Merge pull request #6674 from typhoonzero/refine_doc_structure
refine cluster doc structuredel_some_in_makelist
Tao Luo
8 years ago
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# 使用fabric启动集群训练
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## 准备一个Linux集群
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可以在`paddle/scripts/cluster_train_v2/fabric/docker_cluster`目录下，执行`kubectl -f ssh_servers.yaml`启动一个测试集群，并使用`kubectl get po -o wide`获得这些节点的IP地址。
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## 启动集群作业
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`paddle.py` 提供了自动化脚本来启动不同节点中的所有 PaddlePaddle 集群进程。默认情况下，所有命令行选项可以设置为 `paddle.py` 命令选项并且 `paddle.py` 将透明、自动地将这些选项应用到 PaddlePaddle 底层进程。
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`paddle.py` 为方便作业启动提供了两个独特的命令选项。
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- `job_dispatch_package` 设为本地 `workspace` 目录，它将被分发到 `conf.py` 中设置的所有节点。它有助于帮助频繁修改和访问工作区文件的用户减少负担，否则频繁的多节点工作空间部署可能会很麻烦。
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- `job_workspace` 设为已部署的工作空间目录，`paddle.py` 将跳过分发阶段直接启动所有节点的集群作业。它可以帮助减少分发延迟。
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`cluster_train/run.sh` 提供了命令样例来运行 `doc/howto/usage/cluster/src/word2vec` 集群任务，只需用您定义的目录修改 `job_dispatch_package` 和 `job_workspace`，然后：
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```
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sh run.sh
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```
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集群作业将会在几秒后启动。
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## 终止集群作业
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`paddle.py`能获取`Ctrl + C` SIGINT 信号来自动终止它启动的所有进程。只需中断 `paddle.py` 任务来终止集群作业。如果程序崩溃你也可以手动终止。
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## 检查集群训练结果
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详细信息请检查 $workspace/log 里的日志，每一个节点都有相同的日志结构。
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`paddle_trainer.INFO`
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提供几乎所有训练的内部输出日志，与本地训练相同。这里检验运行时间模型的收敛。
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`paddle_pserver2.INFO`
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提供 pserver 运行日志，有助于诊断分布式错误。
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`server.log`
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提供 parameter server 进程的 stderr 和 stdout。训练失败时可以检查错误日志。
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`train.log`
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提供训练过程的 stderr 和 stdout。训练失败时可以检查错误日志。
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## 检查模型输出
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运行完成后，模型文件将被写入节点 0 的 `output` 目录中。
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工作空间中的 `nodefile` 表示当前集群作业的节点 ID。
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# Cluster Training Using Fabric
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## Prepare a Linux cluster
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Run `kubectl -f ssh_servers.yaml` under the directory: `paddle/scripts/cluster_train_v2/fabric/docker_cluster` will launch a demo cluster. Run `kubectl get po -o wide` to get IP addresses of these nodes.
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## Launching Cluster Job
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`paddle.py` provides automatical scripts to start all PaddlePaddle cluster processes in different nodes. By default, all command line options can be set as `paddle.py` command options and `paddle.py` will transparently and automatically set these options to PaddlePaddle lower level processes.
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`paddle.py`provides two distinguished command option for easy job launching.
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- `job_dispatch_package` set it with local `workspace` directory, it will be dispatched to all nodes which is set in `conf.py`. It could be helpful for frequently manipulating workspace files. otherwise, frequent multi-nodes workspace deployment is very annoying.
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- `job_workspace` set it with already deployed workspace directory, `paddle.py` will skip dispatch stage to directly launch cluster job with all nodes. It could help to reduce heavy
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dispatch latency.
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`cluster_train/run.sh` provides command line sample to run `demo/recommendation` cluster job, just modify `job_dispatch_package` and `job_workspace` with your defined directory, then:
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```
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sh run.sh
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```
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The cluster Job will start in several seconds.
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## Kill Cluster Job
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`paddle.py` can capture `Ctrl + C` SIGINT signal to automatically kill all processes launched by it. So just stop `paddle.py` to kill cluster job. You should manually kill the job if the program crashed.
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## Check Cluster Training Result
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Check log in $workspace/log for details, each node owns same log structure.
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`paddle_trainer.INFO`
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It provides almost all internal output log for training, same as local training. Check runtime model convergence here.
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`paddle_pserver2.INFO`
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It provides parameter server running log, which could help to diagnose distributed error.
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`server.log`
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It provides stderr and stdout of parameter server process. Check error log if training crashes.
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`train.log`
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It provides stderr and stdout of trainer process. Check error log if training crashes.
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## Check Model Output
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After one pass finished, model files will be written in `output` directory in node 0.
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`nodefile` in workspace indicates the node id of current cluster job.
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k8s_aws_en.md
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# Cluster Training Using OpenMPI
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## Prepare an OpenMPI cluster
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Run the following command to start a 3-node MPI cluster and one "head" node.
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```bash
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cd paddle/scripts/cluster_train_v2/openmpi/docker_cluster
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kubectl create -f head.yaml
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kubectl create -f mpi-nodes.yaml
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```
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Then you can log in to every OpenMPI node using ssh without input any passwords.
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## Launching Cluster Job
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Follow the steps to launch a PaddlePaddle training job in OpenMPI cluster:\
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```bash
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# find out node IP addresses
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kubectl get po -o wide
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# generate a "machines" file containing node IP addresses
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kubectl get po -o wide | grep nodes | awk '{print $6}' > machines
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# copy necessary files onto "head" node
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scp -i ssh/id_rsa.mpi.pub machines prepare.py train.py start_mpi_train.sh tutorial@[headIP]:~
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# login to head node using ssh
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ssh -i ssh/id_rsa.mpi.pub tutorial@[headIP]
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# --------------- in head node ---------------
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# prepare training data
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python prepare.py
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# copy training data and dict file to MPI nodes
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cat machines | xargs -i scp word_dict.pickle train.py start_mpi_train.sh machines {}:/home/tutorial
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# creat a directory for storing log files
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mpirun -hostfile machines -n 3 mkdir /home/tutorial/logs
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# copy training data to every node
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scp train.txt-00000 test.txt-00000 [node1IP]:/home/tutorial
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scp train.txt-00001 test.txt-00001 [node2IP]:/home/tutorial
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scp train.txt-00002 test.txt-00002 [node3IP]:/home/tutorial
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# start the job
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mpirun -hostfile machines -n 3 /home/tutorial/start_mpi_train.sh
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```
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