From 0f7126f2cf939de7d30550a695aa279fad6d34f5 Mon Sep 17 00:00:00 2001 From: Yancey1989 Date: Fri, 3 Mar 2017 20:11:16 +0800 Subject: [PATCH] Add prepare training data and efk paddle logs --- doc/howto/usage/k8s/k8s_distributed_cn.md | 82 ++++++++++++++------ doc/howto/usage/k8s/src/efk-paddle-logs.png | Bin 0 -> 215352 bytes 2 files changed, 60 insertions(+), 22 deletions(-) create mode 100644 doc/howto/usage/k8s/src/efk-paddle-logs.png diff --git a/doc/howto/usage/k8s/k8s_distributed_cn.md b/doc/howto/usage/k8s/k8s_distributed_cn.md index 7213a977b8a..8ca158e608b 100644 --- a/doc/howto/usage/k8s/k8s_distributed_cn.md +++ b/doc/howto/usage/k8s/k8s_distributed_cn.md @@ -43,22 +43,55 @@ 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。 -### 上传训练文件 +### 准备训练数据 -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: +这里我们通过在Kubernetes集群上启动一个Job来下载并切割数据,也可以通过修改[k8s_train](./k8s_train)的内容来定制image. -```bash -[root@paddle-kubernetes-node0 mfs]# tree -d +在启动Job之前,需要根据不同的分布式存储来绑定一个[persistentVolumeClaim](https://kubernetes.io/docs/user-guide/persistent-volumes/),生成的数据将会存储在这个volume下. + +```yaml +apiVersion: batch/v1 +kind: Job +metadata: + name: paddle-data +spec: + template: + metadata: + name: pi + spec: + hostNetwork: true + containers: + - name: paddle-data + image: paddledev/paddle-tutorial:k8s_data + imagePullPolicy: Always + volumeMounts: + - mountPath: "/mnt" + name: nfs + env: + - name: OUT_DIR + value: /mnt/paddle-cluster-job + - name: SPLIT_COUNT + value: "3" + volumes: + - name: nfs + persistentVolumeClaim: + claimName: mfs + restartPolicy: Never +``` + +完成后volume中的文件内容大致如下: +```base +[root@paddle-kubernetes-node0 nfsdir]$ tree -d . -└── paddle-cluster-job - ├── data - │   ├── 0 - │   │ - │   ├── 1 - │   │ - │   └── 2 - ├── output - └── recommendation +`-- paddle-cluster-job + |-- 0 + | `-- data + |-- 1 + | `-- data + |-- 2 + | `-- data + |-- output + |-- quick_start ``` 目录中paddle-cluster-job是本次训练对应的job name,本次训练要求有3个PaddlePaddle节点,在paddle-cluster-job/data目录中存放切分好的数据,文件夹0,1,2分别代表3个节点的trainer_id。recommendation文件夹内存放训练文件,output文件夹存放训练结果与日志。 @@ -118,15 +151,16 @@ spec: `env`字段表示容器的环境变量,我们将`paddle`运行的一些参数通过这种方式传递到容器内。 -`JOB_PATH`表示共享存储挂载的路径,`JOB_NAME`表示job名字,`TRAIN_CONFIG_DIR`表示本次训练文件所在目录,这三个变量组合就可以找到本次训练需要的文件路径。 - -`CONF_PADDLE_NIC`表示`paddle pserver`进程需要的`--nics`参数,即网卡名 - -`CONF_PADDLE_PORT`表示`paddle pserver`的`--port`参数,`CONF_PADDLE_PORTS_NUM`则表示稠密更新的端口数量,也就是`--ports_num`参数。 - -`CONF_PADDLE_PORTS_NUM_SPARSE`表示稀疏更新的端口数量,也就是`--ports_num_for_sparse`参数。 - -`CONF_PADDLE_GRADIENT_NUM`表示训练节点数量,即`--num_gradient_servers`参数 +环境变量 | 说明 +--- | --- +JOB_PATH | 共享存储挂在的路径 +JOB_NAME | Job的名字 +TRAIN_CONFIG_DIR | 本次训练文件所在目录,与JOB_PATH,JOB_NAME组合可以找到本次训练需要的文件路径 +CONF_PADDLE_NIC | `paddle pserver`进程需要的`--nics`参数,即网卡名 +CONF_PADDLE_PORT | `paddle paserver`的`--port`参数 +CONF_PADDLE_PORTS_NUM | 稠密更新的端口数量,即`--ports_num`参数 +CONF_PADDLE_PORTS_NUM_SPARSE | 稀疏更新的端口数量,即`--ports_num_for_sparse`参数 +CONF_PADDLE_GRADIENT_NUM | 训练节点数量,即`--num_gradient_servers参数` 这些参数的具体描述,读者可以查看[这里](http://www.paddlepaddle.org/doc/ui/cmd_argument/detail_introduction.html#parameter-server-and-distributed-communication)。 @@ -279,3 +313,7 @@ def getIdMap(podlist): args += " " + args_ext + " --trainer_id=" + \ str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT ``` + +### 使用EFK查询训练日志 +首先需要在集群中[部署EFK(Elasticsearch+Fluentd+Kibana)](https://github.com/k8sp/efk)套件,在Kibana中根据POD或者Container名字来搜索相应日志 +![EFK下的Paddle训练日志](./src/efk-paddle-logs.png) diff --git a/doc/howto/usage/k8s/src/efk-paddle-logs.png b/doc/howto/usage/k8s/src/efk-paddle-logs.png new file mode 100644 index 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