From 60e0df0eeff0ba4155077f2fce93a55c30884028 Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Tue, 28 Feb 2017 17:19:00 +0800 Subject: [PATCH] refine k8s distributed training doc --- doc/howto/usage/k8s/k8s_basis.md | 77 +++++++ doc/howto/usage/k8s/k8s_distributed_cn.md | 241 +++++++++----------- doc/howto/usage/k8s/src/k8s-paddle-arch.png | Bin 22084 -> 530508 bytes 3 files changed, 182 insertions(+), 136 deletions(-) create mode 100644 doc/howto/usage/k8s/k8s_basis.md diff --git a/doc/howto/usage/k8s/k8s_basis.md b/doc/howto/usage/k8s/k8s_basis.md new file mode 100644 index 0000000000..e296d69dce --- /dev/null +++ b/doc/howto/usage/k8s/k8s_basis.md @@ -0,0 +1,77 @@ +# Kubernetes 简介 + +[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统,其提供应用部署、维护、 扩展机制等功能,利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行,且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws),[Azure](http://kubernetes.io/docs/getting-started-guides/azure/),[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前,需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识,下面先简要介绍一下本文用到的几个Kubernetes概念。 + +- [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点,这个节点可以是物理机或者虚拟机,Kubernetes集群就是由node节点与master节点组成的。 + +- [*Pod*](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器,pod是Kubernetes的最小调度单元,一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET,PID,IPC,UTS等Linux namespace。由于容器之间共享NET namespace,所以它们使用同一个IP地址,可以通过*localhost*互相通信。不同pod之间可以通过IP地址访问。 + +- [*Job*](http://kubernetes.io/docs/user-guide/jobs/) 描述Kubernetes上运行的作业,一次作业称为一个job,通常每个job包括一个或者多个pods,job启动后会创建这些pod并开始执行一个程序,等待这个程序执行成功并返回0则成功退出,如果执行失败,也可以配置不同的重试机制。 + +- [*Volume*](http://kubernetes.io/docs/user-guide/volumes/) 存储卷,是pod内的容器都可以访问的共享目录,也是容器与node之间共享文件的方式,因为容器内的文件都是暂时存在的,当容器因为各种原因被销毁时,其内部的文件也会随之消失。通过volume,就可以将这些文件持久化存储。Kubernetes支持多种volume,例如hostPath(宿主机目录),gcePersistentDisk,awsElasticBlockStore等。 + +- [*Namespaces*](https://kubernetes.io/docs/user-guide/namespaces/) 命名空间,在kubernetes中创建的所有资源对象(例如上文的pod,job)等都属于一个命名空间,在同一个命名空间中,资源对象的名字是唯一的,不同空间的资源名可以重复,命名空间主要为了对象进行逻辑上的分组便于管理。本文只使用了默认命名空间。 + +- [*PersistentVolume*]() + +- [*PersistentVolumeClaim*]() + +# 部署Kubernetes集群 + +Kubernetes提供了多种集群部署的方案,本文档内不重复介绍。这里给出集中常见的部署方法: + +- [*minikube*](https://kubernetes.io/docs/getting-started-guides/minikube/): 快速在本地启动一个单机的kubernetes服务器,便于本地验证和测试。 +- [*kubeadm*](http://kubernetes.io/docs/getting-started-guides/kubeadm/): 在不同操作系统,不同主机(Bare-Metal, AWS, GCE)条件下,快速部署集群。 +- [*AWS EC2*](https://kubernetes.io/docs/getting-started-guides/aws/): 在aws上快速部署集群。 +- [*Bare-Metal*](https://kubernetes.io/docs/getting-started-guides/centos/centos_manual_config/): 在物理机上手动部署。 + +可以参考[这个表格](https://kubernetes.io/docs/getting-started-guides/#table-of-solutions)选择适合您的场景的合适方案。 + +# 选择存储方案 + +容器不会保留在运行时生成的数据,job或者应用程序在容器中运行时生成的数据会在容器销毁时消失。为了完成分布式机器学习训练任务,需要有一个外部的存储服务来保存训练所需数据和训练输出。 +常见的可选存储服务包括: + +- [*NFS*](https://github.com/kubernetes/kubernetes/tree/master/examples/volumes/nfs): 可以将磁盘上某个目录共享给网络中其他机器访问。部署和配置比较简单,可以用于小量数据的验证。不提供分布式存储,高可用,冗余等功能。NFS的部署方法可以参考[这里](http://www.tecmint.com/how-to-setup-nfs-server-in-linux/)。 +- [*GlusterFS*](http://gluster.readthedocs.io/en/latest/Quick-Start-Guide/Quickstart/): 网络分布式文件系统,可以在Kubernetes中按照[这个](https://github.com/kubernetes/kubernetes/tree/master/examples/volumes/glusterfs)例子使用。 +- [*Ceph*](http://docs.ceph.com/docs/master/): 分布式文件系统,支持rbd,POSIX API接口(ceph fs)和对象存储API,参考[这里](https://kubernetes.io/docs/user-guide/volumes/#rbd)。 +- [*MooseFS*](https://moosefs.com/documentation.html): 一个分布式的存储系统。需要先挂载到服务器Node上再通过kubernetes hostPath Volume挂载到容器中。 + +# 配置kubectl + +## 安装kubectl +``` +# OS X +curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/darwin/amd64/kubectl + +# Linux +curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl + +# Windows +curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/windows/amd64/kubectl.exe +``` + +## 配置kubectl访问你的kubernetes集群 + +编辑`~/.kube/config`这个配置文件,修改`Master-IP`的地址。如果使用SSL认证,则需要配置`certificate-authority`和`users`中的用户证书。如果是使用非SSL方式访问(比如通过8080都安口),也可以去掉这些证书的配置。 +``` +apiVersion: v1 +clusters: +- cluster: + certificate-authority: /path/to/ca.crt + server: https://[Master-IP]:443 + name: minikube +contexts: +- context: + cluster: minikube + user: minikube + name: minikube +current-context: minikube +kind: Config +preferences: {} +users: +- name: minikube + user: + client-certificate: /path/to/apiserver.crt + client-key: /Users/wuyi/.minikube/apiserver.key +``` diff --git a/doc/howto/usage/k8s/k8s_distributed_cn.md b/doc/howto/usage/k8s/k8s_distributed_cn.md index 2063b98ca8..cfc3086036 100644 --- a/doc/howto/usage/k8s/k8s_distributed_cn.md +++ b/doc/howto/usage/k8s/k8s_distributed_cn.md @@ -1,169 +1,51 @@ -# Kubernetes分布式训练 +# 使用Paddle在Kubernetes集群上完成分布式训练 前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里,我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练,文章 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)介绍了一种通过SSH远程分发任务,进行分布式训练的方法,与此不同的是,本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群,进行分布式训练的方案。 -## Kubernetes 基本概念 - -[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统,其提供应用部署、维护、 扩展机制等功能,利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行,且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws),[Azure](http://kubernetes.io/docs/getting-started-guides/azure/),[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前,需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识,下面先简要介绍一下本文用到的几个Kubernetes概念。 - -- [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点,这个节点可以是物理机或者虚拟机,Kubernetes集群就是由node节点与master节点组成的。 - -- [*Pod*](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器,pod是Kubernetes的最小调度单元,一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET,PID,IPC,UTS等Linux namespace。由于容器之间共享NET namespace,所以它们使用同一个IP地址,可以通过*localhost*互相通信。不同pod之间可以通过IP地址访问。 - -- [*Job*](http://kubernetes.io/docs/user-guide/jobs/) 是Kubernetes上运行的作业,一次作业称为一个job,通常每个job包括一个或者多个pods。 - -- [*Volume*](http://kubernetes.io/docs/user-guide/volumes/) 存储卷,是pod内的容器都可以访问的共享目录,也是容器与node之间共享文件的方式,因为容器内的文件都是暂时存在的,当容器因为各种原因被销毁时,其内部的文件也会随之消失。通过volume,就可以将这些文件持久化存储。Kubernetes支持多种volume,例如hostPath(宿主机目录),gcePersistentDisk,awsElasticBlockStore等。 - -- [*Namespaces*](http://kubernetes.io/docs/user-guide/volumes/) 命名空间,在kubernetes中创建的所有资源对象(例如上文的pod,job)等都属于一个命名空间,在同一个命名空间中,资源对象的名字是唯一的,不同空间的资源名可以重复,命名空间主要为了对象进行逻辑上的分组便于管理。本文只使用了默认命名空间。 +有关Kubernetes相关概念以及如何搭建和配置Kubernetes集群,可以参考[k8s_basis](./k8s_basis.md)。 ## 整体方案 -### 部署Kubernetes集群 - -首先,我们需要拥有一个Kubernetes集群,在这个集群中所有node与pod都可以互相通信。关于Kubernetes集群搭建,可以参考[官方文档](http://kubernetes.io/docs/getting-started-guides/kubeadm/),在以后的文章中我们也会介绍AWS上搭建的方案。本文假设大家能找到几台物理机,并且可以按照官方文档在上面部署Kubernetes。在本文的环境中,Kubernetes集群中所有node都挂载了一个[MFS](http://moosefs.org/)(Moose filesystem,一种分布式文件系统)共享目录,我们通过这个目录来存放训练文件与最终输出的模型。关于MFS的安装部署,可以参考[MooseFS documentation](https://moosefs.com/documentation.html)。在训练之前,用户将配置与训练数据切分好放在MFS目录中,训练时,程序从此目录拷贝文件到容器内进行训练,将结果保存到此目录里。整体的结构图如下: +在训练之前,用户将配置与训练数据切分好放在MFS目录中,训练时,程序从此目录拷贝文件到容器内进行训练,将结果保存到此目录里。整体的结构图如下: ![paddle on kubernetes结构图](src/k8s-paddle-arch.png) -上图描述了一个3节点的分布式训练场景,Kubernetes集群的每个node上都挂载了一个MFS目录,这个目录可以通过volume的形式挂载到容器中。Kubernetes为这次训练创建了3个pod并且调度到了3个node上运行,每个pod包含一个PaddlePaddle容器。在容器创建后,会启动pserver与trainer进程,读取volume中的数据进行这次分布式训练。 - -### 使用 Job - -我们使用Kubernetes中的job这个概念来代表一次分布式训练。Job表示一次性作业,在作业完成后,Kubernetes会销毁job产生的容器并且释放相关资源。 - -在Kubernetes中,可以通过编写一个YAML文件,来描述这个job,在这个文件中,主要包含了一些配置信息,例如PaddlePaddle的节点个数,`paddle pserver`开放的端口个数与端口号,使用的网卡设备等,这些信息通过环境变量的形式传递给容器内的程序使用。 +上图描述了一个3节点的分布式训练场景,在每个Pod上都通过volume方式挂载分布式文件系统的一个目录用于保存训练数据和输出结果。Kubernetes为这次训练创建了3个pod并且调度到了3个node上运行,每个pod包含一个PaddlePaddle容器。在容器创建后,会启动pserver与trainer进程,读取volume中的数据进行这次分布式训练。 -在一次分布式训练中,用户确定好本次训练需要的PaddlePaddle节点个数,将切分好的训练数据与配置文件上传到MFS共享目录中。然后编写这次训练的job YAML文件,提交给Kubernetes集群创建并开始作业。 +根据前文的描述,要在已有的Kubernetes集群上进行PaddlePaddle的分布式训练,按照下面步骤即可: -### 创建PaddlePaddle节点 - -当Kubernetes master收到请求,解析完YAML文件后,会创建出多个pod(个数为PaddlePaddle节点数),Kubernetes会把这些pod调度到集群的node上运行。一个pod就代表一个PaddlePaddle节点,当pod被成功分配到一台物理/虚拟机上后,Kubernetes会启动pod内的容器,这个容器会根据YAML文件中的环境变量,启动`paddle pserver`与`paddle train`进程。 - -### 启动训练 - -在容器启动后,会通过脚本来启动这次分布式训练,我们知道`paddle train`进程启动时需要知道其他节点的IP地址以及本节点的trainer_id,由于PaddlePaddle本身不提供类似服务发现的功能,所以在本文的启动脚本中,每个节点会根据job name向Kubernetes apiserver查询这个job对应的所有pod信息(Kubernetes默认会在每个容器的环境变量中写入apiserver的地址)。 - -根据这些pod信息,就可以通过某种方式,为每个pod分配一个唯一的trainer_id。本文把所有pod的IP地址进行排序,将顺序作为每个PaddlePaddle节点的trainer_id。启动脚本的工作流程大致如下: - - 1. 查询Kubernetes apiserver获取pod信息,根据IP分配trainer_id - 1. 从MFS共享目录中拷贝训练文件到容器内 - 1. 根据环境变量,解析出`paddle pserver`与`paddle train`的启动参数,启动进程 - 1. 训练时,PaddlePaddle会自动将结果保存在trainer_id为0的节点上,将输出路径设置为MFS目录,保存输出的文件 - - -## 搭建过程 - -根据前文的描述,要在已有的Kubernetes集群上进行PaddlePaddle的分布式训练,主要分为以下几个步骤: - -1. 制作PaddlePaddle镜像 -1. 将训练文件与切分好的数据上传到共享存储 -1. 编写本次训练的YAML文件,创建一个Kubernetes job -1. 训练结束后查看输出结果 +1. [制作PaddlePaddle镜像](#制作镜像) +1. [将训练文件与切分好的数据上传到共享存储](#上传训练文件) +1. [编写本次训练的YAML文件,创建一个Kubernetes job](#创建Job) +1. [训练结束后查看输出结果](#查看输出) 下面就根据这几个步骤分别介绍。 - ### 制作镜像 PaddlePaddle镜像需要提供`paddle pserver`与`paddle train`进程的运行环境,用这个镜像创建的容器需要有以下两个功能: - 拷贝训练文件到容器内 - - 生成`paddle pserver`与`paddle train`进程的启动参数,并且启动训练 -因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能,所以我们可以在这个基础上,添加启动脚本,制作新镜像来完成以上的工作。镜像的*Dockerfile*如下: - -```Dockerfile -FROM paddledev/paddle:cpu-latest - -MAINTAINER zjsxzong89@gmail.com - -COPY start.sh /root/ -COPY start_paddle.py /root/ -CMD ["bash"," -c","/root/start.sh"] -``` - -[start.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/start.sh)文件拷贝训练文件到容器内,然后执行[start_paddle.py](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/start_paddle.py)脚本启动训练,前文提到的获取其他节点IP地址,分配`trainer_id`等都在`start_paddle.py`脚本中完成。 - -`start_paddle.py`脚本开始时,会先进行参数的初始化与解析。 - -```python -parser = argparse.ArgumentParser(prog="start_paddle.py", - description='simple tool for k8s') - args, train_args_list = parser.parse_known_args() - train_args = refine_unknown_args(train_args_list) - train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) - podlist = getPodList() -``` - -然后通过函数`getPodList()`访问Kubernetes的接口来查询此job对应的所有pod信息。当所有pod都处于running状态(容器运行都运行)时,再通过函数`getIdMap(podlist)`获取trainer_id。 - -```python - podlist = getPodList() - # need to wait until all pods are running - while not isPodAllRunning(podlist): - time.sleep(10) - podlist = getPodList() - idMap = getIdMap(podlist) -``` - -在函数`getIdMap(podlist)`内部,我们通过读取`podlist`中每个pod的IP地址,将IP排序生成的序号作为trainer_id。 - -```python -def getIdMap(podlist): - ''' - generate tainer_id by ip - ''' - ips = [] - for pod in podlist["items"]: - ips.append(pod["status"]["podIP"]) - ips.sort() - idMap = {} - for i in range(len(ips)): - idMap[ips[i]] = i - return idMap -``` - -在得到`idMap`后,通过函数`startPaddle(idMap, train_args_dict)`构造`paddle pserver`与`paddle train`的启动参数并执行进程。 - -在函数`startPaddle`中,最主要的工作就是解析出`paddle pserver`与`paddle train`的启动参数。例如`paddle train`参数的解析,解析环境变量得到`PADDLE_NIC`,`PADDLE_PORT`,`PADDLE_PORTS_NUM`等参数,然后通过自身的IP地址在`idMap`中获取`trainerId`。 - -```python - program = 'paddle train' - args = " --nics=" + PADDLE_NIC - args += " --port=" + str(PADDLE_PORT) - args += " --ports_num=" + str(PADDLE_PORTS_NUM) - args += " --comment=" + "paddle_process_by_paddle" - ip_string = "" - for ip in idMap.keys(): - ip_string += (ip + ",") - ip_string = ip_string.rstrip(",") - args += " --pservers=" + ip_string - args_ext = "" - for key, value in train_args_dict.items(): - args_ext += (' --' + key + '=' + value) - localIP = socket.gethostbyname(socket.gethostname()) - trainerId = idMap[localIP] - args += " " + args_ext + " --trainer_id=" + \ - str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT -``` - -使用 `docker build` 构建镜像: +因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能,所以我们可以在这个基础上,添加启动脚本,制作新镜像来完成以上的工作。参考镜像的[*Dockerfile*]((https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/k8s/src/k8s_train/Dockerfile)。 ```bash -docker build -t your_repo/paddle:mypaddle . +$ cd doc/howto/usage/k8s/src/k8s_train +$ docker build -t [YOUR_REPO]/paddle:mypaddle . ``` 然后将构建成功的镜像上传到镜像仓库。 ```bash -docker push your_repo/paddle:mypaddle +docker push [YOUR_REPO]/paddle:mypaddle ``` -注意上述命令中`your_repo`表示读者所使用的Docker镜像仓库地址,读者需要替换成自己使用的仓库地址。下文使用`your_repo/paddle:mypaddle`这个地址来表示此步骤所构建出的镜像。 +注意上述命令中`[YOUR_REPO]`表示读者所使用的Docker镜像仓库地址,读者需要替换成自己使用的仓库地址。下文使用`[YOUR_REPO]/paddle:mypaddle`这个地址来表示此步骤所构建出的镜像。 ### 上传训练文件 -本文使用PaddlePaddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容,我们将训练文件与数据放在一个job name命名的目录中,上传到MFS共享存储。完成后MFS上的文件内容大致如下: +本文使用PaddlePaddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容,我们将训练文件与数据放在一个job name命名的目录中,上传到volume所在的共享存储(使用不同分布式存储会有不同的挂载方式,需要要先挂载这个目录,然后拷贝数据)。完成后volume中的文件内容大致如下: ```bash [root@paddle-kubernetes-node0 mfs]# tree -d @@ -205,7 +87,7 @@ spec: path: /home/work/mfs containers: - name: trainer - image: your_repo/paddle:mypaddle + image: [YOUR_REPO]/paddle:mypaddle command: ["bin/bash", "-c", "/root/start.sh"] env: - name: JOB_NAME @@ -289,8 +171,8 @@ I1116 09:10:17.123121 50 Util.cpp:155] commandline: --ports_num=2 --comment=paddle_process_by_paddle --pservers=192.168.129.66,192.168.223.143,192.168.129.71 --ports_num_for_sparse=2 --config=./trainer_config.py - --trainer_count=4 --num_passes=10 --use_gpu=0 - --log_period=50 --dot_period=10 --saving_period=1 + --trainer_count=4 --num_passes=10 --use_gpu=0 + --log_period=50 --dot_period=10 --saving_period=1 --local=0 --trainer_id=0 --save_dir=/home/jobpath/paddle-cluster-job/output I1116 09:10:17.123440 50 Util.cpp:130] Calling runInitFunctions @@ -310,3 +192,90 @@ I1116 09:10:18.019492 50 ParameterClient2.cpp:122] pserver 3 192.168.223.143: I1116 09:10:18.019716 50 ParameterClient2.cpp:122] pserver 4 192.168.129.71:7164 I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7165 ``` + + +## 一些细节的补充 + +### 使用环境变量 + +使用容器方式运行训练任务的Kubernetes Job,通常会使用环境变量配置Job的配置信息`start_paddle.py`提供了一个启动脚本,将环境变量转换成paddle的命令行参数: +``` +API = "/api/v1/namespaces/" +JOBSELECTOR = "labelSelector=job-name=" +JOB_PATH = os.getenv("JOB_PATH") + "/" + os.getenv("JOB_NAME") +JOB_PATH_OUTPUT = JOB_PATH + "/output" +JOBNAME = os.getenv("JOB_NAME") +NAMESPACE = os.getenv("JOB_NAMESPACE") +PADDLE_NIC = os.getenv("CONF_PADDLE_NIC") +PADDLE_PORT = os.getenv("CONF_PADDLE_PORT") +PADDLE_PORTS_NUM = os.getenv("CONF_PADDLE_PORTS_NUM") +PADDLE_PORTS_NUM_SPARSE = os.getenv("CONF_PADDLE_PORTS_NUM_SPARSE") +PADDLE_SERVER_NUM = os.getenv("CONF_PADDLE_GRADIENT_NUM") +``` + +### Pod间通信 +`start_paddle.py`脚本开始时,会先进行参数的初始化与解析。 + +```python +parser = argparse.ArgumentParser(prog="start_paddle.py", + description='simple tool for k8s') + args, train_args_list = parser.parse_known_args() + train_args = refine_unknown_args(train_args_list) + train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) + podlist = getPodList() +``` + +然后通过函数`getPodList()`访问Kubernetes的接口来查询此job对应的所有pod信息。当所有pod都处于running状态(容器运行都运行)时,再通过函数`getIdMap(podlist)`获取trainer_id。 + +```python + podlist = getPodList() + # need to wait until all pods are running + while not isPodAllRunning(podlist): + time.sleep(10) + podlist = getPodList() + idMap = getIdMap(podlist) +``` +* *注意*: `getPodList()`会获取当前namespace下的所有pod,如果已经有pod运行,可能会导致出错。这种集群节点管理方式会在将来使用[statfulsets](https://kubernetes.io/docs/concepts/abstractions/controllers/statefulsets/)代替。 + +在函数`getIdMap(podlist)`内部,我们通过读取`podlist`中每个pod的IP地址,将IP排序生成的序号作为trainer_id。 + +```python +def getIdMap(podlist): + ''' + generate tainer_id by ip + ''' + ips = [] + for pod in podlist["items"]: + ips.append(pod["status"]["podIP"]) + ips.sort() + idMap = {} + for i in range(len(ips)): + idMap[ips[i]] = i + return idMap +``` + +在得到`idMap`后,通过函数`startPaddle(idMap, train_args_dict)`构造`paddle pserver`与`paddle train`的启动参数并执行进程。 + +### 启动任务 + +在函数`startPaddle`中,最主要的工作就是解析出`paddle pserver`与`paddle train`的启动参数。例如`paddle train`参数的解析,解析环境变量得到`PADDLE_NIC`,`PADDLE_PORT`,`PADDLE_PORTS_NUM`等参数,然后通过自身的IP地址在`idMap`中获取`trainerId`。 + +```python + program = 'paddle train' + args = " --nics=" + PADDLE_NIC + args += " --port=" + str(PADDLE_PORT) + args += " --ports_num=" + str(PADDLE_PORTS_NUM) + args += " --comment=" + "paddle_process_by_paddle" + ip_string = "" + for ip in idMap.keys(): + ip_string += (ip + ",") + ip_string = ip_string.rstrip(",") + args += " --pservers=" + ip_string + args_ext = "" + for key, value in train_args_dict.items(): + args_ext += (' --' + key + '=' + value) + localIP = socket.gethostbyname(socket.gethostname()) + trainerId = idMap[localIP] + args += " " + args_ext + " --trainer_id=" + \ + str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT +``` diff --git 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