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README
Apache-2.0

license Release Version PRs Welcome

XLearning是一款支持多种机器学习、深度学习框架的调度系统。基于Hadoop Yarn完成了对TensorFlow、MXNet、Caffe、Theano、PyTorch、Keras、XGBoost等常用框架的集成,同时具备良好的扩展性和兼容性。

English Document

架构设计

architecture

XLearning系统包括三种组件:

  • Client:XLearning客户端,负责启动作业及获取作业执行状态;
  • ApplicationMaster(AM):负责输入数据分片、启动及管理Container、执行日志保存等;
  • Container:作业的实际执行者,负责启动Worker或PS(Parameter Server)进程,监控并向AM汇报进程状态,上传作业的输出等。对于TensorFlow类型作业,还负责启动TensorBoard服务。

功能特性

1 支持多种深度学习框架

支持TensorFlow、MXNet分布式和单机模式,支持所有的单机模式的深度学习框架,如Caffe、Theano、PyTorch等。对于同一个深度学习框架支持多版本和自定义版本。

2 基于HDFS的统一数据管理

训练数据和模型结果统一采用HDFS进行存储,用户可通过--input-strategyxlearning.input.strategy指定输入数据所采用的读取方式。目前,XLearning支持如下三种HDFS输入数据读取方式:

  • Download: AM根据用户在提交脚本中所指定的输入数据参数,遍历对应HDFS路径下所有文件,以文件为单位将输入数据平均分配给不同Worker。在Worker中的执行程序对应进程启动之前,Worker会根据对应的文件分配信息将需要读取的HDFS文件下载到本地指定路径;
  • Placeholder: 与Download模式不同,Worker不会直接下载HDFS文件到本地指定路径,而是将所分配的HDFS文件列表通过环境变量INPUT_FILE_LIST传给Worker中的执行程序对应进程。执行程序从环境变量os.environ["INPUT_FILE_LIST"]中获取需要处理的文件列表,直接对HDFS文件进行读写等操作。该模式要求深度学习框架具备读取HDFS文件的功能,或借助第三方模块库如pydoop等。
  • InputFormat: XLearning集成有MapReduce中的InputFormat功能。在AM中,根据“split size”对所提交脚本中所指定的输入数据进行分片,并均匀的分配给不同Worker。在Worker中,根据所分配到的分片信息,以用户指定的InputFormat类读取数据分片,并通过管道将数据传递给Worker中的执行程序进程。

同输入数据读取类似,用户可通过--output-strategyxlearning.output.strategy指定输出结果的保存方式。XLearning支持如下两种结果输出保存模式:

  • Upload: 执行程序结束后,Worker根据提交脚本中输出数据参数,将本地输出路径保存文件上传至对应HDFS路径。为方便用户在训练过程中随时将本地输出上传至HDFS,XLearning系统在作业执行Web界面提供对输出模型的当前状态主动保存的功能,详情请见“可视化界面”说明部分;
  • OutputFormat: XLearning集成有MapReduce中的OutputFormat功能。在训练过程中,Worker根据指定的OutputFormat类,将结果输出至HDFS。

更多详细说明见数据管理说明

3 可视化界面

作业运行界面大致分为四部分:

  • All Containers:显示当前作业所含Container列表及各Container对应信息,如Contianer ID、所在机器(Container Host)、所属类型(Container Role)、当前执行状态(Container Status)、开始时间(Start Time)、结束时间(Finish Time)、执行进度(Reporter Progress)。其中,点击Container ID超链接可查看该Container运行的详细日志;
  • View TensorBoard:当作业类型为TensorFlow时,可点击该链接直接跳转到TensorBoard页面;
  • Save Model:当作业提交脚本中“--output”参数不为空时,用户可通过Save Model按钮,在作业执行过程中,将本地输出当前模型训练结果上传至HDFS。上传成功后,显示目前已上传的模型列表;
  • Worker Metrix:显示各worker执行所占用的资源信息。

如下图所示:

yarn1

4 原生框架代码的兼容性

TensorFlow分布式模式支持“ClusterSpec”自动分配构建,单机模式和其他深度学习框架代码不用做任何修改即可迁移到XLearning上。

编译&部署指南

1 编译环境依赖

  • jdk >= 1.7
  • Maven >= 3.3

2 编译方法

在源码根目录下,执行:

mvn package

完成编译后,在源码根目录下的target目录中会生成发布包xlearning-1.1-dist.tar.gz。该发布包解压后的主要目录结构如下:

  • bin:作业提交脚本
  • lib:XLearning jar包及所依赖jar包
  • conf:XLearning配置文件
  • sbin:XLearning History Server启动脚本
  • data:运行示例所需输入数据和文件
  • examples:运行示例

3 部署环境依赖

  • CentOS 7.2
  • Java >= 1.7
  • Hadoop = 2.6,2.7,2.8
  • [可选]各计算节点具有所需学习平台的依赖环境,如TensorFlow、numpy、Caffe等。

4 XLearning客户端部署方法

在XLearning发布包根目录$XLEARNING_HOME下的conf目录中,分别配置如下文件:

  • xlearning-env.sh:设置相关环境变量,如:

    • JAVA_HOME
    • HADOOP_CONF_DIR
  • xlearning-site.xml:详细系统配置说明请见配置参数部分。

  • log4j.properties:配置日志级别

5 XLearning History Server启动方法 [可选]

  • 执行$XLEARNING_HOME/sbin/start-history-server.sh

运行示例

在XLearning客户端,使用$XLEARNING_HOME/bin/xl-submit提交脚本将作业提交至Yarn集群。 以TensorFlow作业提交为例:

1 上传训练数据至hdfs路径

将发布包解压后的data文件夹上传至hdfs,如:

cd $XLEARNING_HOME
hadoop fs -put data /tmp/ 

2 提交运行

 cd $XLEARNING_HOME/examples/tensorflow
 $XLEARNING_HOME/bin/xl-submit \
   --app-type "tensorflow" \
   --app-name "tf-demo" \
   --input /tmp/data/tensorflow#data \
   --output /tmp/tensorflow_model#model \
   --files demo.py,dataDeal.py \
   --launch-cmd "python demo.py --data_path=./data --save_path=./model --log_dir=./eventLog --training_epochs=10" \
   --worker-memory 10G \
   --worker-num 2 \
   --worker-cores 3 \
   --ps-memory 1G \
   --ps-num 1 \
   --ps-cores 2 \
   --queue default \

提交脚本各参数含义如下:

参数名称 含义
app-name 作业名称为 "tf-demo"
app-type 作业类型为 "tensorflow"
input 输入文件,HDFS路径:/tmp/data/tensorflow,对应本地路径./data
output 输出文件,HDFS路径:/tmp/tensorflow_model,对应本地路径./model
files 需要传给各container的本地文件,包括 demo.py、dataDeal.py
launch-cmd 训练执行命令
worker-memory worker内存使用为10G
worker-num worker数目为2
worker-cores worker使用CPU核数为3
ps-memory parameterServer内存使用为1G
ps-num parameterServer数目为1
ps-cores parameterServer使用CPU核数为2
queue 作业提交队列

更多相关参数详细说明请见运行提交参数部分。

FAQ

XLearning常见问题

Authors

@Yuance Li, @Wen OuYang, @Runying Jia, @YuHan Jia, @Lei Wang

联系我们

Mail: g-xlearning-dev@360.cn
QQ群:588356340
qq

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简介

XLearning是一款支持多种机器学习、深度学习框架调度系统。基于Hadoop Yarn完成了对TensorFlow、MXNet、Caffe、Theano、PyTorch、Keras、XGBoost等常用框架的集成,同时具备良好的扩展性和兼容性。 展开 收起
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