From 0d81126108c51deecbddbeb82048c0b0e79b7a8b Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Tue, 30 Oct 2018 16:22:34 +0800 Subject: [PATCH] update readme --- .../image_classification/dist_train/README.md | 43 +++++++++++++----- .../images/resnet50_32gpus-acc1.png | Bin 84794 -> 225542 bytes 2 files changed, 32 insertions(+), 11 deletions(-) diff --git a/fluid/PaddleCV/image_classification/dist_train/README.md b/fluid/PaddleCV/image_classification/dist_train/README.md index 282a026a..ecb8c840 100644 --- a/fluid/PaddleCV/image_classification/dist_train/README.md +++ b/fluid/PaddleCV/image_classification/dist_train/README.md @@ -9,7 +9,7 @@ Before getting started, please make sure you have go throught the imagenet [Data 1. The entrypoint file is `dist_train.py`, some important flags are as follows: - - `model`, the model to run with, such as `ResNet50`, `ResNet101` and etc.. + - `model`, the model to run with, default is the fine tune model `DistResnet`. - `batch_size`, the batch_size per device. - `update_method`, specify the update method, can choose from local, pserver or nccl2. - `device`, use CPU or GPU device. @@ -35,14 +35,14 @@ In this example, we launched 4 parameter server instances and 4 trainer instance 1. launch parameter server process - ``` python + ``` bash PADDLE_TRAINING_ROLE=PSERVER \ PADDLE_TRAINERS=4 \ PADDLE_PSERVER_IPS=192.168.0.100,192.168.0.101,192.168.0.102,192.168.0.103 \ PADDLE_CURRENT_IP=192.168.0.100 \ PADDLE_PSERVER_PORT=7164 \ python dist_train.py \ - --model=ResNet50 \ + --model=DistResnet \ --batch_size=32 \ --update_method=pserver \ --device=CPU \ @@ -51,34 +51,33 @@ In this example, we launched 4 parameter server instances and 4 trainer instance 1. launch trainer process - ``` python + ``` bash PADDLE_TRAINING_ROLE=TRAINER \ PADDLE_TRAINERS=4 \ PADDLE_PSERVER_IPS=192.168.0.100,192.168.0.101,192.168.0.102,192.168.0.103 \ PADDLE_TRAINER_ID=0 \ PADDLE_PSERVER_PORT=7164 \ python dist_train.py \ - --model=ResNet50 \ + --model=DistResnet \ --batch_size=32 \ --update_method=pserver \ --device=GPU \ --data_dir=../data/ILSVRC2012 - ``` ### NCCL2 Collective Mode 1. launch trainer process - ``` python + ``` bash PADDLE_TRAINING_ROLE=TRAINER \ PADDLE_TRAINERS=4 \ PADDLE_TRAINER_IPS=192.168.0.100,192.168.0.101,192.168.0.102,192.168.0.103 \ PADDLE_TRAINER_ID=0 \ python dist_train.py \ - --model=ResNet50 \ + --model=DistResnet \ --batch_size=32 \ - --update_method=pserver \ + --update_method=nccl2 \ --device=GPU \ --data_dir=../data/ILSVRC2012 ``` @@ -101,13 +100,35 @@ Pass 0, batch 8, loss 7.264951, accucacys: [0.0, 0.00390625] Pass 0, batch 9, loss 7.43522, accucacys: [0.00390625, 0.00390625] ``` -The training accucacys top1 of local training, distributed training with NCCL2 and parameter server architecture on the ResNet50 model are shown in the below figure: +The below figure shows top 1 train accuracy for local training with 8 GPUs and distributed training +with 32 GPUs, and also distributed training with batch merge feature turned on. Note that the +red curve is train with origin model configuration, which do not have warmup and some detailed modifications. + +For distributed training with 32GPUs using `--model DistResnet` we can achieve test accuracy 75.5% after +90 passes of training (the test accuracy is not shown in below figure).


-Training acc1 curves +Training top-1 accuracy curves

+### Finetuning for Distributed Training + +The default resnet50 distributed training config is based on this paper: https://arxiv.org/pdf/1706.02677.pdf + +- use `--model DistResnet` +- we use 32 P40 GPUs with 4 Nodes, each have 8 GPUs +- we set `batch_size=32` for each GPU, in `batch_merge=on` case, we repeat 4 times before communicating with pserver. +- learning rate start from 0.1 and warm up to 0.4 in 5 passes(because we already have gradient merging, + so we only need to linear scale up to trainer count) using 4 nodes. +- using batch_merge (`--multi_batch_repeat 4`) can make better use of GPU computing power and increase the + total training throughput. Because in the fine-tune configuration, we have to use `batch_size=32` per GPU, + and recent GPU is so fast that the communication between nodes may delay the total speed. In batch_merge mode + we run several batches forward and backward computation, then merge the gradients and send to pserver for + optimization, we use different batch norm mean and variance variable in each repeat so that adding repeats + behaves the same as adding more GPUs. + + ### Performance TBD diff --git a/fluid/PaddleCV/image_classification/images/resnet50_32gpus-acc1.png b/fluid/PaddleCV/image_classification/images/resnet50_32gpus-acc1.png index 6d4c478743d0e5af0a9d727c76b433849c6a81dc..a4dda7babd8054b813596e138945b05e7df330c0 100644 GIT binary patch literal 225542 zcmeFYWmKEp+BFKLxEBh+tw3-sF2#$xJCx$Z-Q5Bdm*NyF?(PsE6oR|EyX(hu-o4NH zwcq{s{P;3P#>lvHWl7e&*FEPouN9%BAcc-XhynuxgD(A5Tm=ROp$Z1(y$KS++m)l% zMtT?+G-*pQF(qj+F>)nGyYH4ZW-u^cBa$=`wN!`jvUF9XEdr3fBDY7V{FX(|mB9K* zKZhhup4;~mOZxU52gdP}KVFMasewO_-gpR7TJ z2{4p|m@|%%-9Ve;XmKQiKv_mEs7W&(8wa&osPDA@U6NoQQ2lqfoXTQ;p*Jt$4H_$j z>&G+Ed~&W39=&)Xobqw=Z;`#tSiK(u@gh*0A_!OeMLo0vLu)0usDxaR%pp8B(ROf( zg^{Io!sUa~Cf0X$k-MR&QqPXgq6RL_!UEO;U 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