Installation Guide

The architecture of OpenPAI has been updated and optimized in v1.0.0. Before v1.0.0, OpenPAI was based on Yarn and Kubernetes, and data was managed by HDFS. Since v1.0.0, OpenPAI has switched to a pure Kubernetes-based architecture. Many new features, such as AAD authorization, Hivedscheduler, Kube Runtime, Marketplace, etc., are also included. If you still want to install the old Yarn-based OpenPAI, please stay with v0.14.0.

To install OpenPAI >= v1.0.0, please first check Installation Requirements. Then, if you don't have older version OpenPAI installed, please follow Installation From Scratch. Otherwise, please first follow Clean Previous Deployment, then follow Installation From Scratch.

Installation Requirements

The deployment of OpenPAI requires you to have at least 3 separate machines: one dev box machine, one master machine, and one worker machine.

Dev box machine controls masters and workers through SSH during installation, maintenance, and uninstallation. There should be one, and only one dev box. Master machine is used to run core Kubernetes components and core OpenPAI services. In most cases, one master machine is enough. You may set multiple masters if you want the cluster to be highly-available. We recommend you to use CPU-only machines for dev box and master. For worker machines, all of them should have GPUs, and have GPU driver correctly installed.

To be detailed, please check the following requirements before installation:

Dev Box Machine
- Kubespray Requirement
  - Ubuntu 16.04 (18.04 should work, but not fully tested.)
  - Server can communicate with all other machine (master and worker machines)
  - SSH service is enabled and share the same username/password and have sudo privilege
  - Passwordless ssh to all other machines (master and worker machines)
  - Be separate from cluster which contains master machines and worker machines
- OpenPAI Requirement
  - Docker is installed, and it is used to start up dev-box container for service deployment.
Master Machines:
- Kubespray Requirement
  - Assign each server a static IP address, and make sure servers can communicate each other.
  - Server can access internet, especially need to have access to the docker hub registry service or its mirror. Deployment process will pull Docker images.
  - SSH service is enabled and share the same username/password and have sudo privilege.
  - NTP service is enabled, and etcd is depended on it.
- OpenPAI Requirement
  - Ubuntu 16.04 (18.04 should work, but not fully tested.)
  - OpenPAI reserves memory and CPU for service running, so make sure there are enough resource to run machine learning jobs. Check hardware requirements for details.
  - Dedicated servers for OpenPAI. OpenPAI manages all CPU, memory and GPU resources of servers. If there is any other workload, it may cause unknown problem due to insufficient resource.
Worker Machines:
- Kubespray Requirement
  - Assign each server a static IP address, and make sure servers can communicate with each other.
  - Server can access internet, especially need to have access to the docker hub registry service or its mirror. Deployment process will pull Docker images.
  - SSH service is enabled and share the same username/password and have sudo privilege.
- OpenPAI Requirement
  - Ubuntu 16.04 (18.04 should work, but not fully tested.)
  - Have GPU and GPU driver is installed. You may use a command to check it. Refer to the installation guidance in FAQs if the driver is not successfully installed. If you are wondering which version of GPU driver you should use, please also refer to FAQs.
  - Docker is installed. You may use command docker --version to check it. Refer to docker's installation guidance if it is not successfully installed.
  - nvidia-container-runtime or other device runtime is installed. And be configured as the default runtime of docker. Please configure it in docker-config-file, because kubespray will overwrite systemd's env.
    - You may use command sudo docker run nvidia/cuda:10.0-base nvidia-smi to check it. This command should output information of available GPUs if it is setup properly.
    - Refer to the installation guidance if the it is not successfully set up.
  - OpenPAI reserves memory and CPU for service running, so make sure there are enough resource to run machine learning jobs. Check hardware requirements for details.
  - Dedicated servers for OpenPAI. OpenPAI manages all CPU, memory and GPU resources of servers. If there is any other workload, it may cause unknown problem due to insufficient resource.

Tips to Use CPU-only Worker

Currently, the support for CPU-only worker is limited. If you have both GPU workers and CPU workers, please first set up PAI with GPU workers only. After PAI is successfully installed, you can attach CPU workers to it and set up a CPU-only virtual cluster. Please refer to How to use CPU Nodes for details. If you only have CPU workers, we haven't had an official installation support yet. Please submit an issue for feature request.

If you are facing network issues such as the machine cannot download some file, or cannot connect to some docker registry, please combine the prompted error log and kubespray as a keyword, and search for solution. You can also refer to the installation troubleshooting and this issue.

Installation From Scratch

Besides the requirements above, this installation script also requires that all worker machines must be homogenous GPU servers, which have the same hardware, e.g. CPU type and number, GPU type and number, memory size. If you have different types of workers, please first include only one type of workers during installation, then follow How to Add and Remove Nodes to add workers with different types. Now, please determine your dev box machine, master machine and worker machine.

Create Configurations

After you have decided all of the machines, please create a master.csv, a worker.csv, and a config file on the dev box machine. The files represent master machine list, worker machine list, and a customized configuration, respectively. The following is the format and example of these 3 files.

`master.csv` format

Please do not insert blank lines or use spaces in this file, and please do not use upper case alphabet letters for hostname.

hostname(Node Name in k8s),host-ip

`master.csv` example

openpai-master-01,10.1.0.1

`worker.csv` format

Please do not insert blank lines or use spaces in this file, and please do not use upper case alphabet letters for hostname.

hostname(Node Name in k8s),host-ip

`worker.csv` example

openpai-001,10.0.0.1
openpai-002,10.0.0.2
openpai-003,10.0.0.3
openpai-004,10.0.0.4

`config` example

user: <your-ssh-username>
password: <your-ssh-password>
branch_name: pai-1.0.y
docker_image_tag: v1.0.0

# Optional

#############################################
# Ansible-playbooks' inventory hosts' vars. #
#############################################
# ssh_key_file_path: /path/to/you/key/file

#####################################
# OpenPAI's service image registry. #
#####################################
# docker_registry_domain: docker.io
# docker_registry_namespace: openpai
# docker_registry_username: exampleuser
# docker_registry_password: examplepasswd

###########################################################################################
#                         Pre-check setting                                               #
# By default, we assume your gpu environment is nvidia. So your runtime should be nvidia. #
# If you are using AMD or other environment, you should modify it.                        #
###########################################################################################
# worker_default_docker_runtime: nvidia
# docker_check: true

# resource_check: true

# gpu_type: nvidia

########################################################################################
# Advanced docker configuration. If you are not familiar with them, don't change them. #
########################################################################################
# docker_data_root: /mnt/docker
# docker_config_file_path: /etc/docker/daemon.json
# docker_iptables_enabled: false

## An obvious use case is allowing insecure-registry access to self hosted registries.
## Can be ipaddress and domain_name.
## example define 172.19.16.11 or mirror.registry.io
# openpai_docker_insecure_registries:
#   - mirror.registry.io
#   - 172.19.16.11

## Add other registry,example China registry mirror.
# openpai_docker_registry_mirrors:
#   - https://registry.docker-cn.com
#   - https://mirror.aliyuncs.com

#######################################################################
#                       kubespray setting                             #
#######################################################################

# If you couldn't access to gcr.io or docker.io, please configure it.
# gcr_image_repo: "gcr.io"
# kube_image_repo: "gcr.io/google-containers"
# quay_image_repo: "quay.io"
# docker_image_repo: "docker.io"
# kubeadm_download_url: "https://storage.googleapis.com/kubernetes-release/release/{{ kubeadm_version }}/bin/linux/{{ image_arch }}/kubeadm"
# hyperkube_download_url: "https://storage.googleapis.com/kubernetes-release/release/{{ kube_version }}/bin/linux/{{ image_arch }}/hyperkube"


# openpai_kube_network_plugin: calico

branch-name and docker-image-tag stands for OpenPAI version you want to install. The user and password is the SSH username and password from dev box machine to master machines and worker machines. In other words, you should make sure all masters and workers share the same SSH username and password. As for optional configurations, customize them if you know exactly what they are.

For Azure Users: If you are deploying OpenPAI in Azure, please uncomment openpai_kube_network_plugin: calico in the config file above, and change it to openpai_kube_network_plugin: weave. It is because Azure doesn't support calico. See here for details.

Start Installation

On the dev box machine, use the following commands to clone the OpenPAI repo:

git clone https://github.com/microsoft/pai.git
git checkout pai-1.0.y  # change to a different branch if you want to deploy a different version
cd pai/contrib/kubespray

The folder pai/contrib/kubespray contains installation scripts, both for kubespray and OpenPAI services. Please run the following script to deploy Kubernetes first. You should modify /path/to to your own absolute path for these files. Do not use relative path. It will cause an error.

/bin/bash quick-start-kubespray.sh -m /path/to/master.csv -w /path/to/worker.csv -c /path/to/config

After Kubernetes is successfully started, run the following script to start OpenPAI services. You should modify /path/to to your own absolute path for these files. Do not use relative path. It will cause an error.

/bin/bash quick-start-service.sh -m /path/to/master.csv -w /path/to/worker.csv -c /path/to/config

If everything goes well, you will get a message as follows:

Kubernetes cluster config :     ~/pai-deploy/kube/config
OpenPAI cluster config    :     ~/pai-deploy/cluster-cfg
OpenPAI cluster ID        :     pai
Default username          :     admin
Default password          :     admin-password

You can go to http://<your-master-ip>, then use the default username and password to log in.

As the message says, you can use admin and admin-password to login to the webportal, then submit a job to validate your installation.

Tips for Checking Requirements

If there is any error, please double check the environment requirements first. Here we provide a requirement checker to help you verify:

/bin/bash requirement.sh -m /path/to/master.csv -w /path/to/worker.csv -c /path/to/config

Keep a Folder

We highly recommend you to keep the folder ~/pai-deploy for future operations such as upgrade, maintenance, and uninstallation. The most important contents in this folder are:

Kubernetes cluster config (the default is ~/pai-deploy/kube/config): Kubernetes config file. It is used by kubectl to connect to k8s api server.
OpenPAI cluster config (the default is ~/pai-deploy/cluster-cfg): It is a folder containing machine layout and OpenPAI service configurations.

If it is possible, you can make a backup of ~/pai-deploy in case it is deleted unexpectedly.

Apart from the folder, you should remember your OpenPAI cluster ID, which is used to indicate your OpenPAI cluster. The default value is pai. Some management operation needs a confirmation of this cluster ID.

Clean Previous Deployment

Save your Data to a Different Place

If you have installed OpenPAI before v1.0.0, to install OpenPAI >= v1.0.0, you should first clean the previous deployment. You cannot preserve any useful data: all jobs, user information, dataset will be lost inevitably and irreversibly. Thus, if you have any useful data in previous deployment, please make sure you have saved them to a different place.

HDFS Data

Before v1.0.0, PAI will deploy an HDFS server for you. After v1.0.0, the HDFS server won't be deployed and previous data will be removed in upgrade. The following commands could be used to transfer your HDFS data:

# check data structure
hdfs dfs -ls hdfs://<hdfs-namenode-ip>:<hdfs-namenode-port>/

hdfs dfs -copyToLocal hdfs://<hdfs-namenode-ip>:<hdfs-namenode-port>/ <local-folder>

<hdfs-namenode-ip> and <hdfs-namenode-port> is the ip of PAI master and 9000 if you did't modify the default setting. Please make sure your local folder has enough capacity to hold the data you want to save.

Metadata of Jobs and Users

Metadata of jobs and users will also be lost, including job records, job log, user name, user password, etc. We do not have an automatical tool for you to backup these data. Please transfer the data manually if you find some are valuable.

Other Resources on Kubernetes

If you have deployed any other resources on Kubernetes, please make a proper backup for them, because the Kubernetes cluster will be destroyed, too.

Remove Previous PAI deployment

To remove the previous deployment, please use the commands below:

git clone https://github.com/Microsoft/pai.git
cd pai
#  checkout to a different branch if you have a different version
git checkout pai-0.14.y

# delete all pai service and remove all service data
./paictl.py service delete

# delete k8s cluster
./paictl.py cluster k8s-clean -f -p <path-to-your-old-config>

If you cannot find the old config, the following command can help you to retrieve it:

./paictl.py config pull -o <path-to-your-old-config>

You should also remove the GPU driver installed by OpenPAI, by executing the following commands on every GPU node, using a root user:

#!/bin/bash

lsmod | grep -qE "^nvidia" &&
{
    DEP_MODS=`lsmod | tr -s " " | grep -E "^nvidia" | cut -f 4 -d " "`
    for mod in ${DEP_MODS//,/ }
    do
        rmmod $mod ||
        {
            echo "The driver $mod is still in use, can't unload it."
            exit 1
        }
    done
    rmmod nvidia ||
    {
        echo "The driver nvidia is still in use, can't unload it."
        exit 1
    }
}

rm -rf /var/drivers
reboot

After the removal, you can now install OpenPAI >= v1.0.0 by following Installation From Scratch.