From 2aa8c6161275b8aea6905ff0c1666337d7fcfe70 Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Sat, 5 Nov 2016 23:07:51 +0800 Subject: [PATCH] add paddle cluster train on k8s --- .../cluster_train_on_kubernetes.md | 290 ++++++++++++++++++ 1 file changed, 290 insertions(+) create mode 100644 doc_cn/build_and_install/cluster_train_on_kubernetes.md diff --git a/doc_cn/build_and_install/cluster_train_on_kubernetes.md b/doc_cn/build_and_install/cluster_train_on_kubernetes.md new file mode 100644 index 0000000000..162b087a13 --- /dev/null +++ b/doc_cn/build_and_install/cluster_train_on_kubernetes.md @@ -0,0 +1,290 @@ + +# 使用Kubernetes进行分布式训练 + +>前一篇文章介绍了如何使用Kubernetes Job进行一次单机的Paddle训练。在这篇文档里，我们介绍如何使用 Kubernetes 进行Paddle的集群训练作业。 +>关于Paddle的分布式集群训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md), 本文在此基础上，利用了Kubernetes快速构建Paddle集群，进行分布式训练任务。 + +## 制作镜像 + +Paddle的集群训练需要有一个Paddle集群来实现，在本文中，我们使用Kubernetes来快速创建一个Paddle集群。我们使用 `paddledev/paddle:cpu-demo-latest` 镜像作为Paddle集群节点的运行环境，里面包含了 Paddle 运行所需要的相关依赖，同时，为了能将训练任务及配置统一分发到各个节点，需要使用到`sshd`以便使用`fabric`来操作。镜像的 Dockerfile 如下： + +``` +FROM paddledev/paddle:cpu-demo-latest + +RUN apt-get update +RUN apt-get install -y openssh-server +RUN mkdir /var/run/sshd +RUN echo 'root:root' | chpasswd + +RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config +RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config + +EXPOSE 22 + +CMD ["/usr/sbin/sshd", "-D"] +``` + +使用 `docker build` 构建镜像： + +``` +docker build -t mypaddle:paddle_demo_ssh . +``` + +## 准备工作空间 + +工作空间 [Job Workspace](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md#prepare-job-workspace) , 即一个包含了依赖库，训练，测试数据，模型配置文件的目录。参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)中的例子，我们也是用`demo/recommendation`作为本文的训练任务。此demo可直接从[Github Paddle源码](https://github.com/baidu/Paddle/tree/develop/demo/recommendation)中获取。 + +### 准备训练数据 + +在Paddle源码中，找到`demo/recommendation`文件夹，即为我们的Workspace, 在本文的环境中，路径为`/home/work/paddle-demo/Paddle/demo/recommendation` + +``` +[root@paddle-k8s-node0 recommendation]# tree +. +├── common_utils.py +├── data +│   ├── config_generator.py +│   ├── config.json +│   ├── meta_config.json +│   ├── meta_generator.py +│   ├── ml_data.sh +│   └── split.py +├── dataprovider.py +├── evaluate.sh +├── prediction.py +├── preprocess.sh +├── requirements.txt +├── run.sh +└── trainer_config.py + +1 directory, 14 files +``` + +运行`data/ml_data.sh`脚本，下载数据，然后运行`preprocess.sh`脚本进行预处理。 + +``` +[root@paddle-k8s-node0 recommendation]# data/ml_data.sh +++ dirname data/ml_data.sh ++ cd data ++ wget http://files.grouplens.org/datasets/movielens/ml-1m.zip +--2016-11-04 10:14:49-- http://files.grouplens.org/datasets/movielens/ml-1m.zip +Resolving files.grouplens.org (files.grouplens.org)... 128.101.34.146 +Connecting to files.grouplens.org (files.grouplens.org)|128.101.34.146|:80... connected. +HTTP request sent, awaiting response... 200 OK +Length: 5917549 (5.6M) [application/zip] +Saving to: ‘ml-1m.zip’ + +100%[==========================>] 5,917,549 50.6KB/s in 2m 29s + +2016-11-04 10:17:20 (38.8 KB/s) - ‘ml-1m.zip’ saved [5917549/5917549] + ++ unzip ml-1m.zip +Archive: ml-1m.zip + creating: ml-1m/ + inflating: ml-1m/movies.dat + inflating: ml-1m/ratings.dat + inflating: ml-1m/README + inflating: ml-1m/users.dat ++ rm ml-1m.zip + +[root@paddle-k8s-node0 recommendation]# ./preprocess.sh +generate meta config file +generate meta file +split train/test file +shuffle train file +``` + +### 修改集群训练配置 + +参考[Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)中的介绍，我们使用`paddle/scripts/cluster_train/`中的文件来作为分布式训练任务的配置和启动脚本。在`run.sh`文件中，填入我们的workspace和训练配置文件路径。 + +``` +#!/bin/sh +python paddle.py \ + --job_dispatch_package="/home/work/paddle-demo/Paddle/demo/recommendation" \ + --dot_period=10 \ + --ports_num_for_sparse=2 \ + --log_period=50 \ + --num_passes=10 \ + --trainer_count=4 \ + --saving_period=1 \ + --local=0 \ + --config=/home/work/paddle-demo/Paddle/demo/recommendation/trainer_config.py \ + --save_dir=./output \ + --use_gpu=0 +``` + +## 创建Paddle集群 + +创建Paddle集训需要编写创建Kubernetes资源的yaml文件，首先，创建一个Service,便于我们通过此Service来查找其对应的Paddle节点。 + +``` +apiVersion: v1 +kind: Service +metadata: + name: cluster-demo +spec: + selector: + app: cluster-demo + ports: + - name: default + protocol: TCP + port: 7164 + targetPort: 7164 +``` + +为了创建多个Paddle节点,我们使用Kubernetes ReplicationController资源来控制Paddle集群中的节点数量，Paddle节点之间需要开放相关的端口来互相通信。下面的例子中，我们开放了每个Paddle节点的7164-7167端口，例如，一个包含4个节点的Paddle集群的yaml文件如下： + +``` +apiVersion: v1 +kind: ReplicationController +metadata: + name: cluster-demo +spec: + replicas: 4 + selector: + app: cluster-demo + template: + metadata: + name: cluster-demo + labels: + app: cluster-demo + spec: + containers: + - name: cluster-demo + image: mypaddle:paddle_demo_ssh + ports: + - containerPort: 7164 + - containerPort: 7165 + - containerPort: 7166 + - containerPort: 7167 +``` + +然后我们可以通过`kubectl`工具来查看所创建的资源信息。 + +首先查看我们创建的Paddle Service，然后根据Service,查看所创建的Paddle节点的IP地址。 + +``` +[root@paddle-k8s-node0 cluster_train]# kubectl get svc +NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE +cluster-demo 11.1.1.77 7164/TCP 6h + +[root@paddle-k8s-node0 cluster_train]# kubectl get -o json endpoints cluster-demo | grep ip + "ip": "192.168.129.79", + "ip": "192.168.129.80", + "ip": "192.168.223.157", + "ip": "192.168.223.158", +``` + +## 开始集群训练 + +我们需要在`paddle/scripts/cluster_train/conf.py`文件中指定各个节点的IP地址以及开放的端口。根据上文创建的信息，`conf.py`文件修改如下： + +``` +HOSTS = [ + "root@192.168.129.79", + "root@192.168.129.80", + "root@192.168.223.157", + "root@192.168.223.158" + ] + +''' +workspace configuration +''' +#root dir for workspace, can be set as any director with real user account +ROOT_DIR = "/home/paddle" + + +''' +network configuration +''' +#pserver nics +PADDLE_NIC = "eth0" +#pserver port +PADDLE_PORT = 7164 +#pserver ports num +PADDLE_PORTS_NUM = 2 +#pserver sparse ports num +PADDLE_PORTS_NUM_FOR_SPARSE = 2 + +#environments setting for all processes in cluster job +LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib64" +``` +然后使用`run.sh`脚本开始训练，启动的打印如下： + +``` +[root@paddle-k8s-node0 cluster_train]# ./run.sh +[root@192.168.129.79] Executing task 'job_create_workspace' +...... +[root@192.168.129.80] Executing task 'job_create_workspace' +...... +[root@192.168.223.157] Executing task 'job_create_workspace' +...... +[root@192.168.223.158] Executing task 'job_create_workspace' +...... +[root@192.168.129.79] run: echo 0 > /home/paddle/JOB20161104171630/nodefile +[root@192.168.129.80] Executing task 'set_nodefile' +[root@192.168.129.80] run: echo 1 > /home/paddle/JOB20161104171630/nodefile +[root@192.168.223.157] Executing task 'set_nodefile' +[root@192.168.223.157] run: echo 2 > /home/paddle/JOB20161104171630/nodefile +[root@192.168.223.158] Executing task 'set_nodefile' +[root@192.168.223.158] run: echo 3 > /home/paddle/JOB20161104171630/nodefile +``` + +可以看到192.168.129.79，192.168.129.80，192.168.223.157，192.168.223.158分别为Paddle集群的Node 0-3. + +我们可以进入其中一个Paddle节点查看训练的日志。 + +``` +root@cluster-demo-fwwi5:/home/paddle/JOB20161104171700/log# less paddle_trainer.INFO +Log file created at: 2016/11/04 09:17:20 +Running on machine: cluster-demo-fwwi5 +Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg +I1104 09:17:20.346797 108 Util.cpp:155] commandline: /usr/local/bin/../opt/paddle/bin/paddle +_trainer --num_gradient_servers=4 --nics=eth0 --port=7164 --ports_num=2 --comment=paddle_proce +ss_by_paddle --pservers=192.168.129.79,192.168.129.80,192.168.223.157,192.168.223.158 --ports_ +num_for_sparse=2 --config=./trainer_config.py --trainer_count=4 --use_gpu=0 --num_passes=10 -- +save_dir=./output --log_period=50 --dot_period=10 --saving_period=1 --local=0 --trainer_id=1 + +root@cluster-demo-fwwi5:/home/paddle/JOB20161104171700/log# tailf paddle_trainer.INFO +...... +I1104 09:17:37.376471 150 ThreadLocal.cpp:37] thread use undeterministic rand seed:151 +I1104 09:18:54.159624 108 TrainerInternal.cpp:163] Batch=50 samples=80000 AvgCost=4.03478 CurrentCost=4.03478 Eval: CurrentEval: + +I1104 09:20:10.207902 108 TrainerInternal.cpp:163] Batch=100 samples=160000 AvgCost=3.75806 CurrentCost=3.48134 Eval: CurrentEval: +I1104 09:21:26.493571 108 TrainerInternal.cpp:163] Batch=150 samples=240000 AvgCost=3.64512 CurrentCost=3.41923 Eval: CurrentEval: + +``` + +最后，我们可以在Paddle集群的node0（192.168.129.79）上查看训练的输出结果。 + +``` +[root@paddle-k8s-node0 ~]# ssh root@192.168.129.79 +...... +root@cluster-demo-r65g0:/home/paddle/JOB20161104171700/output/pass-00000# ll +total 14876 +drwxr-xr-x. 2 root root 4096 Nov 4 09:40 ./ +drwxr-xr-x. 3 root root 23 Nov 4 09:40 ../ +-rw-r--r--. 1 root root 4046864 Nov 4 09:40 ___embedding_0__.w0 +-rw-r--r--. 1 root root 100368 Nov 4 09:40 ___embedding_1__.w0 +-rw-r--r--. 1 root root 6184976 Nov 4 09:40 ___embedding_2__.w0 +-rw-r--r--. 1 root root 2064 Nov 4 09:40 ___embedding_3__.w0 +-rw-r--r--. 1 root root 7184 Nov 4 09:40 ___embedding_4__.w0 +-rw-r--r--. 1 root root 21520 Nov 4 09:40 ___embedding_5__.w0 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 ___fc_layer_0__.w0 +-rw-r--r--. 1 root root 1040 Nov 4 09:40 ___fc_layer_0__.wbias +...... +...... +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w0 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w1 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w2 +-rw-r--r--. 1 root root 1040 Nov 4 09:40 _movie_fusion.wbias +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w0 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w1 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w2 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w3 +-rw-r--r--. 1 root root 1040 Nov 4 09:40 _user_fusion.wbias +-rw-r--r--. 1 root root 169 Nov 4 09:40 done +-rw-r--r--. 1 root root 17 Nov 4 09:40 path.txt +-rw-r--r--. 1 root root 3495 Nov 4 09:40 trainer_config.py +``` \ No newline at end of file -- GitLab