From 39d6d1c2e9fa3ba03eb32ac2bd246f50c78408cf Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Wed, 19 Apr 2017 19:17:47 +0800 Subject: [PATCH] update cluster design --- doc/design/cluster_design.md | 146 ------------------ doc/design/{dist => cluster_train}/README.md | 21 ++- doc/design/cluster_train/checkpointing.md | 76 +++++++++ doc/design/cluster_train/data_dispatch.md | 67 ++++++++ .../src}/checkpointing.png | Bin .../cluster_train/src/data_dispatch.png | Bin 0 -> 33872 bytes .../src/paddle-etcd.graffle | Bin .../src/paddle-etcd.png | Bin .../src/paddle-model-sharding.graffle | Bin .../src/paddle-model-sharding.png | Bin .../src/paddle-ps-0.png | Bin .../src/paddle-ps-1.png | Bin .../src/paddle-ps.graffle | Bin .../src/paddle-task-queues.graffle | Bin .../src/paddle-task-queues.png | Bin .../src/paddle-task-states.graffle | Bin .../src/paddle-task-states.png | Bin doc/design/cluster_train/src/trainer.graffle | Bin 0 -> 5644 bytes .../{images => cluster_train/src}/trainer.png | Bin doc/design/images/master.png | Bin 166142 -> 0 bytes doc/design/images/trainer.graffle | Bin 8566 -> 0 bytes doc/design/images/trainer_data.png | Bin 138135 -> 0 bytes 22 files changed, 157 insertions(+), 153 deletions(-) delete mode 100644 doc/design/cluster_design.md rename doc/design/{dist => cluster_train}/README.md (95%) create mode 100644 doc/design/cluster_train/checkpointing.md create mode 100644 doc/design/cluster_train/data_dispatch.md rename doc/design/{images => cluster_train/src}/checkpointing.png (100%) create mode 100644 doc/design/cluster_train/src/data_dispatch.png rename doc/design/{dist => cluster_train}/src/paddle-etcd.graffle (100%) rename doc/design/{dist => cluster_train}/src/paddle-etcd.png (100%) rename doc/design/{dist => cluster_train}/src/paddle-model-sharding.graffle (100%) rename doc/design/{dist => cluster_train}/src/paddle-model-sharding.png (100%) rename doc/design/{dist => cluster_train}/src/paddle-ps-0.png (100%) rename doc/design/{dist => cluster_train}/src/paddle-ps-1.png (100%) rename doc/design/{dist => cluster_train}/src/paddle-ps.graffle (100%) rename doc/design/{dist => cluster_train}/src/paddle-task-queues.graffle (100%) rename doc/design/{dist => cluster_train}/src/paddle-task-queues.png (100%) rename doc/design/{dist => cluster_train}/src/paddle-task-states.graffle (100%) rename doc/design/{dist => cluster_train}/src/paddle-task-states.png (100%) create mode 100644 doc/design/cluster_train/src/trainer.graffle rename doc/design/{images => cluster_train/src}/trainer.png (100%) delete mode 100644 doc/design/images/master.png delete mode 100644 doc/design/images/trainer.graffle delete mode 100644 doc/design/images/trainer_data.png diff --git a/doc/design/cluster_design.md b/doc/design/cluster_design.md deleted file mode 100644 index e20a44911..000000000 --- a/doc/design/cluster_design.md +++ /dev/null @@ -1,146 +0,0 @@ -# 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) - -## 分布式训练架构 - -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,每个trainer启动时,会先尝试从parameter server集群下载最新的参数,然后以mini-batch为单位读取训练数据集中的一部分数据(Data shard)。trainer会在训练过程中持续与parameter server通讯,上传计算出来的梯度以及下载最新的模型。 - -每个parameter server保存所有parameter的一个分片(Global model shard),并负责接受所有trainer发送的梯度,完成SGD和优化算法,然后发送更新后的parameter到每个trainer。 - -这样,通过trainer和parameter server的分布式协作,可以完成神经网络的SGD方法的训练。Paddle可以同时支持同步SGD(synchronize SGD)和异步SGD(asynchronize SGD)。 - -在使用同步SGD训练神经网络时,Paddle使用同步屏障(barrier),使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中,则并不会等待所有trainer提交梯度才更新参数,这样极大的提高了计算的并行性:parameter server之间不相互依赖,并行的接收梯度和更新参数,parameter server也不会等待trainer全部都提交梯度之后才开始下一步,trainer之间也不会相互依赖,并行的执行模型的训练。可以看出,虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新,在任意时间某一台parameter server上保存的参数可能比另一台要更新,与同步SGD相比,梯度会有噪声。 - -在上面的分布式计算模型中,使用异步SGD比同步SGD可以一定程度的提供训练任务的容灾性。假设在某一时刻,一个trainer进程停止工作,其他的trainer仍然可以完成对部分数据的训练。 - -参考上面所描述的Paddle实现细节,可以进一步的优化以下方面: -1. 目前模型的参数是保存在parameter server进程的内存中的。在同步SGD或异步SGD训练过程中任意一台parameter server不能异常退出,否则参数丢失,训练不能继续执行。需要考虑每个模型分片(model shard)保存多个副本(replica)防止parameter server单点故障。 -1. 不能在一个训练任务中动态的增加或减少Trainer个数或parameter个数(异步SGD是否可以增加Trainer?) -1. 在同步SGD训练过程中,需要保证参数更新满足事务性操作。即可能在更新参数过程中,存放这个参数的shard所在的服务器故障,就需要rollback并重新更新这个参数shard的其他存活副本。 -1. 为了支持大量的训练任务和使用模型的应用在一个集群上,需要支持训练任务节点的伸缩。 -1. 支持训练任务的前置任务和后置任务,支持训练任务的定时调度和对在线流式数据的处理 - -## 模型参数检查点(Checkpointing) -模型数据检查点的实现,可以有效的避免parameter server的单点或多点同时故障。模型参数检查点通过定期向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,来保证训练过程可以从中间状态重新启动。在一个不可中断并缺少备份的训练任务中,可以通过阶段性的保存每个parameter server的数据快照(snapshot)到 ***分布式存储服务/分布式存储挂载点*** 达到容灾的目的,比如每隔10分钟或1小时保存最新的快照,并删除更早的快照。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。 - - - -### 快照保存的设计如下: - -前置要求: -* 所有parameter server在etcd上注册自己的id节点为TTL节点`/paddle/pservers/[id]`,并保持心跳。同时使用watcher监听`/paddle/pservers`目录,监听parameter server增加或丢失的消息。 -* 所有trainers在etcd `/paddle/trainers/[id]` 下注册节点。并监听暂停信号:`/paddle/trainers/pause`(监听节点创建和删除),`re-fetch` 信号。trainer在收到pause创建的信号之后,需要保存trainer的reader所读取的文件信息(文件名/文件元数据),和读取的offset到:`/paddle/trainers/[id]`的内容中。 - -程序流程: -1. 满足条件""每个pass或每n个mini-batch"时,parameter server原子写入`/paddle/trainers/pause`暂停所有trainer上传新的梯度 -2. parameter server在etcd服务中创建`/paddle/checkpoints/[snapshot uuid]/[parameter server id]`TTL节点,标识快照开始更新。然后开始向磁盘/存储服务中一个新的文件写入快照数据,并在写入过程中定时更新 etcd的checkpoint TTL节点已保证心跳。 -3. 任意一个parameter server完成检查点更新后,创建etcd目录`/paddle/checkpoints/[snapshot uuid]/finished/[parameter server id]`,写入完成的timestamp。然后检查是否所有的parameter server都完成。如果是,跳到第5步;否则循环等待。 -4. 如果在任意时间点,收到parameter server增加或丢失的消息,则需要回滚整个集群训练过程到上一个检查点: - - * 如果没有处在暂停状态,则暂停所有的参数更新 - * 删除etcd中`/paddle/checkpoints/[snapshot uuid]`的路径,清理没有成功执行的检查点任务。 - * 从etcd中读取检查点的uuid和timestamp,然后解析所有存储在磁盘上的检查点文件(可能有多个文件),判断对应uuid是否相同,如果都不同,则报错退出(FATAL error)。如果有相同的文件,则加载这个检查点文件,并覆盖内存中的参数。 - * 原子性创建etcd节点:`/paddle/trainer/re-fetch` (即多个parameter server不重复创建),通知trainer重新获取参数 - * 删除`/paddle/trainers/pause` 节点,重新开启训练过程,trainer需要从`/paddle/checkpoints/latest`中找到上一个检查点的file和对应的offset,并将reader重新设置到这个位置。 - -5. 尝试获取`/paddle/checkpoints/finish_lock`分布式锁(使用etcd3或者客户端wrapper)。获取锁之后,更新 `/paddle/checkpoints/latest`的内容为最新的checkpoint的uuid,timestamp;从`/paddle/trainers/[id]`中获取file和offset并更新到`/paddle/checkpoints/latest/files/[id]`中;删除每个pserver的上一个snapshot文件;释放锁;删除`/paddle/trainers/pause`节点。 - -这里需要用户额外注意,在您的实际环境中,训练任务的运行可能会占满trainer和parameter server之间的网络带宽,如果parameter server此时还需要通过网络访问分布式存储以保存快照,可能会造成网络拥塞,而出现阶段性的运行停滞。 - -### ETCD文件一览 -***注:TTL节点表示这个节点在创建者消失时,在TTL时间内也会消失*** - -* `/paddle/pservers/[id]`: TTL节点。id是parameter server的id,保存parameter server的信息。 -* `/paddle/checkpoints/latest`: 最新的checkpoint的信息。json格式保存timestamp, uuid -* `/paddle/checkpoints/latest/files/[trainer id]`: 保存最新的checkpoint对应的每个trainer读取数据的文件和offset -* `/paddle/checkpoints/[snapshot uuid]/[parameter server id]`: TTL节点。uuid是checkpoint生成的唯一snapshot id -* `/paddle/checkpoints/[snapshot uuid]/finished/[parameter server id]`: 同上 -* `/paddle/trainers/[id]`: TTL节点,保存trainer信息。如果发生全局暂停,则节点中以json格式保存trainer正在读取的文件和offset -* `/paddle/trainers/pause`: 控制trainer暂停上传梯度 -* `/paddle/trainers/re-fetch`: 控制trainer重新从parameter server读取参数并覆盖本地参数 - -## 训练数据的存储和分发 - -### 现在的方法 -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,但这样的效率是底下的。如图(Mount/Copy): - - - -### 期望的方法 - -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*** - -我们将使用如下的设计完成数据分发: - - - -如图,数据存储在分布式文件系统中,并将预处理之后的文件切割成3个block存储在不同的机器上。在训练任务开始时,master读取这个分布式文件的元数据,并将一个block分配给一个trainer,然后将分配信息写入etcd中。随后trainer从etcd中获取到数据的分配信息并开始执行训练。一个block数据训练完成后,master负责在将新的block分配给一个trainer(图中虚线所示)。 - -master不会直接发送数据给Trainer而是负责协调训练数据的分配,并以ETCD为协调中心。所以master是一个无状态程序,任务运行过程中,master停止后只需要重新启动即可。 - -## 第一版**不需要**支持的特性 -### 推测执行/加速执行(TODO) -在异构集群中,如果存在某些trainer执行速度过慢会影响整体集群的速度(如图中Trainer 1),此时master将负责启动一个新的Trainer(Accelerate Trainer 2),使用同样的训练数据block。哪个trainer先完成block的训练,则把另一个慢速的kill掉。 - -### 关于存储的考虑 -* 图像/音频类数据,预处理之后以何种方式分布式存储,如何切割? -* 支持流式数据接口和常规文件接口 -* 对不同的分布式存储,需要实现不同的reader wrapper - -### 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,master需要提供对应的API接口: - -```python - def resize(n): - save_checkpoint() - pause_all_trainers() - start_and_wait_trainers(n - self.num_trainers) - start_and_wait_pservers(n - self.num_pservers) - do_parameter_re_hash() - trainers_re_fetch() - start_all_trainers() - return success -``` - -要实现`do_parameter_re_hash()`,将现有的parameter能够在增加parameter servers时,完成重新分布,需要实现以下的细节: - -``` -parameters = large vector -<..............................> -|___| |___| |___| - ^ - | -parameter block -需要: - hash to map to -parameter block --------> 128~1024 slots --------> parameter servers -``` - -接口完成先发送信号暂停训练任务,保存参数的checkpoint,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。 - -## 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。 - -## 附录 -### 引用 - -* [Large Scale Distributed Deep Networks](http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks.pdf), Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Andrew Y. Ng - -### 术语 -* model: 指深度学习训练之后得到的所有参数,使用这个神经网络可以完成对新数据的预测 -* parameters: 神经网络中的参数,包括权重w和偏置b。一个神经网络的模型由大量的参数组成 -* shard: 分片,通常指将一个整体拆分成多份的其中的一份。 -* model shard: 将一个神经网络参数拆分成多份,每个shard分别存储在其中一台parameter server之上 -* parameter block: 多个parameter block构成一个model shard -* 单点故障: 任意时刻只可能同时有一台服务器故障。由于集群中同时存在两台机器故障的概率极低((平均故障率*平均故障修复时间)^2)只对特殊在线系统考虑两台以上同时故障的容灾。 - -### TODO: -All-Reduce和Ring的不同设计考虑 diff --git a/doc/design/dist/README.md b/doc/design/cluster_train/README.md similarity index 95% rename from doc/design/dist/README.md rename to doc/design/cluster_train/README.md index 1788208bc..6a94a8282 100644 --- a/doc/design/dist/README.md +++ b/doc/design/cluster_train/README.md @@ -31,7 +31,7 @@ The master process will: - Keep track of training progress on the dataset with [task queue](#task-queue). A training job will iterate on the dataset for a full pass until it goes into next pass. -#### Task +#### Task A task is a data shard to be trained. The total number of tasks will be much bigger than the total number of trainers. The number of data instances inside a task will be much bigger than the mini-batch size. @@ -78,7 +78,7 @@ The communication pattern between the trainers and the parameter servers depends - Synchronous Stochastic Gradient Descent (sync-SGD) Parameter server will wait for all trainer finish n-th mini-batch calculation and send their gradients before broadcasting new parameters to every trainer. Every trainer will wait for the new parameters before starting n+1-th mini-batch. - + - Asynchronous Stochastic Gradient Descent (async-SGD) There will no synchronization between different trainers, and parameter server updates its parameter as soon as it receives new gradient: @@ -118,8 +118,6 @@ When the master is started by the Kubernetes, it executes the following steps at 1. Watches the trainer prefix keys `/trainer/` on etcd to find the live trainers. 1. Starts dispatching the tasks to the trainers, and updates task queue using an etcd transaction to ensure lock is held during the update. -The master process will kill itself if its etcd lease expires. - When the master process is dead for any reason, Kubernetes will restart it. It will be online again with all states recovered from etcd in few minutes. ### Trainer Process @@ -132,6 +130,8 @@ When the trainer is started by the Kubernetes, it executes the following steps a If trainer's etcd lease expires, it will try set key `/trainer/` again so that the master process can discover the trainer again. +Whenever a trainer fails, the master process is responsible to schedule the failed task back to "todo queue". then kubernetes will try to start the trainer somewhere else, then the recovered trainer will try to fetch new task to continue the training. + ### Parameter Server Process When the parameter server is started by Kubernetes, it executes the following steps at startup: @@ -140,11 +140,11 @@ When the parameter server is started by Kubernetes, it executes the following st 1. Search through etcd keys `/ps/` (`/ps/0`, `/ps/1`, ...) to find the first non-existant key whose index is smaller than the total number of parameter servers. Set the key using a transaction to avoid concurrent writes. The parameter server's index is inferred from the key name. The desired number of parameter servers is 3: - + - + The third parameter server joined: - + 1. The parameter server can load parameters if there are already saved parameters in the save path (inferred from its index). @@ -153,6 +153,13 @@ When the parameter server is started by Kubernetes, it executes the following st If the parameter server's etcd lease expires, the parameter server will kill itself. +## Parameter Server Checkpointing +See [here](./checkpointing.md) + +## Store and dispatching trainning data +See [here](./data_dispatch.md) + + ## Dynamic Scaling ### Trainer Scaling diff --git a/doc/design/cluster_train/checkpointing.md b/doc/design/cluster_train/checkpointing.md new file mode 100644 index 000000000..df2dc81c8 --- /dev/null +++ b/doc/design/cluster_train/checkpointing.md @@ -0,0 +1,76 @@ +# Paddle大规模分布式训练设计 + +## 概览 +参考[这里](./README.md) + +## 分布式训练架构 + +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,每个trainer启动时,会先尝试从parameter server集群下载最新的参数,然后以mini-batch为单位读取训练数据集中的一部分数据(Data shard)。trainer会在训练过程中持续与parameter server通讯,上传计算出来的梯度以及下载最新的模型。 + +每个parameter server保存所有parameter的一个分片(Global model shard),并负责接受所有trainer发送的梯度,完成SGD和优化算法,然后发送更新后的parameter到每个trainer。 + +这样,通过trainer和parameter server的分布式协作,可以完成神经网络的SGD方法的训练。Paddle可以同时支持同步SGD(synchronize SGD)和异步SGD(asynchronize SGD)。 + +在使用同步SGD训练神经网络时,Paddle使用同步屏障(barrier),使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中,则并不会等待所有trainer提交梯度才更新参数,这样极大的提高了计算的并行性:parameter server之间不相互依赖,并行的接收梯度和更新参数,parameter server也不会等待trainer全部都提交梯度之后才开始下一步,trainer之间也不会相互依赖,并行的执行模型的训练。可以看出,虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新,在任意时间某一台parameter server上保存的参数可能比另一台要更新,与同步SGD相比,梯度会有噪声。 + +在上面的分布式计算模型中,使用异步SGD比同步SGD可以一定程度的提供训练任务的容灾性。假设在某一时刻,一个trainer进程停止工作,其他的trainer仍然可以完成对部分数据的训练。 + +参考上面所描述的Paddle实现细节,可以进一步的优化以下方面: +1. 目前模型的参数是保存在parameter server进程的内存中的。在同步SGD或异步SGD训练过程中任意一台parameter server不能异常退出,否则参数丢失,训练不能继续执行。需要考虑每个模型分片(model shard)保存多个副本(replica)防止parameter server单点故障。 +1. 不能在一个训练任务中动态的增加或减少Trainer个数或parameter个数(异步SGD是否可以增加Trainer?) +1. 在同步SGD训练过程中,需要保证参数更新满足事务性操作。即可能在更新参数过程中,存放这个参数的shard所在的服务器故障,就需要rollback并重新更新这个参数shard的其他存活副本。 +1. 为了支持大量的训练任务和使用模型的应用在一个集群上,需要支持训练任务节点的伸缩。 +1. 支持训练任务的前置任务和后置任务,支持训练任务的定时调度和对在线流式数据的处理 + +## 模型参数检查点(Checkpointing) +模型数据检查点的实现,可以有效的避免parameter server的单点或多点同时故障。模型参数检查点通过定期向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,来保证训练过程可以从中间状态重新启动。在一个不可中断并缺少备份的训练任务中,可以通过阶段性的保存每个parameter server的数据快照(snapshot)到 ***分布式存储服务/分布式存储挂载点*** 达到容灾的目的,比如每隔10分钟或1小时保存最新的快照,并删除更早的快照。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。 + + + +### 快照保存的设计如下: + +说明: + +* parameter server在集群中启动后,自动挂载分布式存储目录,并把快照保存到这个目录下。 +* 所有parameter server和trainer在etcd上注册自己的id节点为TTL节点`/ps/[id]`和`/trainer/[id]`,并保持心跳。 +* ***注:trainer在故障恢复后,master会将失败的task重新分配给恢复的trainer执行。这样会引入更大的随机性。*** +* ***注:parameter server在保存检查点时,利用了Linux内核的“写时复制”技术,在fork的进程中保存检查点,原进程可以继续接收trainer的梯度更新请求,而不影响检查点数据的保存。*** +* ***注:每个parameter server的检查点各自独立保存,暂时不考虑多个parameter server同步的保存一个特定时间点的全局检查点,同样会引入随机性。*** + + +检查点保存程序流程: + +1. 如果满足条件""每个pass或每n个mini-batch"时,parameter server会`fork`自己,子进程中执行保存检查点任务,父进程继续工作。如果已经有子进程在进行保存检查点工作,则忽略。 +2. parameter server生成一个UUID,向指定的目录中一个新的文件(文件名为此UUID)写入快照数据。在快照写入完成后,计算这个文件的MD5 sum。然后在etcd的`/checkpoints/[pserver_id]`中写入json内容:`{"uuid": [UUID], "md5", "MD5 sum", "timestamp": xxxx}`。 +3. 删除磁盘目录中不是当前uuid的快照文件。 +4. 关闭fork出来的进程。 + +这里需要用户额外注意,在您的实际环境中,训练任务的运行可能会占满trainer和parameter server之间的网络带宽,如果parameter server此时还需要通过网络访问分布式存储以保存快照,可能会造成网络拥塞,而出现阶段性的运行停滞。 + +### 从快照恢复 + +在parameter server第一次启动或任意时间parameter server故障后被Kubernetes重新启动,则需要回滚到上一个检查点: + + 1. 从etcd中读取节点:`/checkpoints/[pserver_id]`获取最新的检查点的文件uuid + 1. 从磁盘文件中加载uuid文件名的检查点快照文件,并加载其中的参数 + 1. 如果上面两步出现错误,则使用启动参数定义的初始化方法初始化参数 + 1. 开始提供服务 + +## TODO List +### 推测执行/加速执行(TODO) +在异构集群中,如果存在某些trainer执行速度过慢会影响整体集群的速度(如图中Trainer 1),此时master将负责启动一个新的Trainer(Accelerate Trainer 2),使用同样的训练数据block。哪个trainer先完成block的训练,则把另一个慢速的kill掉。 + +### 动态扩容/缩容 +目前只考虑动态扩容trainer数量,可以减小系统复杂性。 + +## 术语 +* model: 指深度学习训练之后得到的所有参数,使用这个神经网络可以完成对新数据的预测 +* parameters: 神经网络中的参数,包括权重w和偏置b。一个神经网络的模型由大量的参数组成 +* shard: 分片,通常指将一个整体拆分成多份的其中的一份。 +* model shard: 将一个神经网络参数拆分成多份,每个shard分别存储在其中一台parameter server之上 +* parameter block: 多个parameter block构成一个model shard +* 单点故障: 任意时刻只可能同时有一台服务器故障。由于集群中同时存在两台机器故障的概率极低((平均故障率*平均故障修复时间)^2)只对特殊在线系统考虑两台以上同时故障的容灾。 diff --git a/doc/design/cluster_train/data_dispatch.md b/doc/design/cluster_train/data_dispatch.md new file mode 100644 index 000000000..7bd01cf2d --- /dev/null +++ b/doc/design/cluster_train/data_dispatch.md @@ -0,0 +1,67 @@ +## 训练数据的存储和分发 + +### 流程介绍 +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。这样就可以在云端执行多种数据类计算任务,包括: + +* 数据预处理任务 +* Paddle训练任务 +* 在线模型预测服务 + + + +### 训练数据的存储 + +选择GlusterFS作为训练数据的存储服务(后续的实现考虑HDFS)。 + +在Kubernetes上运行的不同的计算框架,可以通过Volume或PersistentVolume挂载存储空间到每个容器中。 + +在存储中的共享位置,需要保存PaddlePaddle book中的所有dataset数据,并且可以被提交的job直接使用。 + +### 上传训练文件 + +使用下面命令,可以把本地的训练数据上传到存储集群中 + +``` +paddle upload train_data.list +``` + +其中`.list`文件描述了训练数据的文件和对应的label,对于图像类数据,`.list文件`样例如下,每一行包含了图片文件的路径和其label: + +``` +/data/image1.jpg 1 +/data/image1.jpg 5 +/data/image1.jpg 2 +/data/image1.jpg 5 +/data/image1.jpg 1 +/data/image1.jpg 8 +... +``` + +对于文本类训练数据样例如下(机器翻译),一行中包含源语言,目标语言的文本(label): + +``` +L' inflation , en Europe , a dérapé sur l' alimentation Food : Where European inflation slipped up + +L' inflation accélérée , mesurée dans la zone euro , est due principalement à l' augmentation rapide des prix de l' alimentation . The skyward zoom in food prices is the dominant force behind the speed up in eurozone inflation . +... +``` + +### 使用reader + +使用v2 API编写训练任务是,可以编写如下简单的reader,返回文件中的各列,然后在调用`trainer.train()`时传入,完成训练数据的读取: + +```python +def train(): + fp = open("/glusterfs/mount/dir/yourfile_%d.list" % TRAINER_ID, "r") + + def reader(): + for l in fp: + yield l[:-1].split("\t") + + return reader +``` + +## TODO + +### 支持用户自定义的数据预处理job +### 支持SSTable格式的key-value数据 diff --git a/doc/design/images/checkpointing.png b/doc/design/cluster_train/src/checkpointing.png similarity index 100% rename from doc/design/images/checkpointing.png rename to doc/design/cluster_train/src/checkpointing.png diff --git a/doc/design/cluster_train/src/data_dispatch.png b/doc/design/cluster_train/src/data_dispatch.png new file mode 100644 index 0000000000000000000000000000000000000000..5bdcc24d6a6d193cb014f8c38b362451fded5e54 GIT binary patch literal 33872 zcmdqJbx>7N8~;ml=x&hi4yC&kNkJN43n2cRb(rs)PSv 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