parameter. -1. The trainer will fetch the parameter before running forward Op depends on the specified +1. The trainer will fetch the parameter before running forward Op which depends on the specified parameter. 1. Broadcast the received variable into multiple GPU cards and continue to run the next mini-batch. @@ -44,7 +44,7 @@ send the gradient variables to the multiple PServer instances. - Schedule `FetchVars` operator to fetch the latest parameter from PServer before running the forward ops. - There could be a large number of gradient variables to be sent, so we need to use another -thread pool(IO Threadpool) which a number of the schedulable threads is larger than the +thread pool(IO Threadpool) whose a number of the schedulable threads is larger than the computing thread pool to avoid competitive the thread resources with computing. ### Parameter Server @@ -52,7 +52,7 @@ computing thread pool to avoid competitive the thread resources with computing. - There should be multiple trainer instances want to optimize the same parameter at -the same time, to avoid the pollution, we need one `BlockingQueue` for each gradient +the same time, to avoid the racing, we need one `BlockingQueue` for each gradient variable to process them one by one. - We need a `Map` structure to map a gradient variable name to the `OptimizeBlock` which can optimize the respective parameter. diff --git a/doc/fluid/design/dist_train/src/async_pserver.graffle b/doc/fluid/design/dist_train/src/async_pserver.graffle index ea6dcfa2a9e4daac1500d98743a7a5a4234470f9..d2301611774fcb3866473e3e6470568d1e1312cf 100644 GIT binary patch literal 10525 zcmaLcQ*b8iwkPn|=-9Sx+fF*RJJuUp9Xsh*9ox2T+jia<^X)xnPEAeiIqPCwtgC-L zt7=_5KcXlYkbf08$d&I|OM!Ir%}I3+siAM%vtUJRbfj;^dzXzA0IX?@XZ^8H-I&scVphv z##dkK4t`ZBzCM!1pd~Cz3V%?VEtoJOdbHv5 z$sVjGWYeGuwdY&+S=L!BM)i~cRHF~dw^M0L`D3{&ZPTjPOeS>zp|W6*$w20#{K}AV zF-L#>SsW(K698$3@x_tiGIip7 z>XX_FeOq`2MHdmtDMo&~&*ml%(t2EfET&lcmrZ#e%ct5)@OzRzOVAUTGPjwV7Ua}S z>tVj+H77B3ve!_aug$^U-bBOnEK=BUO}RH;*#wK1@<@TpXw!YHt*-BJZM`2AT@-Mg z2GmIMZx4FDyEm9A@Eevk_(hJ0s5)T;;gCdb;LBq*A44!lef3t4w|S!EE>Gr4f{2*m zYO2J%49EuC_)FI@Ca-q!Z2Kt~Z_`cj_LvTgh8ic%_~@rXQcSw0l(7-iRS!-ecA!Wl 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