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+Copyright 2019 VirtAI Technologies Company Limited +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +This software may check available updates by collecting information including, but not limited to, GPU model type, GPU ID, CUDA version, cuDNN version, OS distribution, OS kernel version, vGPU configuration, network configuration, QEMU-KVM version, Docker version, g++ version. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + diff --git a/README.md b/README.md new file mode 100755 index 0000000..0b8d1a8 --- /dev/null +++ b/README.md @@ -0,0 +1,28 @@ +# Orion AI Platform + +Orion vGPU软件由[VirtAI Technologies Company Limited](https://virtai.tech)开发,是一个为云或者数据中心内的AI应用、CUDA应用提供GPU资源池化、GPU虚拟化能力的系统软件。通过高效的通讯机制连接应用与GPU资源池,使得AI应用、CUDA应用可以不受GPU物理位置的限制,部署在云或者数据中心内任何一个物理机、Container或者VM内。 + +* 兼容已有的AI应用和CUDA应用,无需修改已有应用程序。 +* 细粒度的GPU虚拟化支持。 +* 应用可使用远程物理节点上GPU,应用部署无需受GPU服务器位置、资源数量的约束。 +* vGPU资源动态分配动态释放。无需重启Container/VM/物理机。 +* 通过对GPU资源池的管理和优化,提高整个云和数据中心GPU的利用率和吞吐率。 +* 通过统一管理GPU,降低GPU的管理复杂度和成本。 + +# [Quick Start](doc/quick-start) +快速安装部署并体验GPU虚拟化的使用 + +# [User Guide](doc/Orion-User-Guide.md) +Orion vGPU软件用户手册 + +# [Docker Image](dockerfiles) +预装好无修改的深度学习框架(TensorFlow, PyTorch),以及Orion Client Runtime的容器镜像。 + +# More +我们通过若干技术博客,向用户展示更多的Orion vGPU软件使用场景。 + +# Contact Us + +如果您在使用本产品的过程中遇到问题,欢迎在GitHub上提交issue, 或者通过邮件联系我们: + +feedback@virtaitech.com \ No newline at end of file diff --git a/blogposts/cuda-samples.md b/blogposts/cuda-samples.md new file mode 100755 index 0000000..e69de29 diff --git a/blogposts/use-pytorch.md b/blogposts/use-pytorch.md new file mode 100755 index 0000000..e69de29 diff --git a/blogposts/use-tensorflow.md b/blogposts/use-tensorflow.md new file mode 100755 index 0000000..e69de29 diff --git a/doc/Orion-User-Guide.md b/doc/Orion-User-Guide.md new file mode 100755 index 0000000..3400c9b --- /dev/null +++ b/doc/Orion-User-Guide.md @@ -0,0 +1,401 @@ +# 概述 +Orion vGPU软件是一个为云或者数据中心内的AI应用,CUDA应用提供GPU资源池化,提供GPU虚拟化能力的系统软件。通过高效的通讯机制,使得AI应用,CUDA应用可以运行在云或者数据中心内任何一个物理机,Container或者VM内而无需挂载物理GPU。同时为这些应用程序提供在GPU资源池中的硬件算力。通过这种Orion GPU池化的能力,可以提供多个优点: +* 兼容已有的AI应用和CUDA应用,使其仍然具有使用GPU加速的性能 +* 为AI应用和CUDA应用在云和数据中心的部署提供了很大的灵活度。无需受GPU服务器位置、资源数量的约束。 +* Orion GPU资源随AI应用和CUDA应用启动时分配,随应用程序退出时自动释放。减少GPU空闲时间,提高共享GPU的周转率。 +* 通过对GPU资源池的管理和优化,提高整个云和数据中心GPU的利用率和吞吐率。 +* 通过统一管理GPU,减轻GPU的管理复杂度和成本。 + +# 支持列表 + +## 处理器 +* x86_64 + +## 操作系统 +* 64位 Ubuntu 16.04 LTS,64位 Ubuntu 14.04 LTS +* 64位 CentOS 7.x + +## NVidia GPU +* Nvidia K40,Nvidia K80 +* Nvidia P4,Nvidia P40,Nvidia P100 +* Nvidia V100 +* Nvidia T4 +* Nvidia GeForce GTX 1060,Nvidia GeForce GTX 1080,Nvidia GeForce GTX 1080 Ti,Nvidia GeForce GTX 2080 Ti + +## NVidia CUDA +* CUDA 9.0 +* CUDNN 7.2 以上 + +## 深度学习框架 +* TensorFlow 1.8 至 1.12 +* Pytorch 1.0.1,Pytorch 1.1 + +## 网络 +* TCP以太网络 +* Mellanox Connect-X4 RoCE,Mellanox Connect-X5 RoCE + +## 容器环境 +* Docker 1.13及以后版本 + +## 虚拟化环境 +* QEMU-KVM (QEMU 2.x) + +## 容器框架 +* Kubernetes 1.10 及以后版本 + +# 已知问题 +下面列出当前版本不支持的CUDA库、工具以及使用模式 +* 不支持CUDA应用程序使用Unified Memory +* 不支持nvidia-smi工具 +* 不支持NCCL库,不支持OpenGL相关接口,不支持图形渲染相关接口 +* 在使用Mellanox网卡的RDMA模式时,不支持Pytorch框架 +* 有限支持CUDA IPC,对部分程序可能不支持。 +* 由于Pytorch使用部分当前版本未支持的CUDA库,需要从源码重新编译社区版的Pytorch。 + +# 组件介绍 + +## Orion Client +该组件为一个运行环境,其模拟了NVidia CUDA的运行库环境,为CUDA程序提供了API接口兼容的全新实现。通过和Orion其他功能组件的配合,为CUDA应用程序虚拟化了一定数量的虚拟GPU(Orion vGPU)。 +使用CUDA动态链接库的CUDA应用程序可以通过操作系统环境设置,使得一个CUDA应用程序在运行时由操作系统负责链接到Orion Client提供的动态链接库上。由于Orion Client模拟了NVidia CUDA运行环境,因此CUDA应用程序可以透明无修改地直接运行在Orion vGPU之上。 + +## Orion Controler +该组件为一个长运行的服务程序,其负责整个GPU资源池的资源管理。其响应Orion Client的vGPU请求,并从GPU资源池中为Orion Client端的CUDA应用程序分配并返回Orion vGPU资源。 +该组件可以部署在数据中心任何网络可到达的系统当中。每个资源池部署一个该组件。资源池的大小取决于IT管理的需求,可以是整个数据中心的所有GPU作为一个资源池,也可以每个GPU服务器作为一个独立的资源池。 + +## Orion Server +该组件为一个长运行的系统服务,其负责GPU资源化的后端服务程序。Orion Server部署在每一个物理GPU服务器上,接管本机内的所有物理GPU。通过和Orion Controller的交互把本机的GPU加入到由Orion Controller管理维护的GPU资源池当中。 +当Orion Client端应用程序运行时,通过Orion Controller的资源调度,建立和Orion Server的连接。Orion Server为其应用程序的所有CUDA调用提供一个隔离的运行环境以及真实GPU硬件算力。 + +# 典型部署架构 +以下介绍两种常见的Orion GPU资源池化部署方案。一种是All-in-One的本地GPU虚拟化方案,一种是部署到分布式多台物理机的GPU资源池化方案。 + +## 本地GPU虚拟化方案 +![pic1](pic/pic1.png) + +本地虚拟化GPU方案指的是一个GPU物理机本身作为一个独立的GPU资源池,并且仅向本机内部的CUDA应用程序,本机内部Container里面的CUDA应用程序,以及本机内部VM虚拟机里面的CUDA应用程序提供虚拟化GPU的一种部署方式。 +如上图所示,该部署方案把Orion的三个功能组件Client,Controller和Server都安装部署到一个GPU物理机里。各个组件通过本机内部的网络配置,例如127.0.0.1,或者Container虚拟网络,VM虚拟网络连接通讯。 +Orion Client端可以部署在本地物理机上,Container里,或者VM里,从而支持CUDA应用程序无修改透明地运行在本地物理机上,Container里,或者VM里。 +该方案的与传统的设备虚拟化方案类似,通过Orion实现GPU的虚拟化,使得多个Container或者多个VM可以共享本机的一个或者多个物理GPU。其次该方案不需要高性能的RDMA网卡支持就可以获得较好的性能。 + +**尽管该方案支持CUDA应用程序运行在 本机/Container/VM 三种Orion Client环境中,但是同一时刻的配置仅对其中一种Orion Client生效。因此请勿同时使用多种Orion Client环境。如欲切换不同的Orion Client环境,需要停止上一种Orion Client环境中所有的CUDA应用程序,并且修改相应的配置后重启Orion Server服务。** + +## 分布式GPU资源池化方案 +![pic2](pic/pic2.png) + +分布式GPU资源池化方案指的是把一台或者多台服务器内的GPU作为资源池,通过Orion向局域网内任一个物理机、Container或者VM内的CUDA应用程序提供虚拟化GPU的部署方式。 +如上图所示,该部署方案在每一个GPU服务器上部署Orion Server服务。在每一个需要运行CUDA应用程序的物理机、Container和VM里部署Orion Client运行库。选择网络上至少一个Linux服务器部署Orion Controller。该Controller必须可以被属于同一个资源池的Orion Server以及希望使用该资源池的Orion Client通过网络访问。 +在每一个Orion Server环境和每一个Orion Client环境中都需要配置该Orion Controller的地址信息,使其在运行中可以通过Orion Controller请求vGPU资源,响应请求等。 +在如图2所示的部署方案中,使用了control plane和data plane两套网络。其中control plane用于和Orion Controller组件的通讯。该部分通讯流量不大,对网络延迟和带宽需求不高。data plane用于Orion Client和Orion Server之间执行CUDA任务之间的通讯。该部分对于网络的延迟和带宽有一定需求。建议使用支持RDMA的网络。 + +# 安装部署 + +## 部署Orion Controller +### 环境依赖 +* Linux Centos 7.x 或者 Ubuntu 14.04 及以上版本 +* etcd服务 + +### 启动Orion Controller服务 + +把Orion安装包拷贝至目标环境,以存放路径为 /root/Orion-1.0.1 为例。通过以下的命令可以启动Orion Controller服务。 +``` +/root/Orion-1.0.1/orion-controller start +``` + +### 配置Orion Controller服务 +Orion Controller通过启动参数支持不同的配置选项。orion-controller start 命令支持的参数有 + + -i 绑定服务于指定IP地址。默认值为 "0.0.0.0" + -p 服务监听端口。默认值为 9123 + --kv 指定etcd的key-value存储后端地址。默认值为 "http://127.0.0.1:2379" + --log-level 日志的级别。支持 [debug|info|warn|error] 四种级别。默认值为 "info" + --log 日志文件的输出路径。默认为输出到标准输出 + --vgpu 每个物理GPU切分为多少个vGPU。默认值为 4。允许的合法范围为大于等于1的自然数 + +## 部署Orion Server + +### 环境依赖 +* NVIDIA CUDA 9.0 +* NVIDIA CUDNN 7.2以上版本 +* g++ 4.8或以上版本,libcurl,openssl + +### 可选依赖 +根据支持不同使用场景,对不同软件的依赖包括: +* RDMA支持:MLNX_OFED_LINUX-4.5驱动及用户库 +* 本地虚拟机支持:libvirt >= 1.3,QEMU >= 2.0 + +### 软件安装 +把Orion安装包拷贝至目标环境,以存放路径为 /root/Orion-1.0.1 为例。通过以下的命令安装Orion Server服务。 +``` +sudo CUDA_HOME=/usr/local/cuda /root/Orion-1.0.1/install-server.sh +``` +其中CUDA_HOME变量应该设置为目标环境下正确的CUDA 9.0安装路径。 + +### 配置Orion Server +大部分配置均可使用默认配置,少量配置需要根据使用场景进行编辑配置 +编辑配置文件 /etc/orion/server.conf +* controller_addr:Orion Controller的IP地址 + * 对于上述本地GPU虚拟化方案,可以试用本地回环地址,例如为 127.0.0.1:9123 + * 对于上述的分布式GPU资源池化方案,则应该是Orion Controller在整个局域网的可访问地址 +* bind_addr:修改为Orion Server的data plane的IP地址。 + * 对于上述本地GPU虚拟化方案,如果目标支持Docker Container,则应该是Docker虚拟网络的网关地址。通常为docker0网络接口的IP地址。如果目标支持VM,则应该是VM的虚拟网关的本机IP地址。 + * 对于上述的分布式GPU资源池化方案,如果仅具备TCP以太网网卡,则该地址为TCP以太网网卡的IP地址。如果配置试用RDMA网络,则配置为RDMA网卡的IP地址。 +* enable_shm:启用基于共享内存的通讯加速机制 + * 对于上述本地GPU虚拟化方案,如果目标应用程序运行在Docker Container或者QEMU-KVM的VM里,则设为true。否则设置为false + * 对于上述的分布式GPU资源池化方案,设置为false + * 更多关于基于共享内存的通讯加速的内容请看看章节 “SHM通讯加速” +* enable_rdma:RDMA使能开关 + * 对于上述本地GPU虚拟化方案,设置为false + * 对于上述的分布式GPU资源池化方案,如果本机具备RDMA网络条件,则配置为true,否则为false +* enable_kvm:支持QEMU-KVM的VM + * 对于上述本地GPU虚拟化方案,如果目标应用程序运行在QEMU-KVM的VM里,则设置为true,否则为false + * 对于上述的分布式GPU资源池化方案,设置为false + +如需在安装之后改变CUDA的安装路径设置,则需要编辑文件 /etc/systemd/system/oriond.service。把其中的LD_LIBRARY_PATH变量和PATH变量的路径更改。更改后用如下命令重载配置 +``` +sudo systemctl reload oriond +``` +### 启动Orion Server +通过如下命令可以启动Orion Server +``` +sudo systemctl start oriond +``` +通过如下命令可以执行其他Orion Server操作 +``` +sudo systemctl stop oriond ## 停止Orion Server服务 +sudo systemctl restart oriond ## 重启Orion Server服务 +sudo systemctl status oriond ## 查看Orion Server服务状态 +sudo systemctl enable oriond ## 添加Orion Server服务到开机启动服务 +sudo systemctl enable oriond ## 添加Orion Server服务到开机启动服务 +sudo journalctl -u oriond ## 查看Orion Server的服务日志 +``` + +### Orion Server日志 +Orion Server的日志分为系统日志和用户日志两类 +* Orion Server系统日志位于/var/log/orion/server.log。记录Orion Server的服务日志。 +* Orion Server用户日志位于/var/log/orion/session/目录。每次CUDA应用运行均会在该目录产生一个对应于该次任务的日志文件。 + +## 部署Orion Client + +### 环境依赖 +* g++ 4.8或以上版本,libcurl,openssl,libuuid + +### 可选依赖 +* RDMA支持:MLNX_OFED_LINUX-4.5驱动及用户库 + +### 软件安装 +把Orion安装包内的install-client拷贝至目标环境,并以Root权限运行。以存放路径为/root/install-client 为例 +``` +sudo /root/install-client +``` +上述命令把Orion Client环境安装至默认路径 /usr/lib 中。可以通过其他安装选项进行安装 + + -d 指定安装路径。默认值为 /usr/lib + -q 静默安装。安装过程中跳过输入提示 + +**如果安装在非默认系统动态库搜索路径当中,则需要在运行CUDA应用程序之前,通过LD_LIBRARY_PATH环境变量把Oroin Client的安装路径加入到动态库搜索路径中。** + +### 配置Orion Client +Orion Client可以通过三类方法配置运行参数 + +* 通过环境变量配置运行参数 +* 通过当前用户home目录中 {$HOME}/.orion/client.conf 配置文件配置运行参数 +* 通过 /etc/orion/client.conf 配置文件配置运行参数 + +**上述方法中,通过环境变量配置的优先级最高,系统 /etc 目录中配置文件的优先级最低** + +Orion Clint的配置中分为静态配置部分和动态配置部分。 +* 静态配置部分指的是在目标环境中每次运行CUDA应用程序都保持不变的部分。 +* 动态配置部分指的是根据CUDA应用程序使用的Orion vGPU资源不同而不同的配置。 + +静态配置参数可以通过上述三类方法进行参数配置,而动态配置部分仅能通过环境变量进行参数配置 +静态配置大部分可以使用默认值,少量配置需要修改。包括: + +* 环境变量 ORION_OCNTROLLER 设置 Orion Controller 的地址 + * 例如通过 export ORION_OCNTROLLER=127.0.0.1:9123 指向一个监听在本地9123端口的 Orion Controller +* 配置文件参数 shm_path_base 指定内存文件系统的路径 + * 在本地GPU虚拟化方案中,如果目标环境是Container环境,且Orion Server开启了enable_shm支持,则在Orion Client环境中需要指定该路径。 +* 配置文件参数 controller_addr 指定 Orion Controller 的地址 + * 该参数作用等同于环境变量 ORION_OCNTROLLER 。但优先级较低 + +动态配置包括: +* 环境变量 ORION_VGPU 设置当前环境下 CUDA 应用程序申请使用多少个 Orion vGPU + * 例如通过 export ORION_VGPU=2 指定了当前 CUDA 应用程序申请使用 2 个Orion vGPU + * 该配置无默认值 +* 环境变量 ORION_GMEM 设置当前环境下,CUDA 应用程序申请使用的每个 Orion vGPU 中的显存大小。以MiB为单位。 + * 例如通过 export ORION_GMEM=4096 为当前 CUDA 应用程序指定了每个 Orion vGPU的显存大小为 4096 MiB。 + * 该配置的默认值取决于Orion Controller的配置 + +**更多关于Orion vGPU的配置请参看后面章节 “Orion vGPU的使用”** + +### Orion Client日志 +Orion Client日志位于执行应用程序的用户home目录中的 ${HOME}/.orion/log 目录中。每次CUDA应用运行均会在该目录产生一个对应于该次任务的日志文件。 + + +# 使用Orion vGPU +本章节介绍在Orion Client环境中,如何为CUDA应用程序配置使用Orion vGPU + +## Orion vGPU资源 +通过安装部署Orion vGPU软件,所有Orion Server所在的GPU服务器内的GPU均加入了一个全局共享的资源池。每个物理GPU均被划分为多个逻辑vGPU。划分vGPU的粒度为启动 Orion Controller 时的 --vgpu 参数指定。通过Orion Controller 的 --vgpu 参数把一个物理GPU划分为n个vGPU,则每个vGPU默认的显存大小为物理GPU显存的n分之一。 +Orion vGPU不被多个应用程序共享使用。 + +## Orion vGPU的使用 +由于Orion vGPU的调用接口兼容物理GPU的调用接口,因此CUDA应用程序可以无感知无修改地像使用物理GPU那样使用Orion vGPU。仅需要在运行CUDA应用程序时,通过配置文件、环境变量为本CUDA应用程序配置运行环境即可。 +经过Orion GPU资源池化之后,资源池中的vGPU使用模式为CUDA应用程序即时申请即时使用的模式。也即是当CUDA应用程序调用CUDA接口初始化时才向Orion GPU资源池申请一定数量的vGPU,当CUDA应用程序退出时其申请到的vGPU自动释放至资源池中。多次调用CUDA应用程序分配到的vGPU不一定对应于同样的物理GPU资源。 +当Orion Client的静态环境配置完毕后,在运行一个CUDA应用之前,至少需要用 ORION_VGPU 环境变量指明该CUDA应用程序希望获得的vGPU数目。可选的,可以用 ORION_GMEM 环境变量忽略 Orion Controller 中关于每个vGPU的默认显存的设置。例如一个deviceQuery CUDA程序,如下的命令使得当该CUDA程序做设备发现时,通过CUDA的接口查询到2个GPU,每个GPU的显存是4096MiB。 +``` +export ORION_VGPU=2 +export ORION_GMEM=4096 +./deviceQuery +``` +当上述deviceQuery CUDA程序启动时,会从Orion GPU资源池中独占两个vGPU。该程序结束时,会自动释放两个vGPU。可以通过重新设定环境变量,在运行CUDA应用程序之前改变对vGPU资源的使用。**一次CUDA应用程序所申请的多个vGPU可能存在于多个物理GPU上。** + +**vGPU的使用对象为CUDA应用程序,而非物理机、Container或者VM虚拟机。即使在同一个环境下运行的多个CUDA应用程序,每一个应用程序都按照当前的运行环境向Orion GPU资源池申请独立的vGPU资源。如果并行运行多个CUDA应用程序,则消耗的vGPU数量为应用程序数目乘以 ORION_VGPU 所指定的vGPU数目。** + +# SHM通讯加速 +对于本地GPU虚拟化方案,由于Orion Server和Orion Client都在同一个物理机器上,Orion支持使用基于共享内存的通讯加速——SHM通讯加速。打开SHM通讯加速的配置只需要在Orion Server端的配置文件中设置打开即可。针对不同的CUDA应用程序的运行环境不同,一些必须的步骤也不尽相同。 + +**对于本地GPU虚拟化方案,一个资源池在同一时刻仅仅支持一种运行环境配置。因此以下的SHM配置不会同时生效。** + +## CUDA应用程序运行在和Orion Server相同系统中 +在完成Orion Server 部署之后,在Orion Client运行之前,使用如下的命令创建SHM加速设备 +``` +sudo orion-shm +``` +**由于该设备基于共享内存,因此每次系统重启都需要手动创建一次。** + +## CUDA应用程序运行在 Container 中 +在该种使用场景中,如果使用了Kubernetes作为容器编排框架,Orion vGPU软件提供了Kubernetes Device Plugin来管理SHM加速设备,并不需要用户手动操作。 +如果用户通过手动执行 Docker run 来运行CUDA应用的容器,则需要手动 mount 一个SHM加速设备到应用容器当中。例如 +``` +sudo orion-shm -i 1 +sudo docker run -v /dev/shm/orionsock1:/dev/shm/orionsock1:rw ... +``` +上述命令首先通过orion-shm创建一个索引号为1的SHM加速设备,该命令会在/dev/shm目录中生成SHM设备 orionsock1。docker run执行时,需要用 -v 参数把该设备mount到container内完全相同的路径中,并且打开读写权限。 + +**不同的Container实例必须mount不同的SHM设备** + +## CUDA应用程序运行在基于 QEMU-KVM 的虚拟机中 +在该种使用场景中,不需要手动维护SHM设备。 + +# 故障排查 +Orion vGPU软件提供了健康检查的工具 orion-check 来帮助用户在各个阶段下检测环境和排查故障。包括在安装部署之前检查环境配置以及在运行启动过程中检查故障。 + +## 在安装部署之前检查环境兼容性 + +把Orion安装包拷贝至目标环境,以存放路径为 /root/Orion-1.0.1 为例。 + +* 检查安装Orion Controller的环境 +``` + sudo /root/Orion-1.0.1/orion-check install controller +``` +* 检查安装Orion Server的环境 +``` + sudo /root/Orion-1.0.1/orion-check install server +``` +* 检查安装Orion Client的环境 +``` + sudo /root/Orion-1.0.1/orion-check install client +``` + +## 在安装部署之后检查运行时的健康状况 +* 检查Orion Server的运行状态 +``` + sudo orion-check runtime server +``` + +# 配置文件 +除了通过环境变量和运行二进制时的命令行参数可以控制Orion服务的行为,在Orion Server和Orion Client环境中各有配置文件可以对Orion服务进行配置。 + +## Orion Server配置文件 +该配置文件位于 /etc/orion/server.conf。在安装部署Orion Server之后该文件被创建 + +### [server] 配置 + +* listen_port : Orion Server的监听起始端口。以该端口开始的连续3个端口被用于Orion Server服务。默认值为9960 +* bind_addr :当本机有多个网络地址的时候,通过该参数指定Orion Server绑定地址。默认值为 127.0.0.1 +* enable_shm :是否启用SHM加速通讯。仅仅在Orion Server和Orion Client部署在同一个物理机的时候可以启用。默认值为 true +* enable_kvm :是否支持Orion Client部署在VM里面且使用SHM加速。仅仅在Orion Server和Orion Client部署在同一个物理机的时候可以启用。默认值为 false + +### [server-log] 配置 +* log_with_time :记录log的时候带时间戳。0表示否,1表示是。 +* log_to_screen :log是否输出到屏幕。0表示否,1表示是。 +* log_to_file :log是否输出到磁盘上的文件。0表示否,1表示是。 +* log_level :屏幕输出log的级别。支持 ERROR, WARNING, INFO, DEBUG四个级别 +* file_log_level :磁盘文件输出log的级别。支持 ERROR, WARNING, INFO, DEBUG四个级别 + +### [server-shm] 配置 +该字段的配置仅仅当 [server] 字段的 enable_shm 或者 enable_kvm 被设置为 true的时候生效 +* shm_path_base :OS的共享存储文件的默认位置。默认值为 /dev/shm +* shm_group_name :使用 KVM 的时候,虚拟机的的 Linux 用户组名 +* shm_user_name :使用 KVM 的时候,虚拟机的 Linux 用户名 +* shm_buffer_size :使用SHM加速的时候,分配的内存大小。默认为128MiB。 + +### [controller] 配置 +* controller_addr : 配置Orion Server如何连接Orion Controller + +## Orion Client配置文件 +Orion Client的配置文件名为 client.conf,可以放在两个位置。一个是 /etc/orion/client.conf, 另一个是用户home目录的 $HOME/.orion/client.conf。 后者具有更高的优先级。通过安装文件安装Orion Client之后,仅仅把配置文件安装到 /etc/orion 目录中。 + +### [client-log] 配置 +* log_with_time :记录log的时候带时间戳。0表示否,1表示是。 +* log_to_screen :log是否输出到屏幕。0表示否,1表示是。 +* log_to_file :log是否输出到磁盘上的文件。0表示否,1表示是。 +* log_level :屏幕输出log的级别。支持 ERROR, WARNING, INFO, DEBUG四个级别 +* file_log_level :磁盘文件输出log的级别。支持 ERROR, WARNING, INFO, DEBUG四个级别 + +### [client-shm] 配置 +* shm_path_base :必须和Orion Server环境中的配置一样。默认值为 /dev/shm + +### [controller] 配置 +* controller_addr : 配置Orion Server如何连接Orion Controller + + +# 常见问题 +* Orion Client端应用程序启动报告无法找到NVidia GPU + * 此故障为应用程序没有使用Orion Client运行库导致,可能的原因有几种: + * 该应用程序在编译期间静态链接了NVidia的库,导致其运行时并不调用Orion Client的运行库。该问题应该通过设置动态链接并重新编译解决。 + * 该应用程序虽然使用CUDA相关的动态链接库,但是编译器使用rpath选项指明了CUDA库加载的绝对路径,而该路径并非是Orion Client的安装路径。rpath优先级高导致库加载的路径非期望的Orion Client安装路径。该问题或者通过去掉rpath设置后重新编译解决,或者用Orion Client运行库覆盖rpath指明的路径内的库解决。 + * Orion Client库的安装路径没有使用ldconfig或者环境变量LD_LIBRARY_PATH放到动态库加载路径。该问题通过使用ldconfig永久把Orion Client的安装路径加入到系统搜索路径,或者正确使用环境变量LD_LIBRARY_PATH来设置。 + +* Orion Client端打印 “Could not connect to server” 并且打印“Fail to get version information from Orion Controller.”。 + * 此故障为Orion Client端无法连接上Orion Controller。请从如下几个方面进行故障排除: + * 确认Orion Client的环境是否存在和Orion Controller之间网络可达的路径 + * 借助netstat等命令确认Orion Controller监听在 0.0.0.0 网段的 9123 端口上 + * 借助telnet,nc等命令确认Orion Client可以访问Orion Controller所在环境的 9123 端口。 + * 常见的防火墙配置可能会阻塞Orion Controller所在环境的 9123 端口。包括 CentOS 的 firewalld防火墙,Docker默认自带的 iptables 规则。 + +* Orion Client端没有打印“Fail to get version information from Orion Controller.” 但是打印 “Client exits without allocation ID” + * 此故障为Orion Client申请的vGPU资源超出了资源池可分配的资源。请从如下几个方面进行故障排除: + * 在Orion Server 环境中运行命令 orion-check runtime server。该命令会打印全局资源池中有多少剩余vGPU。如果剩余vGPU为0: + * 确认是否的确由于正在运行的Orion Client过多从而导致资源耗尽。 + * 是否由于连续串行执行多次Orion Client应用程序,系统需要一定时间间隙完成全局资源的释放。 + * 是否没有成功启动 Orion Server服务。 + * Orion Controller的环境中没有安装并启动Linux etcd服务。 + * 如果剩余vGPU资源不为0 + * 确认Orion Clinet环境中的 ORION_VGPU 和 ORION_GMEM 请求的资源确实超出了实际可分配的能力,以下资源请求无法得到满足 + * ORION_GMEM 配置使用的 GPU 显存超出了单个物理 GPU 的显存大小。 + * ORION_VGPU 和 ORION_GMEM 请求的资源超出单个 GPU 服务器的资源数目 + * 是否通过shell脚本连续多次并发启动多个Orion Client请求。 + +* Orion Client打印“Client exits with allocation ID ...”但是没有其他更多提示 + * 此故障可能是Orion Client无法完成和Orion Server的连接导致。请从如下几个方面进行故障排除: + * 借助netstat等命令确认 Orion Server 环境中的 oriond 后台进程监听在9960,9961 端口上,且监听的网段为 Orion Client 可达的网段 + * 对于 KVM 场景此网段应为虚拟网关IP地址 + * 对于 Docker 场景此网段应为虚拟网关IP地址(常见为docker0虚拟网口) + * 借助telnet,nc等命令确认Orion Client可以访问Orion Server所在环境的 9960,9961 端口。 + * 常见的防火墙配置可能会阻塞Orion Server所在环境的 9960,9961 端口。包括 CentOS 的 firewalld防火墙,Docker默认自带的 iptables 规则。 + +* 本地容器使用共享内存时,应用程序hang住,CPU占有率高,可以检查以下项目。修复后需要重启Orion Server方能生效。 + * 检查是否共享内存文件在容器内外名字不一致。注意容器内的挂载地址需要和物理地址完全一样,即使用`-v /dev/shm/orionsock:/dev/shm/orionsock:rw`启动容器。 + * 检查是否多个容器将同一块共享内存挂载进内部。 + * 检查用户是否曾经手动删除、覆盖、修改`/dev/shm`目录下的某块`orionsock`共享内存 + +* Orion Server服务无法启动,oriond进程启动失败 + * 通过运行 orion-check runtime server 来检查环境。可能的原因有 + * oriond进程依赖CUDA,CUDNN库无法搜索到导致可执行文件无法被操作系统启动。 + * 修改上述Orion Server服务配置文件 + * oriond进程依赖的其他库没有安装,例如libcurl,libopenssl等 + +* 在虚拟机场景中,Orion Client的应用程序打印 “Fail to finish system initialization.” 且没有其他错误提示。 + * Orion Server配置文件中的 enable_kvm 没有设置为 true + diff --git a/doc/pic/pic1.png b/doc/pic/pic1.png new file mode 100755 index 0000000000000000000000000000000000000000..1b5dabd76efe41044c812e07b74f3f7107f2e031 GIT binary patch literal 25303 zcmce;2{@E(`v*J{%C3@~($gM7l5K1)PY*>Yva89GVJu_GGL{rt&}yg{o*p5M$ui84 zrN~r+Aq-~h8iO%pnX!!Jy9d2b-v9IdkM}!{@BhtlbTsa{@AEvbbGv@md7amN*Yf-s z5kXl&5C|l4_V<$)K_K1?5Qryx3qNor*I9QL@E;HS;u#ZAexv*d@W*C%<8#I!P(hN= z;#EH2@2$SS+rmL0(HibQ9)eHKbr9&9*V&WCmu@;SD@Y_2n-IF(yqL?^C}TGnw+@p{ z`yNrQK1|oTWh|!LTI6u*ca_rx7w#Y4eOpiJaX`zyhsO|iyP}Wr2yf;yHQ&N>KV~~z z_Pj7b+xLXD+~ALz+79&&&J-0=dRA|nuY3?NXH?XIXztKX`Rh#T>&eskHmC1dpq z=%~1*M=FD4;*dXM%bDzglN^^fgWfsp6Uh)WSdLoay%kTqNIwLS 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vGPU软件**无修改**地使用官方 TensorFlow 1.12 进行模型训练与推理。 + +更多的主题,例如如何调整物理GPU划分成Orion vGPU的粒度,如何使用PyTorch框架,以及如何通过Kubernetes调度Orion vGPU资源等,请参见我们的[用户手册](../Orion-User-Guide.md)以及[技术博客](../../README.md#tech-blog)。 + +# Orion vGPU软件架构 + +![Architecture Overview](./figures/arch-local.png) + +## Orion Client +该组件为一个运行时环境,模拟了NVidia CUDA的运行库环境,为CUDA程序提供了API接口兼容的全新实现。通过和Orion其他功能组件的配合,为CUDA应用程序虚拟化了一定数量的虚拟GPU(Orion vGPU)。 + +使用CUDA动态链接库的CUDA应用程序可以通过操作系统环境设置,使得一个CUDA应用程序在运行时由操作系统负责链接到Orion Client提供的动态链接库上。由于Orion Client模拟了NVidia CUDA运行环境,因此CUDA应用程序可以透明无修改地直接运行在Orion vGPU之上。 + +## Orion Controller +该组件为一个长运行的服务程序,其负责整个GPU资源池的资源管理。其响应Orion Client的vGPU请求,并从GPU资源池中为Orion Client端的CUDA应用程序分配并返回Orion vGPU资源。 + +该组件可以部署在数据中心任何网络可到达的系统当中。每个资源池部署一个该组件。资源池的大小取决于IT管理的需求,可以是整个数据中心的所有GPU作为一个资源池,也可以每个GPU服务器作为一个独立的资源池。 + +## Orion Server +该组件为一个长运行的系统服务,负责GPU资源化的后端服务。Orion Server部署在每一个物理GPU服务器上,接管本机内的所有物理GPU。Orion Server通过和Orion Controller的交互把本机的GPU加入到由Orion Controller管理维护的GPU资源池当中。 + +当Orion Client端应用程序运行时,通过Orion Controller的资源调度,建立和Orion Server的连接。Orion Server为其应用程序的所有CUDA调用提供一个隔离的运行环境以及真实GPU硬件算力。 + + +# Orion 安装包介绍 +## **目录结构** +[下载得到](../..)的安装包内容如下: + +```bash +blogposts doc dockerfiles install-client install-server.sh LICENSE md5sum.txt orion-check orion.conf.template orion-controller oriond orion-shm README.md +``` + +下文中,若非明确说明,假设读者命令行的工作目录即为安装包目录。 + +## **主要组件** +* orion-check: 检查系统兼容性与依赖项,以及Orion平台运行时健康状态的工具 +* install-client: Orion Client安装包 +* install-server.h, oriond: Orion Server安装套件 +* orion-controller:Orion Controller可执行文件 +* orion-shm:创建通讯用共享内存的工具 + + +## **系统环境检查工具** + +直接运行`orion-check`工具,屏幕上会输出命令接受的参数 + +```bash +# (omit output) +COMMANDS: + install + server Check health for installing Orion Server + client Check health for installing Orion Client + controller Check health for installing Orion Controller + all Check health for installing all Orion components + + runtime + server Diagnose the status for Orion Server running + client Diagnose the status for Orion Client running + +EXAMPLES: + orion-check install server + orion-check install client + orion-check runtime server +``` + +`orion-check`工具有两大用途: +* 安装Orion平台之前检查系统环境依赖: `orion-check install all` +* Orion平台运行中诊断各组件的健康状态:`orion-check runtime server/client` + +后续文档中,我们会逐步展示 `orion-check`工具的不同应用。 + +# Orion vGPU软件服务器端安装部署 + +## 步骤一:部署 Orion Controller + +### 安装环境准备 +环境依赖项 + +* etcd + +如果操作系统中尚未安装`etcd`,可以通过以下命令安装并启动: + +```bash +# Ubuntu 16.04 +sudo apt install -y etcd + +# CentOS 7.x +sudo yum install -y etcd.x86_64 +sudo systemctl start etcd +``` + +可以通过`systemctl`确认`etcd`服务正常运行: + +```bash +systemctl status etcd +``` + +正常运行时,屏幕上会出现`active (running)`的彩色文字。 +### 启动 Orion Controller + +在集群上部署Orion vGPU软件时,Orion Controller可以运行在任意节点上。本文为方便起见,将Orion Controller和Orion Server服务部署在同一台含有GPU的节点上。 + +下述命令将启动Orion Controller + +```bash +./orion-controller start +``` + +正常情况下,屏幕会输出如下的日志,表明Orion Controller连接上`etcd`服务,并监听来自网络所有地址的Orion vGPU资源请求。 + +```bash +INFO[0000] Creating database connection to http://127.0.0.1:2379 +INFO[0000] Database connection is created. +INFO[0000] Controller is launching, listening on 0.0.0.0:9123 +``` + +### (可选)后台运行Orion Controller并输出日志到文件 +下述命令将Orion Controller进程运行在后台,并将日志输出到工作目录下的`controller.log`文件。用户可以用`cat`等命令查看日志内容。 + +```bash +nohup ./orion-controller start --log controller.log & +``` + +更多可选参数可以用`./orion-controller help start`查看。 + + +## 步骤二:安装部署 Orion Server 服务 + +### 安装环境准备 +依赖项 +* Ubuntu 14.04, 16.04, CentOS 7.x +* CUDA 9.0 (目前不支持其余CUDA版本) +* CUDNN 7.2及以上版本 +* NVIDIA driver 384及以上版本 +* libcurl + +这里我们假设读者的操作系统上已经安装384以上版本的NVIDIA显卡驱动。 + +用户可参考附录中的[CUDA和CUDNN快速安装](appendix.md#install-cuda)小节来安装`CUDA 9.0`和`CUDNN 7.x`。下文中我们假设用户的CUDA安装路径为默认的`/usr/local/cuda-9.0`,而CUDNN的动态库放在`/usr/local/cuda-9.0/lib64`目录下。 + +Orion Server所依赖的`libcurl`库:可以通过以下命令安装 + +```bash +# Ubuntu 16.04 +sudo apt install -y libcurl4-openssl-dev + +# CentOS 7.x +sudo yum install -y libcurl-devel.x86_64 +``` + + +### 安装Orion Server + +由于Orion Server运行时需要有CUDA环境支持,所以用户在安装Orion Server时,需要通过`CUDA_HOME`环境变量显式指定CUDA 9.0的安装路径: + +```bash +sudo CUDA_HOME=/usr/local/cuda-9.0 ./install-server.sh +``` + +值得注意的是,由于安装以`root`身份进行,即使当前用户环境中已经配置了`CUDA_HOME`环境变量(例如在~/.bashrc中),上述步骤中`CUDA_HOME=/usr/local/cuda-9.0` 仍然**不能**省略。 + +若安装成功,会有下列输出: + +```bash +orion.conf.template is copied to /etc/orion/server.conf as Orion Server configuration file. +Orion Server is successfully installed to /usr/bin +Orion Server is registered as system service. +Using following commands to interact with Orion Server : + + systemctl start oriond # start oriond daemon + systemctl status oriond # print oriond daemon status and screen output + systemctl stop oriond # stop oriond daemon + journalctl -u oriond # print oriond stdout + +Before launching Orion Server, please change settings in /etc/orion/server.conf according to your environment. +``` + +上述命令会将`oriond`,`orion-check`安装到`/usr/bin`目录下 (此后可在任意路径下运行`orion-check`工具),并将Orion Server的二进制文件 `oriond`注册为由`systemctl`管理的系统服务。 + +### Orion Server 服务配置 + +Orion vGPU软件支持以下的Orion Client runtime与Orion Server之间数据交互的通信模式 + +* 本地共享内存(shared memory, SHM) +* 本地/远程 RDMA +* 本地/远程 TCP + +Orion vGPU软件支持用户程序运行在没有GPU的本地计算节点、容器或者KVM虚拟机内。对KVM虚拟机的支持需要手动修改配置文件开启。 + +Orion Server服务启动时,读取`/etc/orion/server.conf`配置文件来决定启动模式。安装Orion Server后,该配置文件的内容为默认值: + +```bash +[server] + listen_port = 9960 + bind_addr = 127.0.0.1 + enable_shm = "true" + enable_rdma = "false" + enable_kvm = "false" + +[server-log] + log_with_time = 1 + log_to_screen = 0 + log_to_file = 1 + log_level = INFO + file_log_level = INFO + +[server-shm] + shm_path_base = "/dev/shm/" + shm_group_name = "kvm" + shm_user_name = "libvirt-qemu" + shm_buffer_size = 134217728 + +[controller] + controller_addr = 127.0.0.1:9123 +``` + +一般来说,有可能需要修改的是`[server]`这一节的配置值: + +* `bind_addr`: Orion Server所接受的数据通路地址,必须保证Orion Client端能够访问这一地址。 +* `enable_shm`, `enable_rdma`: 通信模式开关,不可同时为`true` +* `enable_kvm`:如果想在KVM虚拟机中访问Orion vGPU资源,这一项需要设为`true`, 否则应当设为`false` + +本文后面的各小节会结合不同场景的差异,解释应当如何根据使用环境和需求合理配置Orion Server参数。 + +### 启动Orion Server服务 + +我们保留`/etc/orion/server.conf`中的默认配置,用下述命令启动Orion Server服务: + +```bash +sudo systemctl start oriond +``` + +通过`systemctl`检查Orion Server服务的运行状态 + +```bash +systemctl status oriond +``` + +若服务进程`oriond`正常工作,输出信息中会有彩色的`active (running)`状态显示。 + +如果这时候我们查看Orion Controller的输出(或者日志文件),会发现有类似下面的新增日志: + +```bash +INFO[1737] Received API (method:POST) : /heartbeat/127.0.0.1 res=nvidia_cuda&status=off +INFO[1738] Received API (method:POST) : /heartbeat/127.0.0.1 res=nvidia_cuda&status=on +INFO[1738] Adding new host for NVidia GPU : 127.0.0.1 +INFO[1738] Adding new NVidia GPU to host 127.0.0.1 (GPU-d081d24f-816a-0324-1ada-60863f180517; GeForce GTX 1080 Ti) +``` + +这表明Orion Server扫描了物理机上的GPU信息,并成功地向Orion Controller注册设备信息。 + +如果Orion Server和Orion Controller的状态都正常,但Orion Server服务启动后Orion Controller没有汇报成功添加NVIDIA GPU,说明Orion Server和Orion Controller之间没有成功建立起联系。用户可以参考附录中的[安装部署检查](appendix.md#installation-check)小节运用`orion-check`工具进行检查。 + +# 典型使用场景 + +下面,我们选取三个典型场景,展示在不同的环境下,如何运用Orion vGPU软件**无修改**地使用官方 TensorFlow 1.12 进行模型训练与推理。具体地,我们将展示以下例子: + +## [Docker容器中使用本地节点GPU资源](container.md) + +* 本例中,我们在容器内使用一块Orion vGPU,该vGPU位于本地物理机上的一块 NVIDIA GTX 1080Ti 显卡。 +* 容器用`docker run`命令启动,不依赖于`nvidia-docker`,也没有将物理机上的GPU设备直通穿透(passthrough)到在容器内部。通过安装Orion vGPU软件,容器得以使用Orion vGPU资源加速计算。 +* 安装部署完成后,我们会在容器中启动 `Juypter Notebook`,使用TensorFlow 1.12的eager execution模式进行 pix2pix 模型训练与推理 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/pix2pix/pix2pix_eager.ipynb + +## [KVM虚拟机中使用本地节点GPU资源](kvm.md) + +* 在 KVM 虚拟机中,在Orion vGPU软件的共享内存(shared memory, SHM)模式下进行 CIFAR10 数据集上的 CNN 模型训练与推理。本例中,我们在虚拟机内使用两块Orion vGPU, 两块vGPU分别位于本地物理机上的两块 NVIDIA Tesla V100 计算卡。 +* 安装部署完成后,我们在KVM虚拟中运行TensorFlow官方模型repo中的CIFAR10_Estimator https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10_estimator + + +## [通过RDMA使用远程节点GPU资源](remote_rdma.md) + +* 在 没有 GPU 的节点上,在Orion vGPU软件的 RDMA 模式下使用远程节点GPU资源进行 Imagenet 数据集上的 CNN 模型训练与推理。本例中,我们在虚拟机内使用两块Orion vGPU, 分别位于**远程节点**上的两块 NVIDIA Tesla V100 计算卡。 +* 安装部署完成后,我们运行TensorFlow official benchmark进行Imagnet上CNN模型训练 https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks +* 我们先展示使用随机生成的Imagenet数据 (synthetic Imagenet data) 进行`inception3`模型训练。通过Orion vGPU软件,我们使用远程节点上的两块Tesla V100训练500个batch。 +* 我们展示使用TFRecord格式的真实Imagenet数据训练`inception3`模型。通过Orion vGPU软件,我们使用远程节点上的两块Tesla V100训练5个完整epoch,最终在验证集上达到36.92%的top-1精度,64.04%的top-5精度。 + + +## 更多使用场景 + +读者可以在我们的主页上[技术博客](../../README.md#tech-blog)栏下找到更多的主题,例如: + +* 使用`orion-smi`工具查看Orion vGPU使用情况 +* TensorFlow 1.12版本使用Orion vGPU加速多种模型训练与推理 +* PyTorch 1.1.0版本使用Orion vGPU训练`resnet50`模型 +* 编译NVIDIA官方`CUDA Samples`,从而使用Orion vGPU运行 +* 在Kubernetes集群上通过`k8s-orion-plugin`使用Orion vGPU资源 + +# [附录](appendix.md) + +我们介绍了以下内容 + +* 快速安装CUDA和cuDNN环境 +* 配置防火墙 +* 安装部署前后状态检查 diff --git a/doc/quick-start/appendix.md b/doc/quick-start/appendix.md new file mode 100755 index 0000000..8e998b7 --- /dev/null +++ b/doc/quick-start/appendix.md @@ -0,0 +1,238 @@ +# 附录 + +## CUDA和CUDNN快速安装 + +如果GPU节点上没有预先安装`CUDA 9.0`,我们推荐使用官方的`.run`安装包进行绿色安装 + + sudo bash cuda_9.0.176_384.81_linux.run + +默认会安装到`/usr/local/cuda-9.0`路径下。值得注意的是,当安装包询问是否安装自带的384驱动,需要选择`No`以免覆盖系统已有驱动。此外,用户会被询问是否要将安装目录`/usr/local/cuda-9.0`软链接到`/usr/local/cuda`路径,如果系统中已经有其它CUDA版本的话,建议不要选择建立软链接,以免破坏系统其余应用的环境。 + +对于`CUDNN 7`的安装,我们也推荐下载官方`.tgz`包,将解压后的`cuda/include/cudnn.h`放到`/usr/local/cuda-9.0`下,将解压后的`cuda/include/lib64`下的库文件复制到`/usr/local/cuda-9.0/lib64`下即可。以`CUDNN 7.4.2`为例,从官网下载得到`cudnn-9.0-linux-x64-v7.4.2.24.tgz`后: + + # Uncompress + tar zxvf cudnn-9.0-linux-x64-v7.4.2.24.tgz && cd cuda + + # Copy header file + sudo cp include/cudnn.h /usr/local/cuda-9.0/include/ + + # Install dynamic shared library + sudo cp lib64/libcudnn.so.7.4.2 /usr/local/cuda-9.0/lib64/ + cd /usr/local/cuda-9.0/lib64 + sudo ln -s libcudnn.so.7.4.2 libcudnn.so.7 + sudo ln -s libcudnn.so.7 libcudnn.so + + # (Optional) Install static library + sudo cp lib64/libcudnn_static.a /usr/local/cuda-9.0/lib64/ + +由于我们在执行`install-server.sh`脚本时会指定`CUDA 9.0`的安装目录`/usr/local/cuda-9.0`,因此不一定需要在当前用户的`~/.bashrc`中配置相应的环境变量。 + + +## 防火墙设置 + +我们需要保证在运行Orion Controller的节点上,配置防火墙打开9123监听端口,在运行Orion Server的节点上,配置防火墙打开9960-9961端口。为此,我们假设操作系统上已经启动了`firewalld`服务。 + +```bash +# Check status +firewall-cmd --state +# Allow ports +firewall-cmd --add-port=9123/tcp --permanent +firewall-cmd --add-port=9960-9961/tcp --permanent +# Take effect +firewall-cmd --reload +``` + +例如在CentOS系统上,可能出现`firewalld`服务没有启动,但Docker默认安装时安装依赖项`iptables`,阻止容器内的Orion Client运行时通过9123和9960-9961端口连接到外部。此时,在运行上述命令前,需要先启动`firewalld`服务: + +```bash +systemctl unmask firewalld +systemctl start firewalld +``` + +## 安装部署检查 + +### Orion Server安装前后检查 + +#### 安装前准备 +需要满足以下要求 +* Ubuntu 14.04, 16.04, CentOS 7.x +* CUDA 9.0 (目前不支持其余CUDA版本) +* CUDNN 7.2及以上版本 +* NVIDIA driver 384及以上版本 +* 安装`libcurl`库 + +我们依然使用`orion-check`工具检查环境: + + ./orion-check install all + +为使本地Orion vGPU软件正常工作,至少需要满足基本项`OS`, `CUDA`, `CUDNN`, `NVIDIA GPU`, `etcd service`的状态为`Yes`。 + +除了 Volta 和 Turing架构的显卡(例如 V100, 2080Ti)之外,在使用其余显卡时,一定要确保`CUDA MPS`处于关闭状态。 + +下面的结果是在一台配备一张GTX 1080Ti的普通PC上的输出。可以看到,这台机器的环境不支持RDMA网络,并且没有安装KVM虚拟化环境,但可以在本机容器中使用Orion vGPU。 + +```bash +=============================================== +Installation summaries : + +OS : [Yes] +RDMA : [No] +CUDA : [Yes] +CUDNN : [Yes] +NVIDIA GPU : [Yes] +NVIDIA CUDA MPS : [OFF] +etcd service : [Yes] +QEMU-KVM environment : [No] +Docker container environment : [Yes] +Orion Server binary: [Yes] +``` + +#### 运行状态检查 + +我们使用下述命令检查Orion vGPU软件的运行状态: + +```bash +sudo orion-check runtime server +``` + +一种常见的错误是Orion Controller并没有在GPU节点上正常启动。这种情况下,输出如下: + +```bash +# (omit output) +Orion Controller addrress is set as 127.0.0.1:9123 in configuration file. Using this address to diagnose Orion Controller +[Error] Can not reach 127.0.0.1:9123. Please make sure Orion Controller is launched at the address, and the firewall is correctly set. +``` + +这种情况下,用户可以执行几个步骤: +* 检查Orion Controller的日志输出,确认Orion Controller是否正常启动并监听在`0.0.0.0:9123`端口上 +* 确认`/etc/orion/server.conf/`里的`controller_addr`设置为`controller_ip:9123` +* 根据[防火墙设置](#firewall)小节内容检查9123端口是否开放 + +更多的问题解答,用户可以参见用户手册[相关章节](../Orion-User-Guide.md#常见问题) 。 + +当GPU节点上有一块GPU卡(GTX1080 Ti)时,正确的输出类似于: + +```bash +Searching NVIDIA GPU ... +CUDA driver 430.14 is installed. +1 NVIDIA GPU is found : + 0 : GeForce GTX 1080 Ti + +Checking NVIDIA MPS ... +NVIDIA CUDA MPS is off. + +Checking Orion Server status ... +Orion Server is running with Linux user : root +Orion Server is running with command line : /usr/bin/oriond +Enable SHM [Yes] +Enable RDMA [No] +Enable Local QEMU-KVM with SHM [No] +Binding IP Address : +Listening Port : 9960 + +Testing the Orion Server network ... +Orion Server can not be reached through +Please check the firewall setting. + +Checking Orion Controller status ... +[Info] Orion Controller setting may be different in different SHELL. +[Info] Environment variable ORION_CONTROLLER has the first priority. + +Orion Controller addrress is set as 127.0.0.1:9123 in configuration file. Using this address to diagnose Orion Controller +Address 127.0.0.1:9123 is reached. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 4 vGPU under managered by Orion Controller. 4 vGPU are free now. +``` + +这表明Orion Controller和Orion Server均正常运行,它们之间的交互正常,因此可以服务来自于Orion Client的请求。默认情况下,一块物理GPU被虚拟化成最多四块Orion vGPU。 + +### Orion Client运行时检查 + +#### 常见故障原因 + +当Orion Controller和Orion Server的状态正常后,用户在Orion Client里可能因为多种原因无法使用Orion vGPU服务: + +* 环境变量`ORION_CONTROLLER`或者`/etc/orion/client.conf`里的`controller_addr`设置不对,Orion Client无法向Orion Controller申请资源 +* 由于防火墙的存在,Orion Client无法向Orion Controller申请资源 +* Orion Server启动的模式(例如共享内存、RDMA,KVM模式等)与Orion Client的形态不匹配 +* 通过共享内存在本地容器环境使用Orion vGPU时,没有将`orion-shm`工具创建的`/dev/shm/orionsock`挂载到容器内,或者多个容器挂载了同一块共享内存 +* 一切设置都正常,只是GPU资源池中的Orion vGPU已经耗尽。这里耗尽既可以是因为Orion vGPU的数目超过上限,也可能是因为显存使用超过物理上限。 + +#### 使用`orion-check`工具检查状态 + +在Orion Client内部,用户可以使用`orion-check`工具来判断当前状态: + +```bash +# From inside Orion Client (container/vm instanace/bare metal) +orion-check runtime client +``` + +例如在以`--net host`参数启动的本地容器中,运行成功的结果为: + +```bash +Checking Orion Controller status ... +[Info] Orion Controller setting may be different in different SHELL. +[Info] Environment variable ORION_CONTROLLER has the first priority. + +Environment variable ORION_CONTROLLER is set as 127.0.0.1:9123 Using this address to diagnose Orion Controller. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 4 vGPU under managered by Orion Controller. 4 vGPU are free now. +``` + +下面我们看一个错误的例子。如果容器没有以`--net host`启动,`ORION_CONTROLLER`(或者`/etc/orion/client.conf`中的`controller_addr`)应该设置为`:9123`,例如`172.17.0.1:9123`,才可以向Orion Controller发送请求。如果我们不设置,使用默认值`127.0.0.1:9123`的话,`orion-check`工具会汇报: + +```bash +Orion Controller addrress is set as 127.0.0.1:9123 in configuration file. Using this address to diagnose Orion Controller +[Error] Can not reach 127.0.0.1:9123. Please make sure Orion Controller is launched at the address, and the firewall is correctly set. +``` + +如果确信Orion Controller地址设置正确,用户可以参考[本附录相应小节](#firewall)检查防火墙设置。 + +#### 根据用户应用程序输出判断 + +用户的应用程序在使用Orion Client Runtime时,会打印一定的日志输出,这些日志可以帮助我们诊断问题。 +我们以一个简单的`vectorAdd`为例: + +```bash +./vectorAdd + +[Vector addition of 50000 elements] +Call cudaMalloc +VirtaiTech Resource. Build-cuda-0bf2c23-20190628_063219 +cudaMalloc on device vector A succeeded. +Copy input data from the host memory to the CUDA device +CUDA kernel launch with 196 blocks of 256 threads +Copy output data from the CUDA device to the host memory +Test PASSED +Done +2019-06-29 07:03:58 [INFO] Client exits with allocation ID 4c010d62-3cc7-4cf5-b6d2-ce54fd3e676d +``` + +可以看到,我们在程序运行的首尾各加了一行日志。 + +头部的`VirtaiTech Resource. Build-cuda`表明应用程序成功使用了Orion Client Runtime。否则,用户需要检查以下事项: + +* 应用程序是否动态链接到CUDA Runtime? 对官方TensorFlow来说,这一点是满足的。而对于某些应用,例如NVIDIA官方Samples,Makefile里面的编译选项是静态编译,因此需要重新编译。特别地,对于CMake编译的程序,例如PyTorch,需要检查库的RPATH,把Orion Client Runtime安装到一样的路径下。一般安装到`/usr/local/cuda-9.0`,再软链接`/usr/local/cuda => /usr/local/cuda-9.0`即可。 +* 运行`install-client`包时,如果没有安装到默认路径,需要确保在每个terminal里面都设置`LD_LIBRARY_PATH=:$LID_LIBRARY_PATH`。 + +程序退出时的`Client exits with allocation ID`表明用户从Orion Controller申请到了资源。否则,说明资源申请失败: + +* Orion Controller没有正常工作,或者Orion Client参数设置错误,也可能是防火墙限制导致。用户可以根据前面内容进行检查 +* Orion vGPU资源池里面没有符合`ORION_VGPU`和`ORION_GMEM`要求的资源。用户可以根据用户手册[相关章节](../Orion-User-Guide.md#常见问题)进行检查。 + +有的情况下,这两条日志都成功打印,但程序并没有正常工作,可能还伴随着更多的ERROR日志。一般来说,这是因为Orion Client不能正常与Orion Server交互。用户可以检查: + +* Orion Server启动时配置文件`/etc/orion/server.conf`里的`bind_addr`是否可以从Orion Client访问。如果地址没有问题,需要检查防火墙是否暴露9960-9961端口。 +* Orion Server模式是否正确?例如是否打开/关闭KVM模式。 +* 本地容器环境,使用共享内存加速通信,用户需要检查是否根据quick-start中[使用本地Docker容器](container.md)章节的内容创建并挂载`/dev/shm/orionsock`文件。注意容器内的挂载地址需要和物理地址完全一样,即使用`-v /dev/shm/orionsock:/dev/shm/orionsock:rw`启动容器。 + +对于本地容器使用共享内存,存在一些难以发现的问题。如果用户发现应用程序hang住,CPU占有率高,可以检查: +* 是否共享内存文件在容器内外名字不一致?注意容器内的挂载地址需要和物理地址完全一样,即使用`-v /dev/shm/orionsock:/dev/shm/orionsock:rw`启动容器。 +* 是否多个容器将同一块共享内存挂载进内部? +* 用户是否有手动删除、覆盖、修改`/dev/shm`目录下的某块`orionsock`共享内存? + +如果是以上问题,解决后需要重启Orion Server才可以生效。 + +Orion Client的安装设置问题,用户可以重新参考quick-start中[使用本地Docker容器](container.md)章节。防火墙问题可以参考[本附录相应小节](#firewall)。 + +更全面的常见问题解答,用户可以参考用户手册[相关章节](../Orion-User-Guide.md#常见问题)。 diff --git a/doc/quick-start/container.md b/doc/quick-start/container.md new file mode 100755 index 0000000..1beefaf --- /dev/null +++ b/doc/quick-start/container.md @@ -0,0 +1,308 @@ +# 场景一:Docker 容器中使用本地节点GPU资源 + +本章中我们用到的节点配置如下: +* 单台工作站,配备一张NVIDIA GTX 1080Ti显卡,显存11GB +* Ubuntu 16.04 LTS +* Docker CE 18.09 + +完成Orion vGPU软件部署后,我们将在普通的Docker容器(没有将物理GPU直通进容器内部,不依赖于`nvidia-docker`)中启动 Juypter Notebook,运行TensorFlow1.12,使用Orion vGPU资源进行 pix2pix 模型训练与推理。 + +
+ +![Docker](./figures/arch-docker.png) + +
+ +进入后续步骤之前,我们假设: +* Orion Server已经根据[Orion Server安装部署](README.md#server)小节成功安装 +* Orion Controller已经根据[Orion Controller安装部署](README.md#controller)小节安装并正常启动 + +## **Orion Server 配置与启动** +正如[Orion Server服务配置](README.md#server-config)中所介绍的,我们需要配置`/etc/orion/server.conf`文件中的两类属性: + +* Orion Server所接受的与Client通信的数据通路,即`bind_addr` +* Orion Server运行的模式 + +### Orion Server数据通路设置 + +属性`bind_addr`指Orion Server所接受的数据通路,Client必须要能访问这一地址。对于本地容器环境来说,最简单的方法是在`docker run`的时候带上`--net host`参数,使得容器内部和物理机共享网络环境。此时,`bind_addr`使用默认的`127.0.0.1`即可。 + +当然,这样的便利性是建立在牺牲容器和操作系统之间网络隔离的基础上。感兴趣的读者可以参考本场景最后一小节[使用独立Docker子网](#docker-native),在不使用`--net host`参数的情况下在容器中使用Orion vGPU资源。 + +### Orion Server模式设置 + +对于本地虚拟化环境,性能最佳的选择是通过共享内存来实现数据传输。为此,我们需要将`enable_shm`设置为`true`,并将`enable_rdma`设为`false`。 + +此外,由于我们是容器环境,所以`enable_kvm`应当设为`false`。 + +### 启动Orion Server + +根据上述讨论,`/etc/orion/server.conf`文件中的`[server]`小节参考配置如下(即默认值): + +```bash +[server] + listen_port = 9960 + bind_addr = 127.0.0.1 + enable_shm = "true" + enable_rdma = "false" + enable_kvm = "false +``` + +更改了配置文件后,我们启动/重启Orion Server服务: + +```bash +systemctl restart oriond +``` + +此时,我们可以使用`orion-check`工具检查Orion vGPU软件的状态: + +```bash +orion-check runtime server +``` + +```bash +# (omit output) +Checking Orion Server status ... +Orion Server is running with Linux user : root +Orion Server is running with command line : /usr/bin/oriond +Enable SHM [Yes] +Enable RDMA [No] +Enable Local QEMU-KVM with SHM [No] +Binding IP Address : 127.0.0.1 +Listening Port : 9960 + +Testing the Orion Server network ... +Orion Server can be reached through 127.0.0.1:9960 +# (omit output) +Orion Controller addrress is set as 127.0.0.1:9123 in configuration file. Using this address to diagnose Orion Controller +Address 127.0.0.1:9123 is reached. +Orion Controller Version Infomation : api_version=0.1,data_version=0.1 +There are 4 vGPU under managered by Orion Controller. 4 vGPU are free now. +``` + +正常情况下,每块物理GPU会被虚拟化为4块Orion vGPU,它们都应该处于可用的状态。 + + +## **创建用于通信的共享内存** + +为了Orion Server和容器中的Client应用能够通过共享内存加速数据传输,我们需要在启动容器之前创建一块共享内存,在后续`docker run`时通过`-v`参数挂载到容器中。 + +```bash +orion-shm +``` + +上述命令会在`/dev/shm`目录下创建一块`128MB`的共享内存`/dev/shm/orionsock0`,可以通过`ls /dev/shm/`命令检查。 + +需要注意的是: + +* 如果删除或覆盖了已经被使用过的`/dev/shm/orionsock` 共享内存,一定要重启Orion Server服务; + +* 如果同时运行多个container,每个container需要挂载单独的`/dev/shm/orionsock`共享内存。这些共享内存可以通过`orion-shm -i `来分别创建。 + +## **Orion Client容器启动** + +### 获取带有Orion Client运行时环境的容器 + +我们提供配置好Orion Client runtime的预先安装好官方原生TensorFlow 1.12的Docker镜像,以Python3.5版本为例: + +```bash +docker pull virtaitech/orion-client:tf1.12-py3 +``` + +有兴趣的读者可以参考我们的[Dockerfile](#)来构建自己的容器。 + +### Orion Client端参数配置 + +对于Orion Client端容器来说,需要设置以下环境变量: +* `ORION_CONTROLLER=:9123`: Orion Client向Orion Controller申请Orion vGPU资源时发送RESTful API的网络地址。在本场景中,容器共享操作系统网络,所以`controller_ip`可以设置为`127.0.0.1`即可。 +* `ORION_VGPU`:容器中每个进程申请的Orion vGPU数目,默认情况下,最多可以申请4倍物理GPU数目的vGPU。本例中我们设置`ORION_VGPU=1`。 +* `ORION_GMEM`:申请的每个Orion vGPU所能使用的显存数目(单位:MB)。由于我们使用一张GTX 1080Ti显卡,显存上限为11G,我们设置`ORION_GMEM=10500`。 + +### 启动容器 + +如上文所述,我们在用`docker run`命令启动容器时,需要用`-e`参数设置上一节介绍的环境变量,并用`-v`参数将创建的`/dev/shm/orionsock0`共享内存挂载到容器内的`/dev/shm`目录下。为了方便运行Jupyter Notebook训练TensorFlow官方提供的`pix2pix`模型例子,我们假设执行`docker run`的目录下已经用 + +```bash +git clone https://github.com/tensorflow/tensorflow.git +``` + +将TF的repo克隆到本地。我们在运行容器时也同时将TF的repo挂载到容器内部。 + +```bash +docker run -it --rm \ + -v /dev/shm/orionsock0:/dev/shm/orionsock0:rw \ + -v $(pwd)/tensorflow:/root/tensorflow \ + --net host \ + -e ORION_CONTROLLER=127.0.0.1:9123 \ + -e ORION_VGPU=1 \ + -e ORION_GMEM=10500 \ + virtaitech/orion-client:tf1.12-py3 +``` + +读者可以在容器中通过`ls /dev | grep nvidia`确认容器中没有挂载NVIDIA显卡设备。 + +在运行Jupyter Notebook之前,我们可以用`orion-check`工具检查Orion Client容器内部是否能正常与Orion Controller通信: + +```bash +# From inside Orion Client container +orion-check runtime client +``` + +正常情况下,输出应该是: + +```bash +# (omit output) +Environment variable ORION_CONTROLLER is set as 127.0.0.1:9123 Using this address to diagnose Orion Controller. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 4 vGPU under managered by Orion Controller. 4 vGPU are free now. +``` + +这样的输出说明Orion Client容器内部应用可以向Orion Controller申请资源。否则,用户应该先根据[Orion Controller安装部署](README.md#controller)章节检查Orion Controller的状态,再根据上文检查是否设置了正确的`ORION_CONTROLLER=:9123`。 + +## **运行Jupyter Notebook** + +假定tensorflow文件夹已经被挂载进容器内部。 + +```bash +# From inside Orion Client container +cd tensorflow/tensorflow/contrib/eager/python/examples/ +jupyter notebook --no-browser --allow-root +``` + +会看到如下的输出: + +```bash +To access the notebook, open this file in a browser: + file:///root/.local/share/jupyter/runtime/nbserver-26-open.html +Or copy and paste one of these URLs: + http://localhost:8888/?token= +``` + +如果用户可以使用这台机器的图形界面,那么可以打开浏览器,输入上述地址; + +否则,可以在有图形界面(能打开浏览器)的本地上进行SSH端口转发: + +```bash +ssh -Nf -L 8888:localhost:8888 +``` + +然后在本地浏览器里面输入地址访问Jupyter Notebook + +![Jupyter](./figures/pix2pix/jupyter.png) + + +## **使用TensorFlow 1.12 Eager Execution模式进行 pix2pix 模型训练与推理** + +进入`pix2pix`目录,打开`pix2pix_eager.ipynb`,一路`shift+enter`执行每一个cell,就可以看到模型训练的情况: + +![train-epoch-2](./figures/pix2pix/train-epoch-2.png) + +训练的过程中,我们可以在容器外部通过`nvidia-smi`监视物理GPU的使用情况: + +![nvidia-smi](./figures/pix2pix/nvidia-smi.png) + +从图中可以看到,真正使用物理GPU的进程是Orion Server的进程`oriond`,而不是容器中正在执行TensorFlow训练任务的Python脚本。这是因为容器中的应用程序使用的是Orion vGPU资源,对物理GPU的访问完全由Orion Server所接管。 + +训练完200个epochs以后,可以在测试集上运行模型: + +![inference](./figures/pix2pix/inference.png) + +如果运行有异常,用户可以参考[附录相应小节](appendix.md#trouble-client)进行检查。 + +## (可选)使用独立的Docker子网 + +本小节中,我们展示当容器使用独立的Docker子网时应当如何设置各项参数。我们假定读者已经熟悉并成功完成了本章前面介绍的,当容器使用`--net host`参数启动时使用Orion vGPU的整套流程,因此在本小节中我们只列出与前文不同之处。 + +### Orion Server 数据通路设置 + +设置数据通路`bind_addr`的关键,在于确保Orion Client端可以通过这一地址与Orion Server进行数据交互。对于现在的场景,在容器内的应用默认只能访问Docker子网,所以我们需要把`bind_addr`设为Docker子网的网关(gateway)。 + +我们在物理机上运行`ifconfig`以检查网络设置。一般来说,Docker默认安装后建立的子网为`docker0`: + +```bash +docker0 Link encap:Ethernet HWaddr 02:42:46:9f:27:13 + inet addr:172.17.0.1 Bcast:172.17.255.255 Mask:255.255.0.0 + inet6 addr: fe80::42:46ff:fe9f:2713/64 Scope:Link + UP BROADCAST MULTICAST MTU:1500 Metric:1 + RX packets:416541 errors:0 dropped:0 overruns:0 frame:0 + TX packets:652846 errors:0 dropped:0 overruns:0 carrier:0 + collisions:0 txqueuelen:0 + RX bytes:24865042 (24.8 MB) TX bytes:3116550526 (3.1 GB) +``` + +因此,我们应当设置`bind_addr`为`docker0`子网的网关`172.17.0.1`。 + +### Orion Server 参数配置示例 + +更新后的`/etc/orion/server.conf`文件中的`[server]`小节参考配置如下: + +```bash +[server] + listen_port = 9960 + bind_addr = 172.17.0.1 + enable_shm = "true" + enable_rdma = "false" + enable_kvm = "false +``` + +需要注意的是,若`oriond`之前已经启动,为了使配置文件生效,需要重启`oriond`服务: + +```bash +systemctl restart oriond +``` + +### Orion Client 参数设置 + +这里我们将Orion Controller地址设置为Docker子网网关地址:`ORION_CONTROLLER=172.17.0.1:9123`,从而确保容器中的应用程序向Orion Controller发送的资源请求可以被Orion Controller收到。 + +### 运行容器 + +除了上述修改外,为了容器外部可以在浏览器中访问Jupyter Notebook,我们还需要将`8888`端口通过`-p 8888:8888`暴露到外部。最终启动容器的命令为: + +```bash +docker run -it --rm \ + -v /dev/shm/orionsock0:/dev/shm/orionsock0:rw \ + -v $(pwd)/tensorflow:/root/tensorflow \ + -p 8888:8888 \ + -e ORION_CONTROLLER=172.17.0.1:9123 \ + -e ORION_VGPU=1 \ + -e ORION_GMEM=10500 \ + virtaitech/orion-client:tf1.12-py3 +``` + +同样,我们需要在容器内部检查是否能向Orion Controller申请资源: + +```bash +# From inside Orion Client container +orion-check runtime client +``` + +正常情况下,输出应该是: + +```bash +# (omit output) +Environment variable ORION_CONTROLLER is set as 172.17.0.1:9123 Using this address to diagnose Orion Controller. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 4 vGPU under managered by Orion Controller. 4 vGPU are free now. +``` + +这样的输出说明Orion Client容器内部应用可以向Orion Controller申请资源。否则,用户应该先根据[Orion Controller安装部署](README.md#controller)章节检查Orion Controller的状态,再根据上文检查是否设置了正确的`ORION_CONTROLLER=:9123`。 + +注:在使用独立Docker子网时,可能会遇到防火墙相关问题,读者可以参考附录[防火墙设置](appendix.md#firewall)小节的内容进行检查和设置。 + +## **运行Jupyter Notebook** + +假定tensorflow文件夹已经被挂载进容器内部。此时,我们运行Juypter Notebook时要显式指定`--ip=0.0.0.0`, + +```bash +cd tensorflow/tensorflow/contrib/eager/python/examples/ +jupyter notebook --ip=0.0.0.0 --no-browser --allow-root +``` + +然后再在有图形界面的节点(例如Laptop)上通过SSH端口转发, + +```bash +ssh -Nf -L 8888:localhost:8888 +``` + +从而最终可以在浏览器中通过`localhost:8888/?token=`访问Jupyter Notebook。 \ No newline at end of file diff --git a/doc/quick-start/figures/arch-docker.png b/doc/quick-start/figures/arch-docker.png new file mode 100755 index 0000000000000000000000000000000000000000..8410bdeb04e9f51b40815c59618d2f54824119c2 GIT binary patch literal 43152 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+我们以安装ubuntu16.04操作系统的一台虚拟机`ubuntu-client0`作为Orion Client。这台虚拟机既没有将物理机上的显卡以直通(Passthrough)的方式穿透进来,也没有安装NVIDIA驱动或CUDA组件。我们安装了必要的Python3库,以及TensorFlow 1.12 GPU版本: + +```bash +# From inside VM +sudo apt install python3-dev python3-pip +sudo pip3 install tensorflow-gpu==1.12.0 +``` + +由于虚拟机中不能访问GPU,也没有NVIDIA的软件环境,TensorFlow目前是无法使用的。在配置好Orion vGPU软件后,虚拟机中的TensorFlow就可以使用Orion vGPU进行模型的训练与推理。 + +完成Orion vGPU软件部署后,我们将在`ubuntu-client0`中运行TensorFlow官方的CIFAR10_Estimator示例,使用两块Orion vGPU (分别位于两块物理GPU上)进行模型训练与推理。 + +

    + +![KVM](./figures/arch-kvm.png) + +
    + +进入后续步骤之前,我们假设: +* Orion Server已经根据[Orion Server安装部署](README.md#server)小节成功安装 +* Orion Controller已经根据[Orion Controller安装部署](README.md#controller)小节安装并正常启动 + + +## **Orion Server 配置启动** + +在启动Orion Server服务之前,我们需要修改配置文件,设置数据通路,并打开Orion Server对KVM的支持。 + +### 数据通路设置 + +属性`bind_addr`指Orion Server所接受的数据通路,Client必须要能访问这一地址。对于KVM虚拟机来说,我们需要将其设置为KVM虚拟机网络的网关地址。 + +我们使用`virsh`查看KVM虚拟机的网络配置: + +```bash +# From host OS +sudo virsh domifaddr ubuntu-client0 +``` + + +```bash + Name MAC address Protocol Address +------------------------------------------------------------------------------- + vnet1 52:54:00:04:82:10 ipv4 33.31.0.10/24 +``` + +可以看到,当前KVM虚拟机的IP地址为`33.31.0.10`,因此应该设置`bind_addr=33.31.0.1`。 + +附:如果KVM虚拟机在多个虚拟子网内,例如: + +```bash + Name MAC address Protocol Address +------------------------------------------------------------------------------- + vnet0 52:54:00:04:82:10 ipv4 33.31.0.10/24 + vnet1 52:54:00:c5:43:10 ipv4 33.32.0.10/24 +``` + +选择任意一个子网的网关作为`bind_addr`均可。 + +### Orion Server模式设置 + +本场景中,我们仍然选择本地共享内存来加速数据传输,因此需要设置`enable_shm=true`,`enable_rdma=false`。此外,我们要显式启用Orion vGPU软件对KVM虚拟机的支持,即设置`enable_kvm=true`。 + +### Orion Server 参数配置示例 +本场景中,`/etc/orion/server.conf`的第一小节内容应该配置为 +```bash +[server] + listen_port = 9960 + bind_addr = 33.31.0.1 + enable_shm = "true" + enable_rdma = "false" + enable_kvm = "true" +``` + +### 启动Orion Server +我们需要重启Orion Server使新配置生效,并通过`orion-check`工具进一步确认Orion Server和Orion Controller可以正常交互: + +```bash +# From host OS +sudo systemctl restart oriond +sudo orion-check runtime server +``` + +正常的输出类似下面所示: + +``` bash +Searching NVIDIA GPU ... +CUDA driver 418.67 +418.67 is installed. +2 NVIDIA GPUs are found : + 0 : Tesla V100-PCIE-16GB + 1 : Tesla V100-PCIE-16GB + +Checking NVIDIA MPS ... +NVIDIA CUDA MPS is off. + +Checking Orion Server status ... +Orion Server is running with Linux user : root +Orion Server is running with command line : /usr/bin/oriond +Enable SHM [Yes] +Enable RDMA [No] +Enable Local QEMU-KVM with SHM [Yes] +Binding IP Address : 33.31.0.1 +Listening Port : 9960 + +Testing the Orion Server network ... +Orion Server can be reached through 33.31.0.1:9960 + +Checking Orion Controller status ... +[Info] Orion Controller setting may be different in different SHELL. +[Info] Environment variable ORION_CONTROLLER has the first priority. + +Orion Controller addrress is set as 127.0.0.1:9123 in configuration file. Using this address to diagnose Orion Controller +Address 127.0.0.1:9123 is reached. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 8 vGPU under managered by Orion Controller. 8 vGPU are free now. +``` + +可以看到,我们的Orion Server节点上有两块Tesla V100计算卡,Orion Controller将它们虚拟化成了一共8块Orion vGPU。 + +## 在虚拟机内安装Orion Client运行时 + +### 安装至默认路径 + +在虚拟机中,我们运行Orion Client安装包: + +```bash +# From inside VM +sudo ./install-client +``` + +此时,用户没有指定安装路径,安装包会询问是否将Orion Client运行时安装到默认路径`/usr/lib/orion`下。得到用户许可后,安装包会通过`ldconfig`机制将Orion Client运行时添加到操作系统动态库搜索路径。 + +```bash +Orion client environment will be installed to /usr/lib/orion +Do you want to continue [n/y] ?y + +Configuration file is generated to /etc/orion/client.conf +Please edit the "controller_addr" setting and make it point to the controller address in your environment. + +Orion vGPU client environment has been installed in /usr/lib/orion +To run application with Orion vGPU environment, please make sure Orion environment is loaded. e.g. +export LD_LIBRARY_PATH=/usr/lib/orion:$LD_LIBRARY_PATH +``` + +由于安装包已经配置搜索路径,这里屏幕提示的`export LD_LIBRARY_PATH=:$LD_LIBRARY_PATH`不是必需的。 + +### (可选)安装至自定义路径 +以安装到`/orion`为例: + +```bash +# From inside VM +INSTALLATION_PATH=/orion +sudo mkdir -p $INSTALLATION_PATH +sudo ./install-client -d $INSTALLATION_PATH +``` +这种情形下,安装包会直接将Orion Client运行时安装到用户指定的`INSTALLATION_PATH=/orion`路径下,并向屏幕输出下列提示: + +```bash +Configuration file is generated to /etc/orion/client.conf +Please edit the "controller_addr" setting and make it point to the controller address in your environment. + +Orion vGPU client environment has been installed in /orion +To run application with Orion vGPU environment, please make sure Orion environment is loaded. e.g. +export LD_LIBRARY_PATH=/orion:$LD_LIBRARY_PATH +``` + +用户在terminal内运行应用程序之前,一定要保证Orion Client运行时在操作系统动态库搜索路径中: + +```bash +# From current working terminal inside VM +export LD_LIBRARY_PATH=/usr/local/orion:$LD_LIBRARY_PATH +``` + +注意这条命令只对当前terminal生效。为方便起见,用户可以将上述语句加至`~/.bashrc`的最后一行,然后用`source ~/.bashrc`使其生效,此后登录虚拟机便不需要反复设置。 + + +## Orion Client参数配置 + +正如[使用Docker容器](#container.md)中所介绍的,Orion Client端需要向Orion Controller发送对Orion vGPU资源的申请。对于容器环境,我们是通过在启动容器时用`ORION_CONTROLLER=:9123`环境变量设置Orion Controller的地址。对于KVM虚拟机来说,我们可以更改`/etc/orion/client.conf`来达到参数配置的目的。 + +由于Orion Controller监听在物理机上的`0.0.0.0:9123`上,我们将`controller_addr`设置为虚拟机子网网关地址即可: + +```bash +[controller] + controller_addr = 33.31.0.1:9123 +``` + +设置完后,我们用`orion-check`工具检查状态: + +```bash +# From inside VM +orion-check runtime client +``` + +如果Orion Client虚拟机内部可以连接到Orion Controller,输出为: + +```bash +# (omit output) +Orion Controller addrress is set as 33.31.0.1:9123 in configuration file. Using this address to diagnose Orion Controller +Address 33.31.0.1:9123 is reached. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 8 vGPU under managered by Orion Controller. 8 vGPU are free now. +``` + +## 运行TF官方CIFAR10_Estimator示例 + +在运行应用程序之前,我们用环境变量指定应用程序向Orion Controller申请的Orion vGPU数目与显存: + +```bash +export ORION_VGPU=2 +export ORION_GMEM=12000 +``` + +我们的每一块Tesla V100计算卡有16GB显存,因此如果用户将`ORION_GMEM`设置得少于8GB,两块Orion vGPU会被调度到同一块物理GPU上。这里我们设置Orion vGPU的显存为12000MB,那么两块Orion vGPU将分别调度到两块物理GPU上,方便我们展示双卡的模型训练。 + +下面我们使用TensorFlow官方的CIFAR10 Estimator例子展示模型的训练与推理: +https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10_estimator/README.md + +首先,我们`git clone`TF官方模型repo: + +```bash +# From inside VM +git clone https://github.com/tensorflow/models +``` + +然后进入CIFAR10 Estimator文件夹内 + +```bash +cd models/tutorials/image/cifar10_estimator/ +``` + +读者第一次训练模型前,需要下载CIFAR10数据集并转换为TFRecord格式: + +```bash +mkdir data +python3 generate_cifar10_tfrecords.py --data-dir ./data +``` + +处理好后,`data`目录应该包括以下内容,共520MB: + +```bash +user@ubuntu-client0:~/models/tutorials/image/cifar10_estimator/data$ ls +cifar-10-batches-py cifar-10-python.tar.gz eval.tfrecords train.tfrecords validation.tfrecords +``` + +下面我们使用两块Orion vGPU进行模型训练,每块Orion vGPU上的batch_size设为128,总共256: + +```bash +python3 cifar10_main.py \ + --data-dir=${PWD}/data \ + --job-dir=/tmp/cifar10 \ + --variable-strategy=GPU \ + --num-gpus=2 \ + --train-steps=10000 \ + --train-batch-size=256 \ + --learning-rate=0.1 +``` + +TensorFlow打印的日志如下: + +```bash +VirtaiTech Resource. Build-cuda-7675815-20190624_081551 +2019-06-25 15:43:43.493814: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2019-06-25 15:43:43.493882: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: +name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38 +pciBusID: 0000:00:09.0 +totalMemory: 11.72GiB freeMemory: 11.72GiB +2019-06-25 15:43:43.604945: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2019-06-25 15:43:43.605002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 1 with properties: +name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38 +pciBusID: 0000:00:09.0 +totalMemory: 11.72GiB freeMemory: 11.72GiB +2019-06-25 15:43:43.606527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0, 1 +2019-06-25 15:43:43.606568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix: +2019-06-25 15:43:43.606577: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0 1 +2019-06-25 15:43:43.606582: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N Y +2019-06-25 15:43:43.606589: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 1: Y N +2019-06-25 15:43:43.606657: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 11400 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:00:09.0, compute capability: 7.0) +2019-06-25 15:43:43.607202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:1 with 11400 MB memory) -> physical GPU (device: 1, name: Tesla V100-PCIE-16GB, pci bus id: 0000:00:09.0, compute capability: 7.0) +# (omit output) +INFO:tensorflow:global_step/sec: 14.2797 +INFO:tensorflow:loss = 0.48649728, step = 9900 (7.003 sec) +INFO:tensorflow:learning_rate = 0.1, loss = 0.48649728 (7.003 sec) +INFO:tensorflow:Average examples/sec: 3639.99 (4009.58), step = 9900 +INFO:tensorflow:Average examples/sec: 3640.07 (3717.26), step = 9910 +INFO:tensorflow:Average examples/sec: 3640.09 (3655.01), step = 9920 +INFO:tensorflow:Average examples/sec: 3640.31 (3873.63), step = 9930 +INFO:tensorflow:Average examples/sec: 3640.45 (3788.08), step = 9940 +INFO:tensorflow:Average examples/sec: 3640.79 (4017.58), step = 9950 +INFO:tensorflow:Average examples/sec: 3641.19 (4089.74), step = 9960 +INFO:tensorflow:Average examples/sec: 3641.23 (3679.08), step = 9970 +INFO:tensorflow:Average examples/sec: 3641.43 (3847.37), step = 9980 +INFO:tensorflow:Average examples/sec: 3641.4 (3615.53), step = 9990 +INFO:tensorflow:Saving checkpoints for 10000 into /tmp/cifar10/model.ckpt. +INFO:tensorflow:Loss for final step: 0.46667284. +# (omit output) +INFO:tensorflow:Evaluation [100/100] +INFO:tensorflow:Finished evaluation at 2019-06-25-08:06:14 +INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.7628, global_step = 10000, loss = 1.0683168 +INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/cifar10/model.ckpt-10000 +2019-06-25 16:06:15 [INFO] Client exits with allocation ID fda38164-711b-4809-9984-2759a3a2165b +``` + +从日志中可以看到: + +* 应用程序启动时,Orion Client运行时会打印日志`VirtaiTech Resource. Build-cuda-xxx`。这一行日志说明应用程序成功加载了Orion Client运行时。 +* 应用程序退出时,Orion Client运行时会打印日志`Client exits with allocation ID xxx`。这一行日志说明应用程序在生命周期里成功向Orion Controller申请到了Orion vGPU资源,退出时释放这一资源。 +* TensorFlow启动时识别出了两块GPU,显存各自为11.72GB (对应于我们设置的`ORION_GMEM=12000`) + +模型训练的过程中,我们在物理机操作系统中运行`nvidia-smi`查看物理GPU使用情况: + +![cifar10-nvidia-smi](./figures/cifar10/nvidia-smi.png) + +从结果中可以看出: + +* 对物理GPU的访问被Orion Server进程`oriond`完全接管 +* 两块Orion vGPU被调度到了两块物理GPU上 +* 我们限制了Orion vGPU对显存的占用 + +如果运行有异常,用户可以参考[附录相应小节](appendix.md#trouble-client)进行检查。 \ No newline at end of file diff --git a/doc/quick-start/remote_rdma.md b/doc/quick-start/remote_rdma.md new file mode 100755 index 0000000..c3c7d3f --- /dev/null +++ b/doc/quick-start/remote_rdma.md @@ -0,0 +1,391 @@ +# 场景三:在没有GPU的节点上使用远程节点GPU资源 + +本章中我们用到的环境配置如下: +* 两台服务器,其中一台(`server1`)配备两张NVIDIA Tesla V100计算卡,每张16GB显存;另一台(`server0`)没有配备物理GPU +* 两台服务器上均配备Mellanox ConnectX-5 25Gb网卡,通过光交换机互相连接 +* MLNX_OFED_LINUX04.5.2驱动及用户库 +* Ubuntu Server 16.04 LTS + +我们将Orion Server和Orion Controller均部署在配备有物理GPU的`server1`上,将`server0`作为Orion Client。我们在`server0`上安装了必要的Python3库以及TensorFlow 1.12 GPU版本: + +```bash +# On server0 (which has no GPU) +sudo apt install python3-dev python3-pip +sudo pip3 install tensorflow-gpu==1.12.0 +``` + +`server0`上没有物理GPU,也没有NVIDIA的软件环境,因此TensorFlow并不能使用GPU加速训练。一旦Orion vGPU软件部署完成,我们就可以在`server0`上运行TensorFlow通过Orion vGPU加速模型训练。我们会将TensorFlow official benchmark运行在随机生成数据、真实Imagenet数据两种场景下。 + +
    + +![RDMA](./figures/arch-rdma.png) + +
    + +进入后续步骤之前,我们假设: +* Orion Server已经根据[Orion Server安装部署](README.md#server)小节成功安装在`server1`上 +* Orion Controller已经根据[Orion Controller安装部署](README.md#controller)小节安装在`server1`上并正常启动 + +## Orion Server 参数配置 + +在启动`server1`上的Orion Server服务前,我们需要修改配置文件,设置数据通路使得`server0`上的Orion Client可以连接到Orion Server;此外,我们需要关闭默认的共享内存模式,打开RDMA通道。 + +### 数据通路设置 + +由于我们要使用RDMA加速数据交换,我们应该将`bind_addr`设置为Orion Client在RDMA网段可以访问的地址。以本场景环境为例,两台服务器节点所在的RDMA网段为`192.168.25.xxx`,因此我们将`bind_addr`设置为Orion Server所在`server1`的RDMA网段地址`192.168.25.21`。 + +### Orion Server模式设置 +我们选择RDMA加速数据传输,因此需要设置`enable_shm=false`,`enable_rdma=true`。此外,我们的Orion Client并非本地KVM虚拟机,因此我们要设置`enable_kvm=false`。 + +### Orion Server 参数配置示例 +本场景中,`/etc/orion/server.conf`的第一小节内容应该配置为 +```bash +[server] + listen_port = 9960 + bind_addr = 192.168.25.21 + enable_shm = "false" + enable_rdma = "true" + enable_kvm = "false" +``` + +### 启动Orion Server +我们需要重启Orion Server使新配置生效,并检查状态: + +```bash +# From server1 +sudo systemctl restart oriond + +sudo orion-check runtime server +``` + +正常的输出如下: + +```bash +Searching NVIDIA GPU ... +CUDA driver 418.67 +418.67 is installed. +2 NVIDIA GPUs are found : + 0 : Tesla V100-PCIE-16GB + 1 : Tesla V100-PCIE-16GB + +Checking NVIDIA MPS ... +NVIDIA CUDA MPS is off. + +Checking Orion Server status ... +Orion Server is running with Linux user : root +Orion Server is running with command line : /usr/bin/oriond +Enable SHM [No] +Enable RDMA [Yes] +Enable Local QEMU-KVM with SHM [No] +Binding IP Address : 192.168.25.21 +Listening Port : 9960 + +Testing the Orion Server network ... +Orion Server can be reached through 192.168.25.21:9960 + +Checking Orion Controller status ... +[Info] Orion Controller setting may be different in different SHELL. +[Info] Environment variable ORION_CONTROLLER has the first priority. + +Orion Controller addrress is set as 127.0.0.1:9123 in configuration file. Using this address to diagnose Orion Controller +Address 127.0.0.1:9123 is reached. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 8 vGPU under managered by Orion Controller. 8 vGPU are free now. +``` + +表明Orion Controller将两块物理GPU一共虚拟成了8块Orion vGPU,目前均处于可用状态。 + +## 安装Orion Client运行时 + +### 安装至默认路径 + +在`server0`中,我们运行Orion Client安装包: + +```bash +# From inside server0 +sudo ./install-client +``` + +此时,用户没有指定安装路径,安装包会询问是否将Orion Client运行时安装到默认路径`/usr/lib/orion`下。得到用户许可后,安装包会通过`ldconfig`机制将Orion Client运行时添加到操作系统动态库搜索路径。 + +```bash +Orion client environment will be installed to /usr/lib/orion +Do you want to continue [n/y] ?y + +Configuration file is generated to /etc/orion/client.conf +Please edit the "controller_addr" setting and make it point to the controller address in your environment. + +Orion vGPU client environment has been installed in /usr/lib/orion +To run application with Orion vGPU environment, please make sure Orion environment is loaded. e.g. +export LD_LIBRARY_PATH=/usr/lib/orion:$LD_LIBRARY_PATH +``` + +由于安装包已经配置搜索路径,这里屏幕提示的`export LD_LIBRARY_PATH=:$LD_LIBRARY_PATH`不是必需的。 + +### (可选)安装至自定义路径 +以安装到`/orion`为例: + +```bash +# From inside server0 +INSTALLATION_PATH=/orion +sudo mkdir -p $INSTALLATION_PATH +sudo ./install-client -d $INSTALLATION_PATH +``` +这种情形下,安装包会直接将Orion Client运行时安装到用户指定的`INSTALLATION_PATH=/orion`路径下,并向屏幕输出下列提示: + +```bash +Configuration file is generated to /etc/orion/client.conf +Please edit the "controller_addr" setting and make it point to the controller address in your environment. + +Orion vGPU client environment has been installed in /orion +To run application with Orion vGPU environment, please make sure Orion environment is loaded. e.g. +export LD_LIBRARY_PATH=/orion:$LD_LIBRARY_PATH +``` + +用户在terminal内运行应用程序之前,一定要保证Orion Client运行时在操作系统动态库搜索路径中: + +```bash +# From current working terminal inside server0 +export LD_LIBRARY_PATH=/usr/local/orion:$LD_LIBRARY_PATH +``` + +注意这条命令只对当前terminal生效。为方便起见,用户可以将上述语句加至`~/.bashrc`的最后一行,然后用`source ~/.bashrc`使其生效,此后以当前用户身份登录`server0`不需要反复设置。 + + +## Orion Client参数配置 + +Orion Client端需要向Orion Controller发送对Orion vGPU资源的申请。我们可以更改`/etc/orion/client.conf`来达到参数配置的目的。 + +由于Orion Controller监听在`server1`上的`0.0.0.0:9123`上,我们将`controller_addr`设置为`server1`的任意一个从`server0`能够访问的IP地址即可。此处我们可以依旧使用RDMA网段的地址`192.168.25.21`,或者使用`server1`的一个TCP地址`10.10.1.21`。我们选择后者作为示范。 + +```bash +[controller] + controller_addr = 10.10.1.21:9123 +``` + +设置完后,我们用`orion-check`工具检查状态: + +```bash +# From inside server0 +orion-check runtime client +``` + +如果Orion Client `server0`内部可以连接到Orion Controller,输出为: + +```bash +# (omit output) +Orion Controller addrress is set as 10.10.1.21:9123 in configuration file. Using this address to diagnose Orion Controller +Address 10.10.1.21:9123 is reached. +Orion Controller Version Infomation : data_version=0.1,api_version=0.1 +There are 8 vGPU under managered by Orion Controller. 8 vGPU are free now. +``` + +## 运行TF Official Benchmark +在`server0`上运行应用程序之前,我们用环境变量指定应用程序向Orion Controller申请的Orion vGPU数目与显存: + +```bash +export ORION_VGPU=2 +export ORION_GMEM=15500 +``` + +我们的每一块Tesla V100计算卡有16GB显存,因此如果用户将`ORION_GMEM`设置得少于8GB,两块Orion vGPU会被调度到同一块物理GPU上。这里我们设置Orion vGPU的显存为15500MB,那么两块Orion vGPU将分别调度到两块物理GPU上,方便我们展示双卡的模型训练。 + +首先,我们将TF official benchmark repo克隆下来: + +```bash +# From inside server0 +git clone --branch=cnn_tf_v1.12_compatible https://github.com/tensorflow/benchmarks.git +``` + +TF official benchmark支持两种运行模式:随机生成数据,或者用转换为TFRecord格式的Imagenet数据集。我们分别介绍这两种情形。 + +### 使用随机生成数据(Synthetic data) + +下面的代码会使用两块Orion vGPU训练inception_v3模型,每块vGPU上的batch_size=128, 总batch_size为256。 + +对于随机数据来说,不可能提升训练精度,因此我们只训练500个batch即可: + +```bash +python3 ./benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \ + --data_name=imagenet \ + --model=inception3 \ + --optimizer=rmsprop \ + --num_batches=500 \ + --num_gpus=2 \ + --batch_size=128 +``` +输出为 +```bash +VirtaiTech Resource. Build-cuda-7675815-20190624_081551 +2019-06-25 19:55:37.099719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: +name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38 +pciBusID: 0000:d9:00.0 +totalMemory: 15.14GiB freeMemory: 15.14GiB +2019-06-25 19:55:37.218239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 1 with properties: +name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38 +pciBusID: 0000:d9:00.0 +totalMemory: 15.14GiB freeMemory: 15.14GiB +2019-06-25 19:55:37.222562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0, 1 +2019-06-25 19:55:37.222765: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix: +2019-06-25 19:55:37.222795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0 1 +2019-06-25 19:55:37.222815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N Y +2019-06-25 19:55:37.222831: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 1: Y N +2019-06-25 19:55:37.222994: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14725 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:d9:00.0, compute capability: 7.0) +2019-06-25 19:55:37.225850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 14725 MB memory) -> physical GPU (device: 1, name: Tesla V100-PCIE-16GB, pci bus id: 0000:d9:00.0, compute capability: 7.0) +TensorFlow: 1.12 +Model: inception3 +Dataset: imagenet (synthetic) +Mode: BenchmarkMode.TRAIN +SingleSess: False +Batch size: 256 global + 128.0 per device +Num batches: 500 +Num epochs: 0.10 +Devices: ['/gpu:0', '/gpu:1'] +Data format: NCHW +Optimizer: rmsprop +Variables: parameter_server +========== +Generating training model +# (omit output) +Running warm up +Done warm up +Step Img/sec total_loss +1 images/sec: 442.6 +/- 0.0 (jitter = 0.0) 7.416 +# (omit output) +490 images/sec: 434.6 +/- 1.2 (jitter = 19.5) 7.384 +500 images/sec: 435.1 +/- 1.2 (jitter = 19.9) 7.378 +---------------------------------------------------------------- +total images/sec: 435.00 +---------------------------------------------------------------- +2019-06-25 20:01:06 [INFO] Client exits with allocation ID b928be93-0b40-4252-b6b4-291ca4c99462 +``` + +从日志中可以看到: + +* 应用程序启动时,Orion Client运行时会打印日志`VirtaiTech Resource. Build-cuda-xxx`。这一行日志说明应用程序成功加载了Orion Client运行时。 +* 应用程序退出时,Orion Client运行时会打印日志`Client exits with allocation ID xxx`。这一行日志说明应用程序在生命周期里成功向Orion Controller申请到了Orion vGPU资源,退出时释放这一资源。 +* TensorFlow启动时识别出了两块GPU,显存各自为15.14GB (对应于我们设置的`ORION_GMEM=15500`) + +模型训练的过程中,我们在物理机操作系统中运行`nvidia-smi`查看物理GPU使用情况: + +![inception3-nvidia-smi-synthetic](./figures/inception3/nvidia-smi-synthetic.png) + +从结果中可以看出: + +* 对物理GPU的访问被Orion Server进程`oriond`完全接管 +* 两块Orion vGPU被调度到了两块物理GPU上 +* 我们限制了Orion vGPU对显存的占用 + +### (可选)使用TFRecord格式的Imagenet数据集 + +首先,用户需要按照 + +https://github.com/tensorflow/models/tree/master/research/inception#getting-started + +里的步骤下载Imagenet数据集,并转换成TFRecord格式。如果原始Imagenet数据集已经存储在本地,用户可以相应地修改脚本实现本地的格式转换。转换后的TFRecord文件一共有144GB。 + +下面的命令会使用两块Orion vGPU在真实Imagenet数据集上使用`rmsprop`优化器训练inception_v3模型,每块vGPU上的batch_size=128, 总batch_size为256。我们在全部训练集上训练5个完整的epoch,训练过程中的checkpoionts存储在`./train_dir`目录。 + +```bash +export IMAGENET_DIR= +python3 ./benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \ + --data_dir=$IMAGENET_DIR \ + --data_name=imagenet \ + --print_training_accuracy=True \ + --train_dir=./train_dir \ + --save_model_steps=1000 \ + --eval_during_training_every_n_steps=5000 \ + --save_summaries_steps=1000 \ + --summary_verbosity=3 \ + --model=inception3 \ + --optimizer=rmsprop \ + --num_epochs=5 \ + --num_gpus=2 \ + --batch_size=128 +``` + +TensorFlow向屏幕输出如下的日志: + +```bash +VirtaiTech Resource. Build-cuda-7675815-20190624_081551 +# (omit output) +TensorFlow: 1.12 +Model: inception3 +Dataset: imagenet +Mode: BenchmarkMode.TRAIN_AND_EVAL +SingleSess: False +Batch size: 256 global + 128.0 per device +Num batches: 25022 +Num epochs: 5.00 +Devices: ['/gpu:0', '/gpu:1'] +Data format: NCHW +Optimizer: rmsprop +Variables: parameter_server +========== +Generating training model +# (omit output) +Running warm up +Done warm up +Step Img/sec total_loss top_1_accuracy top_5_accuracy +1 images/sec: 318.7 +/- 0.0 (jitter = 0.0) 7.415 0.004 0.004 +10 images/sec: 357.5 +/- 7.6 (jitter = 15.8) 7.364 0.000 0.000 +# (omit output) +1490 images/sec: 370.6 +/- 0.3 (jitter = 7.9) 6.736 0.008 0.043 +1500 images/sec: 370.6 +/- 0.3 (jitter = 7.9) 6.654 0.020 0.070 +# (omit output) +24990 images/sec: 368.8 +/- 0.1 (jitter = 7.8) 3.411 0.352 0.613 +25000 images/sec: 368.8 +/- 0.1 (jitter = 7.8) 3.493 0.344 0.629 +Running evaluation at global_step 25010 +# (omit output) +Accuracy @ 1 = 0.3692 Accuracy @ 5 = 0.6404 [249856 examples] +2019-06-26 01:49:26 [INFO] Client exits with allocation ID a2062e12-8199-4515-a8ec-59dfe9723b4d +``` + +可以看到`loss`不断下降,精度不断上升,最终在训练完5个epochs后达到36.92%的top-1精度,64.04%的top-5精度。训练过程中,每过5000个batch,TF会进行一次全面的evaluation。有兴趣的读者可以在`server0`上新打开terminal,运行TensorBoard从而在浏览器中监视训练过程: + +```bash +# From inside server0 +tensorboard --logdir ./train_dir +``` + +随后,在浏览器中访问`localhost:6006`即可(可能需要用ssh作端口转发,才能在有图形界面的终端访问) + +如果运行有异常,用户可以参考[附录相应小节](appendix.md#trouble-client)进行检查。 + + +## 附:确认Orion平台工作在RDMA模式下 + +如果Orion Server或者Orion Client端的RDMA驱动不能正常工作,或者Orion Server的`bind_addr`没有设置为RDMA网段的地址,Orion vGPU软件会自动切换成TCP模式进行数据传输。此时,性能会有明显下降。因此,我们需要通过Orion Server的日志来确认RDMA模式成功开启。 + +`server1`上,Orion Server的日志输出到`/var/log/orion/session`中。我们进到这个目录下,用`ll -rt`找到最新的日志文件: + +```bash +# From inside server1 +cd /var/log/orion/session +ll -rt +``` + +屏幕最下方的文件即为最新日志,我们查看它的内容: + +``` bash +3686900184038864: 2019-06-25 20:24:52 [INFO] Resource successfully confirmed with controller +3686900190516972: 2019-06-25 20:24:52 [INFO] Resource successfully confirmed with controller +3686900190589780: 2019-06-25 20:24:52 [INFO] Final virtual gpu list : 0:0:15500,0:1:15500, begin to initialize CUDA device manager +3686904167685066: 2019-06-25 20:24:54 [INFO] Registered v-GPU 0 on p-GPU 0. +3686904167700354: 2019-06-25 20:24:54 [INFO] Registered v-GPU 0 on p-GPU 1. +3686904167738402: 2019-06-25 20:24:54 [INFO] Architecture initialization is done. Resource is confirmed. +3686904331240842: 2019-06-25 20:24:54 [INFO] Client supports RDMA mode, then server runs in RDMA mode. +3686904345514000: 2019-06-25 20:24:54 [INFO] Launching workers ... +``` + +说明Orion平台工作模式为RDMA。反之,如果日志中包含 + +```bash +[INFO] Client supports TCP mode, then server also falls back to TCP mode. +``` + +说明Orion平台工作模式退化为TCP,用户需要检查RDMA环境,以及Orion Server数据通路的设置。 + +如果Orion Server启动时,配置文件中的`enable_shm`和`enable_rdma`均为`false`,则Orion vGPU软件会默认工作在TCP模式,Orion Server日志中也不会有`Client supports XXX mode...`这行日志。 \ No newline at end of file diff --git a/doc/quick-start/trouble_shooting.md b/doc/quick-start/trouble_shooting.md new file mode 100755 index 0000000..3faf7b2 --- /dev/null +++ b/doc/quick-start/trouble_shooting.md @@ -0,0 +1,37 @@ +# 常见问题与解答 + +本章节我们针对用户在阅读和使用Quick Start的过程中可能遇到的问题进行解答。更加全面的常见问题列表,读者可以参考[用户手册相关部分](../Orion-User-Guide.md#常见问题)。 + +## 安装部署常见问题 + +## 运行失败 + +VirtaiTech=>with/without allocation id + +with allocation id: fail to connect to server + +* GPU节点CUDA环境配置出错(或`deb`安装) +* 安装时未指定`CUDA_HOME`环境变量 +* Orion Controller无法连接到系统中已有`etcd`服务 +* [INFO] Client exits without allocation ID. +* Orion Server bind address出错 +* Orion Client ORION_CONTROLLER设置出错(或client.conf出错) +* Orion Client 没有设置ORION_VGPU环境变量 +* Orion Client由于防火墙设置,无法与Orion Controller和Orion Server通信 +* container内没有mount SHM +* 多个container使用了相同的SHM +* 把/dev/shm目录全mount进了容器 +* SHM被误删,而没有重启server +* Controller被杀死,重启后没有重启server +* 修改`/etc/orion/server.conf`后没有重启`server` + +## Orion Client状态检查 + +防火墙 + +## **资源分配相关错误** + +## **显存 quota 相关** + +## **MPS相关错误** + diff --git a/dockerfiles/README.md b/dockerfiles/README.md new file mode 100755 index 0000000..4d4da06 --- /dev/null +++ b/dockerfiles/README.md @@ -0,0 +1,67 @@ +# Docker镜像 + +我们准备了安装有Orion Client Runtime,以及TensorFlow,PyTorch的不同镜像。其中, +* TensorFlow 1.12直接从`pip`源安装 +* PyTorch 1.1.0从官方源码直接编译生成 +* 镜像内操作系统均为`Ubuntu 16.04` +* 在部分镜像中,我们还安装了`MNLX_OFED 4.5.1`RDMA驱动 + +此repo中的Dockerfile对应于Orion vGPU软件的官方[Docker Hub Registry](https://hub.docker.com/r/virtaitech/orion-client)。 + +需要注意的是,每个镜像对应的路径下所需要的 +* `install-client`安装包 +* MLNX_OFED 4.5-1.0.1.0驱动 +* 以及PyTorch从源码编译得到的wheel包 + +需要用户自行放置到路径下,方可成功运行`docker build`。 + +## [TensorFlow 1.12 基础镜像](./client-tf1.12-base) + +```bash +docker pull virtaitech/orion-client:tf1.12-base +``` + +此镜像中通过`pip3 install tensorflow-gpu==1.12`安装了官方TensorFlow,然后通过`install-client`安装包安装了Orion Client运行时。 + +## [TensorFlow 1.12 带MNLX驱动,Python 3.5环境](./client-tf1.12-py3) + +```bash +docker pull virtaitech/orion-client:tf1.12-py3 +``` + +此镜像中通过`pip3 install tensorflow-gpu==1.12`安装了官方TensorFlow,然后通过`install-client`安装包安装了Orion Client运行时。 + +此外,我们安装了`MNLX_OFED 4.5.1`RDMA驱动,用户如果将Mellanox的RDMA设备直通到容器内部,就可以参照quick-start文档中的[通过RDMA使用远程节点GPU资源](./quick-start/remote_rdma.md)章节内容在容器内部使用远程GPU资源。 + +为了展示的方便,我们同样安装了Juypter Notebook和部分Python packages。 + +## [TensorFlow 1.12 带MNLX驱动,Python 2.7环境](./client-tf1.12-py2) + +```bash +docker pull virtaitech/orion-client:tf1.12-py2 +``` + +此镜像中通过`pip install tensorflow-gpu==1.12`安装了官方TensorFlow,然后通过`install-client`安装包安装了Orion Client运行时。 + +此外,我们安装了`MNLX_OFED 4.5.1`RDMA驱动,用户如果将Mellanox的RDMA设备直通到容器内部,就可以参照quick-start文档中的[通过RDMA使用远程节点GPU资源](./quick-start/remote_rdma.md)章节内容在容器内部使用远程GPU资源。 + +本镜像中,我们安装了部分Python packages,以便用户使用[TensorFlow Object Detection](https://github.com/tensorflow/models/tree/master/research/object_detection)模型,以及其余[官方Models](https://github.com/tensorflow/models)。 + +## [PyTorch 1.1.0, Python 3.5环境](./client-pytorch-1.1.0-py3) + +由于PyTorch官方提供的`pip`源wheel包里面编译了太多组件,部分组件我们这一版的Orion vGPU软件还不支持,我们通过PyTorch的源码编译了1.1.0版本的wheel包。我们没有对源码进行任何修改,只是更改了编译选项。 + +我们同样从源码开始,使用默认编译选项编译了torchvision 0.3.0版本,打包进镜像。我们也安装了部分Python packages,使得用户可以直接在镜像里面运行PyTorch的官方examples:https://github.com/pytorch/examples + +最后,我们通过通过`install-client`安装包安装了Orion Client运行时。 + +我们在[PyTorch 1.10 Python3.5 镜像](./client-pytorch-1.1.0-py3)中介绍了我们编译PyTorch 1.1.0,TorchVision 0.3.0,以及安装Orion Client Runtime的步骤,用户可以参考。 + +### 注意事项 +由于PyTorch DataLoader需要通过IPC通讯,启动容器时需要通过`--shm-size=8G`参数保证DataLoader可以正常工作。这一点对于Native环境也是一样的。 + +此外,由于我们对PyTorch的支持还在持续开发中,用户需要注意的是: +* 我们还不支持PyTorch通过RDMA网络使用远程GPU资源 +* 在使用多卡训练时,需要用GLOO作为后端,而不是默认的NCCL + +在我们的[一篇技术博客](../blogposts/use-pytorch.md)里,我们介绍了如何让PyTorch使用多块Orion vGPU在Imagenet数据集上训练Resnet50模型。 \ No newline at end of file diff --git a/dockerfiles/client-pytorch-1.1.0-py3/Dockerfile b/dockerfiles/client-pytorch-1.1.0-py3/Dockerfile new file mode 100755 index 0000000..1fd112c --- /dev/null +++ b/dockerfiles/client-pytorch-1.1.0-py3/Dockerfile @@ -0,0 +1,42 @@ +FROM ubuntu:16.04 +MAINTAINER zoumao@virtaitech.com + +RUN sed -i 's/archive.ubuntu.com/mirrors.ustc.edu.cn/g' /etc/apt/sources.list + +RUN apt update -y &&\ + apt install -y libcurl4-openssl-dev &&\ + apt install -y python3-dev python3-pip &&\ + apt install -y git wget curl bc net-tools &&\ + apt install -y lsb-core &&\ + apt install -y libjpeg-dev zlib1g-dev libopenmpi-dev libomp-dev &&\ + apt clean + +# Setup pip source +COPY pip.conf /etc/ + +WORKDIR /root + +# Install PyTorch, torchvision and other python packages +COPY torch-1.1.0-cp35-cp35m-linux_x86_64.whl . +RUN pip3 install torch-1.1.0-cp35-cp35m-linux_x86_64.whl && rm torch-1.1.0-cp35-cp35m-linux_x86_64.whl +COPY requirement.txt . +RUN pip3 install -r requirement.txt && rm requirement.txt + +COPY torchvision /usr/local/lib/python3.5/dist-packages/torchvision + +# Install Orion Client runtime +ENV CUDA_HOME=/usr/local/cuda-9.0 +RUN mkdir -p $CUDA_HOME && mkdir -p $CUDA_HOME/lib64 +ENV LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH + +COPY install-client . +RUN chmod +x install-client && ./install-client -d $CUDA_HOME/lib64 -q && rm install-client + +RUN ln -sf $CUDA_HOME/lib64/liborion.so $CUDA_HOME/lib64/libnvToolsExt.so.1 &&\ + ln -sf $CUDA_HOME/lib64/liborion.so $CUDA_HOME/lib64/libnccl.so.2 + +# Set the num of Orion vGPU each process requests from Orion Controller +ENV ORION_VGPU=1 + +WORKDIR /root +CMD ["/bin/bash"] diff --git a/dockerfiles/client-pytorch-1.1.0-py3/README.md b/dockerfiles/client-pytorch-1.1.0-py3/README.md new file mode 100755 index 0000000..5733ac8 --- /dev/null +++ b/dockerfiles/client-pytorch-1.1.0-py3/README.md @@ -0,0 +1,124 @@ +# PyTorch 1.10 Python3.5 镜像 + +## 注意事项 +由于PyTorch DataLoader需要通过IPC通讯,启动容器时需要通过`--shm-size=8G`参数保证DataLoader可以正常工作。这一点对于Native环境也是一样的。 + +此外,由于我们对PyTorch的支持还在持续开发中,用户需要注意的是: +* 我们还不支持PyTorch通过RDMA网络使用远程GPU资源 +* 在使用多卡训练时,需要用GLOO作为后端,而不是默认的NCCL + +在我们的[一篇技术博客](../../blogposts/use-pytorch.md)里,我们介绍了如何让PyTorch使用多块Orion vGPU在Imagenet数据集上训练Resnet50模型。 + +如果要构建镜像,用户需要按照下面的步骤从源码编译PyTorch和TorchVision。 + +## 从源码编译PyTorch 1.1.0的Python 3.5版本 + +我们以Ubuntu 16.04环境为例。 + +首先`git clone`相应的repo,以及第三方依赖项: + +```bash +git clone --recursive https://github.com/pytorch/pytorch +cd pytorch +git checkout v1.1.0 # switch to v1.1.0 branch +``` + +如果之前已经克隆了repo,用下面命令更新第三方库 + +```bash +git checkout v1.1.0 +git submodule sync +git submodule update --init --recursive +``` + +然后,我们安装相应的依赖项: + +```bash +apt install python3-dev python3-pip cmake g++ \ + libopenmpi-dev libomp-dev libjpeg-dev zlib1g-dev + +pip3 install numpy pillow +``` + +在安装前,我们通过环境变量设置编译选项: + +```bash +export NO_TEST=1 +export NO_FBGEMM=1 +export NO_MIOPEN=1 +export NO_MKLDNN=1 +export NO_NNPACK=1 +export NO_QNNPACK=1 +export USE_STATIC_NCCL=0 +export TORCH_CUDA_ARCH_LIST="3.5;6.0;6.1;7.0" +``` + +最后,构建wheel包: + +```bash +cd pytorch +python3 setup.py bdist_wheel +``` + +用户可以在生成的`dist`目录下,找到生成的`torch-1.1.0-cp35-cp35m-linux_x86_64.whl`。 + +## 从源码编译TorchVision 0.3.0 + +最新(2019/06/29)的TorchVision 0.3.0和PyTorch 1.1.0相匹配。从源码build PyTorch之后,TorchVision也需要重新build。 + +```bash +git clone https://github.com/pytorch/vision.git +cd vision +git checkout v0.3.0 +``` + +然后用默认参数build: + +```bash +python3 setup.py build +``` + +视情况而定,用户可能需要安装`pillow`等Python库。完成后,用户可以在生成的`build/lib.linux-x86_64-3.5`目录下找到生成的`torchvision`目录: + +```bash +ls build/lib.linux-x86_64-3.5/torchvision + +_C.cpython-35m-x86_64-linux-gnu.so __init__.py ops utils.py +datasets models transforms version.py +``` + +构建Docker镜像时,只要拷贝这个目录到容器内Python3.5 dist-packages路径即可: + +```bash +COPY torchvision /usr/local/lib/python3.5/dist-packages/torchvision +``` + +## 最后步骤 + +在运行`docker build`之前,用户需要把`install-client`安装包,以及上面两步得到的PyTorch wheel包,以及TorchVision都放到Dockerfile所在路径下。 + +## 附录:安装Orion Client运行时 + +我们进一步解释Dockerfile中安装Orion Client Runtime相关的步骤。 + +PyTorch经过CMake编译后指定了RPATH。如果用户build PyTorch时,`CUDA_HOME=/usr/local/cuda-9.0`,那么容器内Orion Client运行时必须安装到这个路径下才可以支持PyTorch使用Orion vGPU。 +安装至非默认路径时,需要手动配置`LD_LIBRARY_PATH`。 + +```bash +ENV CUDA_HOME=/usr/local/cuda-9.0 +RUN mkdir -p $CUDA_HOME && mkdir -p $CUDA_HOME/lib64 +ENV LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH + +COPY install-client . +RUN chmod +x install-client && ./install-client -d $CUDA_HOME/lib64 -q && rm install-client +``` + +由于PyTorch默认还依赖`libnvToolsExt.so.1`和`libnccl.so.2`,我们需要创建软链接保证PyTorch能正常运行任务: + +```bash +RUN ln -sf $CUDA_HOME/lib64/liborion.so 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+1,3 @@ +[global] +index-url=https://pypi.doubanio.com/simple/ +trusted-host=pypi.doubanio.com diff --git a/dockerfiles/client-pytorch-1.1.0-py3/requirement.txt b/dockerfiles/client-pytorch-1.1.0-py3/requirement.txt new file mode 100755 index 0000000..a9e51d2 --- /dev/null +++ b/dockerfiles/client-pytorch-1.1.0-py3/requirement.txt @@ -0,0 +1,8 @@ +pillow +scipy==1.2.0 +matplotlib==3.0 +pandas +cython +tqdm +contextlib2 +lxml diff --git a/dockerfiles/client-tf1.12-base/Dockerfile b/dockerfiles/client-tf1.12-base/Dockerfile new file mode 100755 index 0000000..d1197cb --- /dev/null +++ b/dockerfiles/client-tf1.12-base/Dockerfile @@ -0,0 +1,37 @@ +FROM ubuntu:16.04 +MAINTAINER zoumao@virtaitech.com + +RUN sed -i 's/archive.ubuntu.com/mirrors.ustc.edu.cn/g' /etc/apt/sources.list + +RUN apt update -y &&\ + apt install -y libcurl4-openssl-dev &&\ + apt install -y python3-dev python3-pip &&\ + apt install -y git wget curl bc net-tools &&\ + apt install -y lsb-core &&\ + apt install -y vim &&\ + apt clean + +# Configurate pip source +COPY pip.conf /etc/ + +# Install TensorFlow 1.12 GPU version +RUN pip3 install tensorflow-gpu==1.12.0 + +# Install Python packages +COPY requirements.txt . +RUN pip3 install -r requirements.txt && rm requirements.txt + +WORKDIR /root + +# TensorFlow official benchmark +RUN git clone --branch=cnn_tf_v1.12_compatible https://github.com/tensorflow/benchmarks.git + +# Install Orion Client runtime +COPY install-client . +RUN chmod +x install-client && ./install-client -q && rm install-client + +# Set default ORION_VGPU for each process requesting vgpu resources +ENV ORION_VGPU 1 + +WORKDIR /root +CMD ["/bin/bash"] diff --git a/dockerfiles/client-tf1.12-base/README.md b/dockerfiles/client-tf1.12-base/README.md new file mode 100755 index 0000000..112674d --- /dev/null +++ b/dockerfiles/client-tf1.12-base/README.md @@ -0,0 +1,2 @@ +# 构建镜像 +用户只需将`install-client`安装包放到Dockerfile所在的路径下,即可通过`docker build`命令构建镜像。 \ No newline at end of file diff --git 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+index-url=https://pypi.doubanio.com/simple/ +trusted-host=pypi.doubanio.com diff --git a/dockerfiles/client-tf1.12-base/requirements.txt b/dockerfiles/client-tf1.12-base/requirements.txt new file mode 100755 index 0000000..a4a8a64 --- /dev/null +++ b/dockerfiles/client-tf1.12-base/requirements.txt @@ -0,0 +1,2 @@ +pillow +scipy==1.2.0 diff --git a/dockerfiles/client-tf1.12-py2/Dockerfile b/dockerfiles/client-tf1.12-py2/Dockerfile new file mode 100755 index 0000000..9930c71 --- /dev/null +++ b/dockerfiles/client-tf1.12-py2/Dockerfile @@ -0,0 +1,45 @@ +FROM ubuntu:16.04 +MAINTAINER zoumao@virtaitech.com + +RUN sed -i 's/archive.ubuntu.com/mirrors.ustc.edu.cn/g' /etc/apt/sources.list + +RUN apt update -y &&\ + apt install -y libcurl4-openssl-dev &&\ + apt install -y python-dev python-pip &&\ + apt install -y git wget curl bc net-tools &&\ + apt install -y lsb-core &&\ + apt install -y vim &&\ + apt install -y python-tk &&\ + apt clean + +# Install RDMA driver +WORKDIR /tmp + +COPY MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64.tgz . + +RUN tar xvf MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64.tgz &&\ + cd MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64 &&\ + ./mlnxofedinstall --user-space-only --without-fw-update --all --force -q &&\ + cd /tmp && rm -rf * + +# Configurate pip source +COPY pip.conf /etc/ + +# Install TensorFlow 1.12 GPU version +RUN pip install tensorflow-gpu==1.12.0 + +# Install Python packages +COPY requirements.txt . +RUN pip install -r requirements.txt && rm requirements.txt + +WORKDIR /root + +# Install Orion Client runtime +COPY install-client . +RUN chmod +x install-client && ./install-client -q && rm install-client + +# Set default ORION_VGPU for each process requesting vgpu resources +ENV ORION_VGPU 1 + +WORKDIR /root +CMD ["/bin/bash"] diff --git a/dockerfiles/client-tf1.12-py2/README.md b/dockerfiles/client-tf1.12-py2/README.md new file mode 100755 index 0000000..750d7a7 --- /dev/null +++ b/dockerfiles/client-tf1.12-py2/README.md @@ -0,0 +1,9 @@ +# 构建镜像 +用户需要将`install-client`安装包放到Dockerfile所在的路径下。 + +然后,用户需要在Mellanox官网下载MLNX_OFED 4.5-1.0.1.0驱动: +http://www.mellanox.com/page/mlnx_ofed_eula?mtag=linux_sw_drivers&mrequest=downloads&mtype=ofed&mver=MLNX_OFED-4.5-1.0.1.0&mname=MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64.tgz + +接受协议后方可在浏览器中开始下载。 + +最后,用户可以通过`docker build`命令构建镜像。 \ No newline at end of file diff --git a/dockerfiles/client-tf1.12-py2/build-docker-images.sh b/dockerfiles/client-tf1.12-py2/build-docker-images.sh new file mode 100755 index 0000000..fbe9df2 --- /dev/null +++ b/dockerfiles/client-tf1.12-py2/build-docker-images.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +set -e + +cd `dirname $0` + +docker build -t orion-client:tf1.12-py3 . diff --git a/dockerfiles/client-tf1.12-py2/pip.conf b/dockerfiles/client-tf1.12-py2/pip.conf new file mode 100755 index 0000000..9fc6e2d --- /dev/null +++ b/dockerfiles/client-tf1.12-py2/pip.conf @@ -0,0 +1,3 @@ +[global] +index-url=https://pypi.doubanio.com/simple/ +trusted-host=pypi.doubanio.com diff --git a/dockerfiles/client-tf1.12-py2/requirements.txt b/dockerfiles/client-tf1.12-py2/requirements.txt new file mode 100755 index 0000000..a43707c --- /dev/null +++ b/dockerfiles/client-tf1.12-py2/requirements.txt @@ -0,0 +1,7 @@ +pillow +scipy==1.2.0 +matplotlib==2.2.4 +contextlib2 +lxml +cython +requests diff --git a/dockerfiles/client-tf1.12-py3/Dockerfile b/dockerfiles/client-tf1.12-py3/Dockerfile new file mode 100755 index 0000000..fca3172 --- /dev/null +++ b/dockerfiles/client-tf1.12-py3/Dockerfile @@ -0,0 +1,44 @@ +FROM ubuntu:16.04 +MAINTAINER zoumao@virtaitech.com + +RUN sed -i 's/archive.ubuntu.com/mirrors.ustc.edu.cn/g' /etc/apt/sources.list + +RUN apt update -y &&\ + apt install -y libcurl4-openssl-dev &&\ + apt install -y python3-dev python3-pip &&\ + apt install -y git wget curl bc net-tools &&\ + apt install -y lsb-core &&\ + apt install -y vim &&\ + apt clean + +# Install RDMA driver +WORKDIR /tmp + +COPY MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64.tgz . + +RUN tar xvf MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64.tgz &&\ + cd MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64 &&\ + ./mlnxofedinstall --user-space-only --without-fw-update --all --force -q &&\ + cd /tmp && rm -rf * + +# Configurate pip source +COPY pip.conf /etc/ + +# Install TensorFlow 1.12 GPU version +RUN pip3 install tensorflow-gpu==1.12.0 + +# Install Python packages +COPY requirements.txt . +RUN pip3 install -r requirements.txt && rm requirements.txt + +WORKDIR /root + +# Install Orion Client runtime +COPY install-client . +RUN chmod +x install-client && ./install-client -q && rm install-client + +# Set default ORION_VGPU for each process requesting vgpu resources +ENV ORION_VGPU 1 + +WORKDIR /root +CMD ["/bin/bash"] diff --git a/dockerfiles/client-tf1.12-py3/README.md b/dockerfiles/client-tf1.12-py3/README.md new file mode 100755 index 0000000..7bd7112 --- /dev/null +++ b/dockerfiles/client-tf1.12-py3/README.md @@ -0,0 +1,9 @@ +# 构建镜像 +用户需要将`install-client`安装包放到Dockerfile所在的路径下。 + +然后,用户需要在Mellanox官网下载MLNX_OFED 4.5-1.0.1.0驱动: +http://www.mellanox.com/page/mlnx_ofed_eula?mtag=linux_sw_drivers&mrequest=downloads&mtype=ofed&mver=MLNX_OFED-4.5-1.0.1.0&mname=MLNX_OFED_LINUX-4.5-1.0.1.0-ubuntu16.04-x86_64.tgz + +接受协议后方可在浏览器中开始下载。 + +最后,用户可以通过`docker build`命令构建镜像。 \ No newline at end of file diff --git a/dockerfiles/client-tf1.12-py3/build-docker-images.sh b/dockerfiles/client-tf1.12-py3/build-docker-images.sh new file mode 100755 index 0000000..fbe9df2 --- /dev/null +++ b/dockerfiles/client-tf1.12-py3/build-docker-images.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +set -e + +cd `dirname $0` + +docker build -t orion-client:tf1.12-py3 . diff --git a/dockerfiles/client-tf1.12-py3/install-client b/dockerfiles/client-tf1.12-py3/install-client new file mode 100755 index 0000000000000000000000000000000000000000..49a5a0032a3de6f90fb17fcb88bbe94ef4d2f5de GIT binary patch literal 7702896 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{ + echo "Usage: install-server.sh [-h|-d [target path]]" + echo " -d installed target path. Default /usr/bin" + echo " -h print this help" +} + + +install_path="/usr/bin" + +while getopts "d:h" opt +do + case $opt in + d) install_path=$OPTARG;; + h) + print_help + exit 0;; + ?) + print_help + exit 1;; + esac +done + +if [ ! -f oriond ]; then + echo "Can not find binary oriond. Please check your install package." + exit 1 +fi + +if [ ! -f orion-check ]; then + echo "Can not find binary orion-check. Please check your install package." + exit 1 +fi + +if [ ! -f orion-shm ]; then + echo "Can not find binary orion-shm. Please check your install package." + exit 1 +fi + +if [ "$(id -u)" != "0" ]; then + echo "Error. Root privilege is required to install Orion Server." + exit 1 +fi + +if systemctl status oriond > /dev/null 2>&1; then + systemctl stop oriond +fi + +mkdir -p /var/log/orion +chmod 777 /var/log/orion + +cp oriond orion-check orion-shm $install_path +chmod 755 $install_path/oriond +chmod 755 $install_path/orion-check +chmod 755 $install_path/orion-shm + +if which virsh > /dev/null 2>&1; then + if virsh capabilities | grep -F "apparmor" > /dev/null 2>&1; then + armor_qemu_file=/etc/apparmor.d/abstractions/libvirt-qemu + if [ -f $armor_qemu_file ]; then + if grep -F "orionsock*" $armor_qemu_file > /dev/null; then + : + else + sed -i '/^\s*\/[{]*dev\>.*\/shm\>\s*r,.*/a \ \ \/dev\/shm\/orionsock* rw,' $armor_qemu_file + systemctl reload apparmor.service + fi + fi + fi +fi + +if [ -f orion.conf.template ]; then + mkdir -p /etc/orion + cp orion.conf.template /etc/orion/server.conf + chmod 755 /etc/orion + chmod 644 /etc/orion/server.conf + echo "orion.conf.template is copied to /etc/orion/server.conf as Orion Server configuration file." +fi + +echo "Orion Server is successfully installed to $install_path" +CUDA_HOME=${CUDA_HOME:-"/usr/local/cuda-9.0"} + +cat > /etc/systemd/system/oriond.service << EOF +[Unit] +Description=Orion Server Daemon Service + +[Service] +Type=simple +ExecStart=/usr/bin/oriond +KillMode=process +KillSignal=SIGINT +SendSIGKILL=yes +Environment="LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH" +Environment="PATH=$CUDA_HOME/bin:$PATH" + +[Install] +WantedBy=multi-user.target +EOF + +systemctl reload oriond > /dev/null 2>&1 +systemctl enable oriond > /dev/null 2>&1 + +echo "Orion Server is registered as system service." +echo "Using following commands to interact with Orion Server :" +echo -e "\n\tsystemctl start oriond # start oriond daemon" +echo -e "\tsystemctl status oriond # print oriond daemon status and screen output" +echo -e "\tsystemctl stop oriond # stop oriond daemon" +echo -e "\tjournalctl -u oriond # print oriond stdout" + +echo -e "\nBefore launching Orion Server, please change settings in /etc/orion/server.conf according to your environment.\n" + + diff --git a/md5sum.txt b/md5sum.txt new file mode 100755 index 0000000..a5c7cdd --- /dev/null +++ b/md5sum.txt @@ -0,0 +1,7 @@ +74872f49042cf6570b821e1cba7c892e ./oriond +93eded304497cff77b4f3ef013108e16 ./install-server.sh +69d642f58f3c793c885111188409792b ./orion-shm +93fd51ccba8a56c695d58ecb7a83c6e2 ./orion.conf.template +c5974ac53ff4b55e64e3023a26dd91d3 ./orion-controller +2a593887296675fd80a3a2d8fb190dcb ./install-client +5b2745b18ca619eebcce4c86d5258081 ./orion-check diff --git a/orion-check b/orion-check new file mode 100755 index 0000000..8c6ba08 --- /dev/null +++ b/orion-check @@ -0,0 +1,865 @@ +#!/bin/bash + +cd `dirname $0` +SELF_PATH=$(pwd) + +function print_help { + echo " +NAME: + Orion Health Check Tool + +VERSION: + v0.1 + +COMMANDS: + install + server Check health for installing Orion Server + client Check health for installing Orion Client + controller Check health for installing Orion Controller + all Check health for installing all Orion components + + runtime + server Diagnose the status for Orion Server running + client Diagnose the status for Orion Client running + +EXAMPLES: + orion-check install server + orion-check install client + orion-check runtime server +" +} + + +while getopts "h" opt +do + case $opt in + h) + print_help + exit 0;; + ?) + print_help + exit 1;; + esac +done + +if [ -z "$1" -o -z "$2" ]; then + echo "Invalid usage for Orion Health Check Tool." + print_help + exit 1 +fi + +summary_os_support= +function check_os { + OS_NAME= + OS_VERSION= + KERNAL_VERSION=$(uname -r) + if [ ! -f /etc/os-release ]; then + echo -e "\nOS information : unknown OS" + echo -e " : Kernel $KERNAL_VERSION" + return 0 + fi + + OS_NAME=$(cat /etc/os-release | grep -w NAME | awk -F '"' '{print $2}' | awk '{print $1}') + OS_VERSION=$(cat /etc/os-release | grep -w VERSION_ID | awk -F '"' '{print $2}') + echo -e "\nOS information : $OS_NAME $OS_VERSION" + echo -e " : Kernel $KERNAL_VERSION" + + if [ $OS_NAME == "CentOS" ]; then + if [ $OS_VERSION == "7" ]; then + summary_os_support="Yes" + else + summary_os_support="No" + fi + elif [ $OS_NAME == "Ubuntu" ]; then + if [ $OS_VERSION == "16.04" ]; then + summary_os_support="Yes" + else + summary_os_support="Unknown" + fi + else + summary_os_support="Unknown" + fi +} + + +function check_hw_configuration { + echo -e "\nChecking CPU configuration ..." + lscpu | head -n -1 + + echo -e "\nChecking disk space ..." + df -hT +} + + +default_lib_path=( +/lib +/lib64 +/usr/lib +/usr/lib64 +/usr/local/lib +/usr/local/lib64 +) +rdma_driver=0 +rdma_support=0 +summary_rdma_support="No" +function find_rdma_support { + echo -e "\nChecking RDMA network support ..." + if ls /dev/infiniband/rdma_cm > /dev/null 2>&1; then + if ls /dev/infiniband/uverbs* > /dev/null 2>&1; then + rdma_driver=1 + for path in ${default_lib_path[@]}; do + if [ -d $path ]; then + result=$(find $path -name "librdmacm.so") + if [ -n "$result" ]; then + rdma_support=1 + fi + fi + done + fi + fi + + if [ $rdma_support -eq 0 ]; then + if [ $rdma_driver -eq 0 ]; then + echo "No RDMA network support is found in the system." + else + echo "RDMA device is found but rdmacm library is not found in default searching path." + fi + else + echo "RDMA support is found in the system." + summary_rdma_support="Yes" + # Try to get information by using Mellanox OFED tools + if which ibdev2netdev > /dev/null 2>&1; then + printf "\n RMDA-Port RDMA-Rate Interface Status\n" + printf " ------------------------------------------------\n" + i=0 + ibdev2netdev | + while IFS= read -r line + do + mlx_port_name[$i]=$(echo $line | awk '{print $1}') + mlx_port_interface[$i]=$(echo $line | awk '{print $5}') + mlx_port_interface_status[$i]=$(echo $line | awk '{print $6}') + + if which ibstatus > /dev/null 2>&1; then + mlx_port_rate[$i]=$(ibstatus ${mlx_port_name[$i]} | grep -F "rate:" | awk '{print $2, $3}') + fi + printf "%8s %14s %14s %10s\n" "${mlx_port_name[$i]}" "${mlx_port_rate[$i]}" "${mlx_port_interface[$i]}" "${mlx_port_interface_status[$i]}" + let i++ + done + fi + fi +} + + +cuda_install_path="/usr/local" +cuda_path= +summary_cuda_support="No" +function find_cuda { + echo -e "\nSearching CUDA ..." + if [ -n "$CUDA_HOME" ]; then + echo "CUDA_HOME is set to ${CUDA_HOME}" + fi + + possible_path=$(find $cuda_install_path -maxdepth 1 -type d -name "cuda*") + if [ -z "$possible_path" ]; then + echo -e "\033[31m[Error] Fail to find cuda in default path $cuda_install_path:\033[0m" + return 1 + fi + + i=0 + for path in $possible_path; do + if ls $path/version.txt > /dev/null 2>&1; then + version=$(cat $path/version.txt | head -n 1) + echo "Find $version in $path" + if echo $version | grep "\<9.0." > /dev/null; then + summary_cuda_support="Yes" + fi + cuda_path[$i]=$path + let i++ + fi + done +} + +cudnn_version= +summary_cudnn_support="No" +function find_cudnn { + echo -e "\nSearching CUDNN ..." + i=0 + for path in ${cuda_path[@]}; do + version= + pushd $path/lib64 > /dev/null + libcudnn=$(find -type l -name "libcudnn.so*" -o -type f -name "libcudnn.so*" | awk -F '/' '{print $2}') + if [ -n "$libcudnn" ]; then + version=$(find -type f -name "libcudnn.so*" | awk -F 'so.' '{print $2}') + if [ -z "$version" ]; then + version="(unknown version)" + if [ $summary_cudnn_support == "No" ]; then + summary_cudnn_support="Unknown" + fi + fi + echo "CUDNN $version is installed in $path" + ls -l libcudnn.so* | awk '{print " ", $9, $10, $11}' + cudnn_version[$i]=${version} + + cudnn_major=$(echo $version | awk -F '.' '{print $1}') + cudnn_mid=$(echo $version | awk -F '.' '{print $2}') + if [ $cudnn_major == "7" ]; then + if [ $cudnn_mid -gt 1 ]; then + summary_cudnn_support="Yes" + fi + fi + let i++ + fi + popd > /dev/null + done + + if [ $i -eq 0 ]; then + echo "No CUDNN library is found." + fi +} + + +summary_nvidia_gpu_support="No" +function find_nvidia_gpu { + cuda_driver_version= + nvidia_gpu= + + echo -e "\nSearching NVIDIA GPU ..." + if nvidia-smi -i 0 -q > /dev/null; then + cuda_driver_version=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader) + echo "CUDA driver $cuda_driver_version is installed." + + gpus=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader) + if [ -z "$gpus" ]; then + echo -e "\033[33m[Warning] No NVIDIA GPU is found in the system.\033[0m" + return 0 + fi + + summary_nvidia_gpu_support="Yes" + i=0 + tmp_ifs=$IFS + IFS=$'\n' + for name in $gpus; do + nvidia_gpu[$i]="$name" + let i++ + done + IFS=$tmp_ifs + + if [ $i -gt 1 ]; then + echo "$i NVIDIA GPUs are found :" + else + echo "$i NVIDIA GPU is found :" + fi + + i=0 + for name in "${nvidia_gpu[@]}"; do + echo " $i :" "$name" + let i++ + done + else + echo -e "\033[31m[Error] Fail to get NVIDIA driver version.\033[0m" + return 1 + fi +} + + +enable_nvidia_mps=0 +summary_nvidia_mps="OFF" +function find_mps_support { + echo -e "\nChecking NVIDIA MPS ..." + user=$(ps -aux | grep -v grep | grep -F "nvidia-cuda-mps-control" | awk '{print $1}') + if [ -n "$user" ]; then + enable_nvidia_mps=1 + summary_nvidia_mps="ON" + echo "NVIDIA CUDA MPS is running by Linux account : $user" + echo -e "\033[33m[Info] Orion only supports enabling MPS with NVIDIA Volta and later GPU.\033[0m" + else + echo "NVIDIA CUDA MPS is off." + fi +} + + +etcd_running=0 +etcd_version= +etcd_v2=0 +etcd_v3=0 +summary_etcd_support="No" +function find_etcd { + echo -e "\nSeaching etcd service ..." + bin= + running_bin=$(ps -aux | grep -v grep | grep -w etcd | awk '{print $11}') + if [ -z "$running_bin" ]; then + if which etcd > /dev/null 2>&1; then + bin=$(which etcd) + fi + else + bin=$running_bin + etcd_running=1 + summary_etcd_support="Yes" + fi + + if [ -z "$bin" ]; then + echo "No etcd is running or installed in the system." + return 1 + fi + + etcd_version=$($bin --version | grep "etcd Version" | awk '{print $3}') + echo "etcd (version $etcd_version) is installed in $bin" + + major=${etcd_version:0:1} + if [ $major -eq 2 ]; then + etcd_v2=1 + elif [ $major -eq 3 ]; then + etcd_v3=1 + fi +} + + +qemu_api_version= +qemu_version= +qemu_version_major= +qemu_version_mid= +qemu_net_list= +summary_qemu_kvm_support="No" +function find_qemu_kvm { + echo -e "\nSearching VM support ..." + running_bin=$(ps -aux | grep -v grep | grep -w libvirtd | awk '{print $11}') + if [ -z "$running_bin" ]; then + echo "libvirtd is not running. Please install libvirt-bin and start libvirtd before luanching Orion Server." + return 1 + fi + + qemu_api_version=$(virsh version | grep "Using API" | awk '{print $4}') + qemu_version=$(virsh version | grep "Running hypervisor" | awk '{print $4}') + qemu_version_major=$(echo $qemu_version | awk -F '.' '{print $1}') + qemu_version_mid=$(echo $qemu_version | awk -F '.' '{print $2}') + echo "QEMU API version : $qemu_api_version" + echo "QEMU version : $qemu_version" + + if [ $qemu_version_major == "2" ]; then + summary_qemu_kvm_support="Yes" + fi + + nets=$(virsh net-list | tail -n +3 | head -n -1 | awk '{print $1}') + i=0 + for net in $nets; do + qemu_net_list[$i]=$(virsh net-dumpxml $net | grep -F " /dev/null 2>&1; then + docker_installed=1 + if docker images 2>&1 | grep "Cannot connect" > /dev/null; then + echo "Docker is not launched in the system" + return + fi + + if docker images 2>&1 | grep "permission denied" > /dev/null; then + echo "Permission denied to check docker environment." + return + fi + + summary_docker_support="Yes" + docker version + docker_gateway=$(docker inspect -f '{{range .IPAM.Config}}{{.Gateway}}{{end}}' bridge) + if [ -z "$docker_gateway" ]; then + docker_gateway=$(ip addr show docker0 2>/dev/null | grep inet | awk '{print $2}' | awk -F '/' '{print $1}') + fi + else + echo "Docker is not installed in the system" + fi +} + + +server_name="oriond" +summary_server_support="No" +function check_server_install { + echo -e "\nChecking Orion Server binary ..." + if [ ! -f ${server_name} ]; then + echo "Can not find installation file \"${server_name}\"" + return 1 + fi + + if [ -z "$CUDA_HOME" ]; then + echo -e "\033[33mCUDA_HOME is not set in current enviornment. You may want to set it before doing the checking.\033[0m" + else + export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH + fi + + unfound_lib=$(ldd ${server_name} | grep "not found" | awk '{print $1}') + if [ -n "$unfound_lib" ]; then + echo -e "\033[31mFollowing libraries are needed but not found :\033[0m" + echo "$unfound_lib" + return 1 + fi + + summary_server_support="Yes" +} + + +function check_controller_runtime { + config_path= + if [ "$1" == "server" ]; then + config_path="/etc/orion/server.conf" + else + config_path="/etc/orion/client.conf" + fi + + echo -e "\nChecking Orion Controller status ..." + echo -e "\033[33m[Info] Orion Controller setting may be different in different SHELL.\033[0m" + echo -e "\033[33m[Info] Environment variable ORION_CONTROLLER has the first priority.\033[0m\n" + controller_addr_env=$ORION_CONTROLLER + controller_addr= + if [ -r $config_path ]; then + controller_addr=$(sed -n 's/^\s*controller_addr\s*=\s*\([0-9]*\.[0-9]*\.[0-9]*\.[0-9]*:[0-9]*\).*/\1/p' $config_path) + controller_addr=${controller_addr:-"127.0.0.1:9123"} + fi + + if [ -z "$controller_addr_env" -a -z "$controller_addr" ]; then + echo -e "\033[31m[Error] No Orion Controller address is set in either environment variable ORION_CONTROLLER or configuration file.\033[0m" + return + fi + + controller_ip= + controller_port= + target_addr= + if [ -n "$controller_addr_env" ]; then + target_addr=$controller_addr_env + echo "Environment variable ORION_CONTROLLER is set as ${controller_addr_env} Using this address to diagnose Orion Controller." + else + target_addr=$controller_addr + echo "Orion Controller addrress is set as $controller_addr in configuration file. Using this address to diagnose Orion Controller" + fi + + controller_ip=$(echo $target_addr | awk -F ':' '{print $1}') + controller_port=$(echo $target_addr | awk -F ':' '{print $2}') + if [ -z "$controller_port" ]; then + echo -e "\033[31m[Error] Invalid Orion Controller address. No port is specified.\033[0m" + return + fi + + if which nc > /dev/null 2>&1; then + if nc -zv $controller_ip $controller_port > /dev/null 2>&1; then + echo "Address $target_addr is reached." + else + echo -e "\033[31m[Error] Can not reach ${target_addr}. Please make sure Orion Controller is launched at the address, and the firewall is correctly set.\033[0m" + return + fi + fi + + if which curl > /dev/null 2>&1; then + result=$(curl -s "http://$target_addr/info?data_version&api_version") + if [ $? -ne 0 ]; then + echo -e "\033[31m[Error] Can not reach ${target_addr}. Please make sure Orion Controller is launched at the address, and the firewall is correctly set.\033[0m" + return + else + echo "Orion Controller Version Infomation : $result" + fi + + data_version=$(echo $result | awk -F ',' '{print $1}') + api_version=$(echo $result | awk -F ',' '{print $2}') + + data_version=${data_version/=/:} + api_version=${api_version/=/:} + + result=$(curl -s -H "${data_version}" -H "${api_version}" "http://$target_addr/devices?res=nvidia_cuda&used=true") + if [ $? -ne 0 ]; then + echo -e "\033[31m[Error] Can not fetch vGPU status from Orion Controller.\033[0m" + return + else + free_num=${result##*=} + fi + + result=$(curl -s -H "${data_version}" -H "${api_version}" "http://$target_addr/devices?res=nvidia_cuda") + if [ $? -ne 0 ]; then + echo -e "\033[31m[Error] Can not fetch vGPU status from Orion Controller.\033[0m" + return + else + total_num=${result##*=} + fi + + echo "There are $total_num vGPU under managered by Orion Controller. $free_num vGPU are free now." + else + echo -e "\033[33mLinux curl is needed to diagnose Orion Controller.\033[0m" + return + fi +} + + +function check_oriond_runtime { + echo -e "\nChecking Orion Server status ..." + if ! which netstat > /dev/null 2>&1; then + echo "Linux tool netstat is not found. Installing the tool helps to diagnose the system." + fi + if ! which nc > /dev/null 2>&1; then + echo "Linux tool net-cat is not found. Installing the tool helps to diagnose the system." + fi + if ! which curl > /dev/null 2>&1; then + echo "Linux tool curl is not found. Installing the tool helps to diagnose the system." + fi + + running_bin=$(ps -aux | grep -v grep | grep -w oriond) + if [ -z "$running_bin" ]; then + echo -e "\033[33mOrion Server is not running.\033[0m\n" + + bin_path= + if [ -f ${server_name} ]; then + bin_path=${server_name} + else + if [ -f /usr/bin/${server_name} ]; then + bin_path="/usr/bin/${server_name}" + else + echo "Can not find Orion Server binary \"oriond\" in either `pwd` or /usr/bin." + fi + fi + + if [ -r /etc/systemd/system/oriond.service ]; then + bin_path=$(cat /etc/systemd/system/oriond.service | grep -F 'ExecStart=' | awk -F '=' '{print $2}') + echo "Orion Server has been registered as system service. Using binary $bin_path to infer the runtime environment." + ld_path=$(cat /etc/systemd/system/oriond.service | grep -F 'Environment="LD_LIBRARY_PATH=' | awk -F '[="]' '{print $4}') + path_path=$(cat /etc/systemd/system/oriond.service | grep -F 'Environment="PATH=' | awk -F '[="]' '{print $4}') + if [ -n "$ld_path" ]; then + export LD_LIBRARY_PATH=$ld_path + echo "Injecting oriond service environment LD_LIBRARY_PATH=$ld_path" + fi + + if [ -n "$path_path" ]; then + export PATH=$path_path + echo "Injecting oriond service environment PATH=$path_path" + fi + fi + + if [ -n "$bin_path" ]; then + + unfound_lib=$(ldd ${bin_path} | grep "not found" | awk '{print $1}') + if [ -n "$unfound_lib" ]; then + echo -e "\033[31mFollowing libraries are needed but not found in current environment:\033[0m" + echo "$unfound_lib" + return 1 + fi + fi + + controller_addr_env=$ORION_CONTROLLER + if [ -n "$controller_addr_env" ]; then + if echo $controller_addr_env | grep ":" > /dev/null; then + echo "Environment variable ORION_CONTROLLER=$controller_addr_env is set in current SHELL." + else + echo "Environment variable ORION_CONTROLLER=$controller_addr_env is set in current SHELL." + echo "\033[33m[Warning] Invalid format. No port is specified.\033[0m" + fi + fi + + controller_addr= + if [ -r /etc/orion/server.conf ]; then + controller_addr=$(sed -n 's/^\s*controller_addr\s*=\s*\([0-9]*\.[0-9]*\.[0-9]*\.[0-9]*:[0-9]*\).*/\1/p' /etc/orion/server.conf) + bind_ip=$(sed -n 's/^\s*bind_addr\s*=\s*\([0-9]*\.[0-9]*\.[0-9]*\.[0-9]*\).*/\1/p' /etc/orion/server.conf) + bind_port=$(sed -n 's/^\s*listen_port\s*=\s*\([0-9]*\).*/\1/p' /etc/orion/server.conf) + + controller_addr=${controller_addr:-"127.0.0.1:9123"} + bind_ip=${bind_ip:-"127.0.0.1"} + bind_port=${bind_port:-"9960"} + + echo "" + echo "Configuration file is found at /etc/orion/server.conf" + echo "Orion Server will connect to Orion Controller at $controller_addr unless the setting is overwritten by environment variable \"ORION_CONTROLLER\"" + echo "Orion Server will listen on port $bind_port unless the setting is overwritten by -p option" + + valid_ip=0 + if ip addr > /dev/null 2>&1; then + while read line + do + if [ $bind_ip == ${line} ]; then + valid_ip=1 + break + fi + done <<< "$(ip addr | grep -w inet | awk '{print $2}' | awk -F '/' '{print $1}')" + else + valid_ip=1 + fi + + if [ $valid_ip -eq 1 ]; then + echo "Orion Server will bind to address $bind_ip unless the setting is overwritten by -b option" + else + echo -e "\033[33mOrion Server is configured to bind at address \"${bind_ip}\" which may be invalid.\033[0m" + fi + + cfg_enable_shm=0 + cfg_enable_rdma=0 + cfg_enable_kvm=0 + if [ -f /etc/orion/server.conf ]; then + result=$(sed -n 's/^\s*enable_shm\s*=\s*"\([a-z]*\)".*/\1/p' /etc/orion/server.conf) + if [ "$result"x == "truex" ]; then + cfg_enable_shm=1 + fi + result=$(sed -n 's/^\s*enable_rdma\s*=\s*"\([a-z]*\)".*/\1/p' /etc/orion/server.conf) + if [ "$result"x == "truex" ]; then + cfg_enable_rdma=1 + fi + result=$(sed -n 's/^\s*enable_kvm\s*=\s*"\([a-z]*\)".*/\1/p' /etc/orion/server.conf) + if [ "$result"x == "truex" ]; then + cfg_enable_kvm=1 + fi + fi + + printf "%-40s" "Enable SHM" + if [ $cfg_enable_shm == 1 ]; then + printf "[Yes]\n" + else + printf "[No]\n" + fi + + printf "%-40s" "Enable RDMA" + if [ $cfg_enable_rdma == 1 ]; then + printf "[Yes]\n" + else + printf "[No]\n" + fi + + printf "%-40s" "Enable Local QEMU-KVM with SHM" + if [ $cfg_enable_kvm == 1 ]; then + printf "[Yes]\n" + else + printf "[No]\n" + fi + else + echo "No configuration is set in the system. Default setting and environment variables will be used to configure Orion Server." + echo -e "Orion Server will connect to Orion Controller set by environment variable \033[32mORION_CONTROLLER\033[0m" + echo -e "Orion Server will bind to address \033[32m127.0.0.1\033[0m unless the setting is overwritten by \033[32m-b\033[0m option" + echo -e "Orion Server will listen on port \033[32m9960\033[0m unless the setting is overwritten by \033[32m-p\033[0m option" + bind_port=9960 + fi + + if which netstat > /dev/null 2>&1; then + result=$(netstat -tulpn 2>/dev/null | grep -w LISTEN | awk '{print $4}' | grep ":${bind_port}\>") + if [ -n "$result" ]; then + echo -e "\033[33m[Warning] Linux port $bind_port is in used by other program.\033[0m" + fi + fi + else + pid=$(ps -aux | awk '{print $2,$11}' | grep -v grep | grep -w "oriond" | awk '{print $1}') + pid_controller=$(strings /proc/${pid}/environ | grep ORION_CONTROLLER | awk -F '=' '{print $2}') + if [ -n "$pid_controller" ]; then + echo "Orion Server runs with environment ORION_CONTROLLER=${pid_controller}" + export ORION_CONTROLLER=${pid_controller} + fi + + cfg_enable_shm=0 + cfg_enable_rdma=0 + cfg_enable_kvm=0 + if [ -f /etc/orion/server.conf ]; then + result=$(sed -n 's/^\s*enable_shm\s*=\s*"\([a-z]*\)".*/\1/p' /etc/orion/server.conf) + if [ "$result"x == "truex" ]; then + cfg_enable_shm=1 + fi + result=$(sed -n 's/^\s*enable_rdma\s*=\s*"\([a-z]*\)".*/\1/p' /etc/orion/server.conf) + if [ "$result"x == "truex" ]; then + cfg_enable_rdma=1 + fi + result=$(sed -n 's/^\s*enable_kvm\s*=\s*"\([a-z]*\)".*/\1/p' /etc/orion/server.conf) + if [ "$result"x == "truex" ]; then + cfg_enable_kvm=1 + fi + fi + + user_name=$(ps -aux | grep -v grep | grep "oriond" | awk '{print $1}') + command_line=$(ps -aux | grep -v grep | grep "oriond" | awk '{for(i=11;i<=NF;i++){printf "%s ", $i}; printf "\n"}') + echo "Orion Server is running with Linux user : $user_name" + echo "Orion Server is running with command line : $command_line" + + cudart_path=$(ls -l /proc/${pid}/map_files | grep libcudart | awk '{print $11}' | head -n 1 | awk -F 'so.' '{print $2}') + if [ -n "$cudart_path" ]; then + echo "Orion Server is running with CUDA version $cudart_path" + fi + + cudnn_path=$(ls -l /proc/${pid}/map_files | grep libcudnn | awk '{print $11}' | head -n 1 | awk -F 'so.' '{print $2}') + if [ -n "$cudnn_path" ]; then + echo "Orion Server is running with CUDNN version $cudnn_path" + fi + + + enable_shm=0 + enable_rdma=0 + enable_kvm=0 + bind_ip= + bind_port=9960 + + printf "%-40s" "Enable SHM" + if echo $command_line | grep -e " -m " > /dev/null; then + enable_shm=1 + printf "[Yes]\n" + elif [ $cfg_enable_shm == 1 ]; then + enable_shm=1 + printf "[Yes]\n" + else + printf "[No]\n" + fi + + printf "%-40s" "Enable RDMA" + if echo $command_line | grep -e " -r " > /dev/null; then + enable_rdma=1 + printf "[Yes]\n" + elif [ $cfg_enable_rdma == 1 ]; then + enable_rdma=1 + printf "[Yes]\n" + else + printf "[No]\n" + fi + + printf "%-40s" "Enable Local QEMU-KVM with SHM" + if echo $command_line | grep -e " -k " > /dev/null; then + enable_kvm=1 + printf "[Yes]\n" + elif [ $cfg_enable_kvm == 1 ]; then + enable_kvm=1 + printf "[Yes]\n" + else + printf "[No]\n" + fi + + if which netstat > /dev/null 2>&1; then + listen_addr=$(netstat -nap 2>/dev/null | grep oriond | grep LISTEN | awk '{print $4}' | sort | head -n 1) + if [ -n "$listen_addr" ]; then + bind_ip=$(echo $listen_addr | awk -F ':' '{print $1}') + listen_port=$(echo $listen_addr | awk -F ':' '{print $2}') + fi + else + if echo $command_line | grep -e " -b " > /dev/null; then + bind_ip=$(echo $command_line | sed -n 's/.*\s\+-b\s\+\([0-9]*\.[0-9]*\.[0-9]*\.[0-9]*\).*/\1/p') + else + if [ -r /etc/orion/server.conf ]; then + bind_ip=$(sed -n 's/^\s*bind_addr\s*=\s*\([0-9]*\.[0-9]*\.[0-9]*\.[0-9]*\)/\1/p' /etc/orion/server.conf) + bind_port=$(sed -n 's/^\s*listen_port\s*=\s*\([0-9]*\)/\1/p' /etc/orion/server.conf) + bind_ip=${bind_ip:-"127.0.0.1"} + bind_port=${bind_port:-"9960"} + fi + fi + fi + + printf "%-40s%s\n" "Binding IP Address :" "$bind_ip" + printf "%-40s%s\n\n" "Listening Port :" "$bind_port" + + if which nc > /dev/null 2>&1; then + echo "Testing the Orion Server network ..." + if nc -zv $bind_ip $listen_port > /dev/null 2>&1; then + echo "Orion Server can be reached through $listen_addr" + else + echo "Orion Server can not be reached through $listen_addr" + echo "Please check the firewall setting." + fi + fi + fi +} + + + +if [ "$1" == "install" ]; then + if [ "$2" == "all" ]; then + check_os + find_rdma_support + find_cuda + find_cudnn + find_nvidia_gpu + find_mps_support + find_etcd + find_qemu_kvm + find_docker + check_server_install + + echo -e "\n===============================================" + echo -e "Installation summaries :\n" + printf "%-40s [%s]\n" "OS :" "$summary_os_support" + printf "%-40s [%s]\n" "RDMA :" "$summary_rdma_support" + printf "%-40s [%s]\n" "CUDA :" "$summary_cuda_support" + printf "%-40s [%s]\n" "CUDNN :" "$summary_cudnn_support" + printf "%-40s [%s]\n" "NVIDIA GPU :" "$summary_nvidia_gpu_support" + printf "%-40s [%s]\n" "NVIDIA CUDA MPS :" "$summary_nvidia_mps" + printf "%-40s [%s]\n" "etcd service :" "$summary_etcd_support" + printf "%-40s [%s]\n" "QEMU-KVM environment :" "$summary_qemu_kvm_support" + printf "%-40s [%s]\n" "Docker container environment :" "$summary_docker_support" + printf "%-40s [%s]\n" "Orion Server binary:" "$summary_server_support" + elif [ "$2" == "server" ]; then + check_os + find_rdma_support + find_cuda + find_cudnn + find_nvidia_gpu + find_mps_support + find_qemu_kvm + find_docker + check_server_install + + echo -e "\n===============================================" + echo -e "Installation summaries :\n" + printf "%-40s [%s]\n" "OS :" "$summary_os_support" + printf "%-40s [%s]\n" "RDMA :" "$summary_rdma_support" + printf "%-40s [%s]\n" "CUDA :" "$summary_cuda_support" + printf "%-40s [%s]\n" "CUDNN :" "$summary_cudnn_support" + printf "%-40s [%s]\n" "NVIDIA GPU :" "$summary_nvidia_gpu_support" + printf "%-40s [%s]\n" "NVIDIA CUDA MPS :" "$summary_nvidia_mps" + printf "%-40s [%s]\n" "QEMU-KVM environment :" "$summary_qemu_kvm_support" + printf "%-40s [%s]\n" "Docker container environment :" "$summary_docker_support" + printf "%-40s [%s]\n" "Orion Server binary:" "$summary_server_support" + elif [ "$2" == "client" ]; then + check_os + find_rdma_support + find_qemu_kvm + find_docker + + echo -e "\n===============================================" + echo -e "Installation summaries :\n" + printf "%-40s [%s]\n" "OS :" "$summary_os_support" + printf "%-40s [%s]\n" "RDMA :" "$summary_rdma_support" + printf "%-40s [%s]\n" "QEMU-KVM environment :" "$summary_qemu_kvm_support" + printf "%-40s [%s]\n" "Docker container environment :" "$summary_docker_support" + elif [ "$2" == "controller" ]; then + check_os + find_etcd + + echo -e "\n===============================================" + echo -e "Installation summaries :\n" + printf "%-40s [%s]\n" "OS :" "$summary_os_support" + printf "%-40s [%s]\n" "etcd service :" "$summary_etcd_support" + + if [ $summary_etcd_support == "No" ]; then + echo -e "\n\033[31mOrion Controller can not be installed in this environment.\033[0m" + fi + else + echo "Invalid parameters." + print_help + exit 1 + fi +elif [ "$1" == "runtime" ]; then + if [ "$2" == "server" ]; then + find_nvidia_gpu + find_mps_support + check_oriond_runtime + check_controller_runtime server + elif [ "$2" == "client" ]; then + check_controller_runtime client + else + echo "Invalid parameters." + print_help + exit 1 + fi +else + echo "Invalid parameters." + print_help + exit 1 +fi + + diff --git a/orion-controller b/orion-controller new file mode 100755 index 0000000000000000000000000000000000000000..0114616b1a458c3b85a51cad0b095d0b1950a57d GIT binary patch literal 16112703 zcmeFa33yXw+CQ8^gHfm_$W{;~Xw;&pt)jGIB^8tt3|a-XDkF8!ii&`c0y?NEX@MA` zRnc*q8OCu&opHuxl*P5BNC6!daX}ds_a_EN)Dg;}{eHjuIVU*@Mc?_q?{|I6^>SVG zJm=Z&{eJFeJGu3&(Ak~x@?7@6POdXtd}~~KmShF2f@z(s1Reobsp}y8J<4^Us|()q zB~Oi|4$TCJ=OA9j8FdH!eND=iE8*&EOeExnr2a)Exg{#x1TybsXW**>!AE-8y_ z((M{8eYG3d?d|6%6tI1|T3R10xaS>hH&@j_hsKmsy8VgEb&V5mlxLw;zpM4M z`DW+8nbW?|YF}uzZ_;YzVtc~KZNDAue{64`YQx`pl7_$aBn`jPf_qR-dn?=Z3EHPdl{V7hCNYTkSXJw72oMb^8vqw@9>6l4oRyhTo2I 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