From 29b00fadf15a9091bf943cb8a556bb185e14d2f4 Mon Sep 17 00:00:00 2001 From: "yi.wu" Date: Thu, 23 Mar 2017 11:32:44 +0800 Subject: [PATCH] cluster design and thoughts --- doc/design/cluster_design.md | 85 +++++++++++++++++++++++-- doc/design/images/arch.png | Bin 0 -> 165032 bytes doc/design/images/less_trainer.png | Bin 0 -> 61124 bytes doc/design/images/more_trainer.png | Bin 0 -> 32517 bytes doc/design/images/replica.png | Bin 0 -> 59597 bytes doc/design/images/trainer.graffle | Bin 0 -> 4895 bytes doc/design/images/trainer.png | Bin 0 -> 119703 bytes doc/design/images/trainer_data.png | Bin 0 -> 138135 bytes doc/design/images/two_phase_commit.png | Bin 0 -> 49120 bytes 9 files changed, 79 insertions(+), 6 deletions(-) create mode 100644 doc/design/images/arch.png create mode 100644 doc/design/images/less_trainer.png create mode 100644 doc/design/images/more_trainer.png create mode 100644 doc/design/images/replica.png create mode 100644 doc/design/images/trainer.graffle create mode 100644 doc/design/images/trainer.png create mode 100644 doc/design/images/trainer_data.png create mode 100644 doc/design/images/two_phase_commit.png diff --git a/doc/design/cluster_design.md b/doc/design/cluster_design.md index fe92ecfb9d2..fbf25a73f9d 100644 --- a/doc/design/cluster_design.md +++ b/doc/design/cluster_design.md @@ -1,12 +1,13 @@ # Paddle大规模分布式训练设计 ## 概览 +参考[这里](https://github.com/PaddlePaddle/Paddle/pull/1620/files) -## 常见的分布式训练架构 +## 分布式训练架构 -深度学习分布式训练的架构如图: +常见的深度学习分布式训练的架构如图: - + 为了完成一个深度学习的训练任务,集群中会运行多个trainer和parameter server,集群会把模型的参 数分布式的存储在多个parameter server上,trainer完成每个mini-batch数据训练之后会把梯度发送 @@ -27,7 +28,7 @@ 为了实现parameter server集群可以容忍单点故障,必须将每个模型参数的分片在集群中存储多个副本。虽然 也可以考虑使用校验和的技术减少副本大小,但为了整体系统的简单可靠,优先选择使用副本的方式。 - + 上图显示了在2台parameter server中实现每个模型参数的分片均保存两个副本的状态。parameter 负责存储 所有参数分片副本并在etcd中同步每个副本的状态。每个分片的多个副本中同时只有一个处于"master"状态, @@ -79,10 +80,82 @@ etcd中数据存储格式为: ``` ## 数据一致性 -存在多个副本数据的情况下就需要考虑,多个副本之间的数据一致性。如果使用数据强一致性,则在故障恢复时 -可以获得一个完整的数据集,但每次更新模型参数的性能会下降,因为需要保证多个副本都完全更新之后才算更新 +存在多个副本数据的情况下就需要考虑,多个副本之间的数据一致性。如果使用数据强一致性(例如paxos/raft或两段式提交), +则在故障恢复时可以获得一个完整的数据集,但每次更新模型参数的性能会下降,因为需要保证多个副本都完全更新之后才算更新 成功。如果使用异步同步(最终一致性),则在重新选举"master"副本时,可能得到的副本并没有完成数据同步。 +本文档讨论使用两阶段提交实现模型副本数据的更新。 +* 每个副本通常由多个parameter block组成,多个block之间可以并发更新,但更新同一个block需要保证顺序性。 +* 每次需要更新一个block的时候,trainer首先向存放"master"副本的服务器提交“准备更新”请求,"master"副本检查其他副本的状态并创建一个更新事务,然后返回OK。 +* trainer再向"master"发送变化部分的梯度数据和这份数据的id,然后"master"并发的更新本地和其他副本的模型数据,更新成功返回OK,如果有更新失败的节点,则执行"rollback",退回到更新前状态并返回错误代码。 + + + +## 模型数据检查点(Checkpointing) +模型数据检查点,可以在磁盘上保存一份存储在parameter server内存中的模型数据的完整镜像。在一个不可中断并缺少备份的训练任务中,可以通过阶段性的在每个parameter server的本地磁盘保存检查点快照达到容灾的目的,比如每个pass保存一次快照。在出现单点故障时,只需要恢复这台节点,或者将这台节点迁移到另一个节点并启动即可恢复训练任务。 + +## 训练数据的存储和分发 +生产环境中的训练数据集通常体积很大,并被存储在诸如Hadoop HDFS, Ceph, AWS S3之类的分布式存储之上。这些分布式存储服务通常会把数据切割成多个分片分布式的存储在多个节点之上,而多个trainer通常也需要预先完成文件的切割。但通常的方法是从HDFS上将数据拷贝到训练集群,然后切割到多个trainer服务器上,如图(Mount/Copy): + + + +考虑到HDFS实际上已经完成了数据切割的任务,而且如果存在前置的数据预处理任务(Map-Reduce或Spark SQL),这些任务的输出也都存放于HDFS之上,则trainer可以直接调用HDFS LowLevel API,从元数据节点获得每个数据分片存储的位置,直接获得分片。 + +***注:每个数据分片保存多个mini_batch*** + +进一步优化,trainer可以寻找在物理网络拓扑中离自己最近的一个分片副本获取数据。 + +trainer和训练数据分片的均衡: +* 当trainer >= 数据分片: + trainer个数和数据分片个数相同时,可以获得最高的吞吐量。当trainer个数再大于分片数量时,必然有Trainer获取不到数据分片,处于等待状态。但对整体任务运行没有影响,等待的trainer也会消耗很小的资源。 + + + +* 当trainer < 数据分片 + 每个trainer负责多个数据分片,轮询方式完成一个分片训练之后开始下一个分片。 + + + ## 故障恢复 +在通用集群上运行的应用和任务,通常需要有能够自动伸缩的能力,这样在在线集群进行扩容时,可以适当的减小训练任务的资源(进程数/并发数),而不需要直接停止训练任务,修改参数后重新提交任务。 + +然而对于常见的在线服务(比如Web服务,RPC服务等),是可以“无状态”伸缩的,即扩容和缩容只需要增删对应的节点,集群能力就可以自动伸缩,Web服务的每个节点不会维护自身的状态变化和自身的数据,这些数据通常会借由外部的存储或服务完成,如MySQL,Redis等。而对于训练任务来说,每个parameter server都需要保存状态(mini-batch id)和数据(parameters),在增删节点的时候都会涉及到数据重新分布(re-sharding)和处理数据同步的问题。 + +用户只要根据实际训练任务场景,配置parameter server和trainer的初始节点个数,最大节点个数和最小节点个数,模型副本个数,是否开启检查点等配置项,即可配置并启动一个可以容灾的训练集群。具体的过程如下: + +1. 配置parameter server和trainer的初始节点个数、最大节点个数、最小节点个数、模型副本个数、是否开启检查点等配置以及训练任务相关配置。 +1. 启动parameter server和trainer,每个实例会在etcd中注册一个临时节点。这样当某个parameter server或trainer失效是,etcd中的节点会反应这个示例的状态。每个parameter server在所有的parameter server上会使用etcd watcher监听节点的变化状态,已完成后续处理。 +1. parameter server如果开启了检查点,则先判断是否已经存在本地检查点快照数据,如果有,则从快照数据中加载状态和数据,并开始提供服务。如果没有则执行初始化启动步骤。 +1. 提交用户定义的深度学习网络(topology),并根据网络中参数完成pre-sharding,将参数block哈希到512或1024个slot中,每个slot即为一个参数分片。根据实际存在的parameter server个数,将slot和parameter server完成对应的映射,使slot可以平均存储在这些parameter server上。 +1. parameter server开始监听端口并接收数据。每次接收到数据,都使用两段式提交方式同步到所有的副本。如果需要存储检查点,则在同步所有副本之后,保存检查点。 +1. 当故障发生后,parameter server会收到etcd发送的watcher信号,此时将暂停trainer的训练(此时要检查最后一次更新的mini_batch id,如果处于不同步状态,需要执行rollback),执行re-sharding步骤: + 1. 根据现有存活的parameter server的个数,找出丢失master分片的参数slot,重新标记成为master,然后确保集群中一个分片只选择出一个master。 + 2. 重新分布每个slot,将slot平均分布在所有parameter server上,保证负载均衡。 + 3. 重新开始trainer的训练。 + +新增节点的方法类似,此处不再赘述。 ## 动态扩容/缩容 +虽然故障恢复可以提供任意时刻的节点新增和删除仍然可以保证任务正常运行,但通常这样是比较暴力的。为了能graceful的关闭多个节点,parameter server需要提供对应的API接口: + +```python + def resize(n): + # do resize + return success +``` + +接口完成先发送信号暂停训练任务,re-shard数据分片,然后重新开启训练。这样可以避免程序bug导致的数据不同步问题出现。 + +## 性能考虑 +由于每次数据提交都需要完成分片同步,而且在每个pass之后执行检查点保存,必然会带来parameter server性能下降。可以根据不同的场景配置不同的容灾方案。 + +* 测试任务/极短训练任务:如果训练任务在几十分钟或小时级别可以运行完成,可以考虑不开启副本也不开启检查点。 +* 短期训练任务/测试任务:训练任务运行时间如果在数小时或数天范围,可以考虑只使用一个副本(每个slot只保存一份),并开启检查点。在这个时长内出现不可恢复的硬件故障的概率极低。 +* 大型训练任务:训练时间以周或月为单位。建议开启多个副本和检查点。这样可以在任意一个pass停止任务,并重新从这个pass开始训练。或者在通用集群运行时,可以考虑动态扩容和缩容。 + +## 实现考虑 +由于两阶段提交和数据备份同步、选举部分实现比较复杂,可以考虑使用一些开源库函数,比如2pc,raft库等,后期在优化过程中逐步替换。 + + +## TODO: +All-Reduce和Ring的不同设计考虑 diff --git a/doc/design/images/arch.png b/doc/design/images/arch.png new file mode 100644 index 0000000000000000000000000000000000000000..659e340388f63f689f2a0112957f8c9dacf05b20 GIT binary patch literal 165032 zcmeFZbyQVr*ES9aN{Ao`C{l_d4bmM-NjK6W-BMDLii!#d(%s!94Hifv4blkGAgLhm zn_KthocDX)-{0T(#(2&+$0M7y*1luLHLrQyD^OWc8XuP&7Yz*!|Bj4=DjM49CunF` zuGkpx$zDNh8vGC4Nmcq5T45K(BK&~!Kt|gM4eiPR;y-lE`0@m}V8BxSuJc_5c>!a4 zTUH|z`}?M>Znh8LYBV$(EWcQj|a 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