Oms in cloud setup - openmpp/openmpp.github.io GitHub Wiki

OpenM++ web-service (oms) can provide basic computational resources management for your local computer or cluster of servers on local network or in cloud. It can manage model runs queue if your computational resources (CPU and memory) are limited and also can automatically start and stop cloud servers.

Content below assuming you are familiar with basics of Oms: openM++ web-service.

Before reading this page please start at: Oms: Cloud and model runs queue.

Google cloud: front-end server and and auto scale of multiple back-end servers

In example below we are using a small front-end server with 4 cores and 4 back-end servers: cpc-1, cpc-2, cpc-3, cpc-4 with 16 cores each. You are using public cloud and want to pay only for actual usage of back end servers:

server(s) must be started automatically when user (Alice or Bob) want to run the model;
server(s) must stop after model run completed to reduce cloud cost

Scripts below are also available at our GitHub↗

[Common]
LocalCpu      = 4       ; localhost CPU cores limit, localhost limits are applied only to non-MPI jobs
LocalMemory   = 0       ; gigabytes, localhost memory limit, zero means no limits
MpiMaxThreads = 8       ; max number of modelling threads per MPI process
MaxErrors     = 10      ; errors threshold for compute server or cluster
IdleTimeout   = 900     ; seconds, idle time before stopping server or cluster
StartTimeout  = 180     ; seconds, max time to start server or cluster
StopTimeout   = 180     ; seconds, max time to stop server or cluster

Servers   = cpc-1, cpc-2, cpc-3, cpc-4  ; computational servers or clusters

StartExe  = /bin/bash              ; default executable to start server, if empty then server is always ready, no startup
StopExe   = /bin/bash              ; default executable to stop server, if empty then server is always ready, no shutdown
ArgsBreak = -@-                    ; arguments delimiter in StartArgs or StopArgs line
                                   ; delimiter can NOT contain ; or # chars, which are reserved for # comments
                                   ; it can be any other delimiter of your choice, e.g.: +++
; StartArgs = ../etc/compute-start.sh    ; default command line arguments to start server, server name will be appended
; StopArgs  = ../etc/compute-stop.sh     ; default command line arguments to stop server, server name will be appended

[cpc-1]
Cpu       = 16          ; default: 1 CPU core
Memory    = 0           ; zero means no limits
StartArgs = ../etc/compute-start-4.sh-@-us-zone-b-@-cpc-1
StopArgs  = ../etc/compute-stop-4.sh-@-us-zone-b-@-cpc-1

[cpc-2]
Cpu       = 16          ; default: 1 CPU core
Memory    = 0           ; zero means no limits
StartArgs = ../etc/compute-start-4.sh-@-us-zone-c-@-cpc-2
StopArgs  = ../etc/compute-stop-4.sh-@-us-zone-c-@-cpc-2

[cpc-3]
Cpu       = 16          ; default: 1 CPU core
Memory    = 0           ; zero means no limits
StartArgs = ../etc/compute-start-4.sh-@-us-zone-d-@-cpc-3
StopArgs  = ../etc/compute-stop-4.sh-@-us-zone-d-@-cpc-3

[cpc-4]
Cpu       = 16          ; default: 1 CPU core
Memory    = 0           ; zero means no limits
StartArgs = ../etc/compute-start-4.sh-@-us-zone-a-@-cpc-4
StopArgs  = ../etc/compute-stop-4.sh-@-us-zone-a-@-cpc-4

; OpenMPI hostfile
;
; cpm   slots=1 max_slots=1
; cpc-1 slots=2
; cpc-3 slots=4
;
[hostfile]
HostFileDir = models/log
HostName = @-HOST-@
CpuCores = @-CORES-@
RootLine = cpm slots=1 max_slots=1
HostLine = @-HOST-@ slots=@-CORES-@

; MS-MPI machinefile (on Windows with Microsoft MPI)
;
; cpm:1
; cpc-1:2
; cpc-3:4
;
; [hostfile]
; HostFileDir = models\log
; HostName = @-HOST-@
; CpuCores = @-CORES-@
; RootLine = cpm:1
; HostLine = @-HOST-@:@-CORES-@

Oms is using StartExe and StartArgs in order to start each server. On Linux result of above job.ini is:

/bin/bash etc/compute-start.sh cpc-1

On Windows you can use cmd or PowerShell in order to control servers. Related part of job.ini can look like:

StartExe  = cmd                         ; default executable to start server, if empty then server is always ready, no startup
StopExe   = cmd                         ; default executable to stop server, if empty then server is always ready, no shutdown
StartArgs = /C-@-etc\compute-start.bat  ; default command line arguments to start server, server name will be appended
StopArgs  = /C-@-etc\compute-stop.bat   ; default command line arguments to stop server, server name will be appended

which result in following command to start server:

cmd /C etc\compute-start.bat cpc-1

Start and stop scripts can look like (Google cloud version):

#!/bin/bash
#
# start computational server, run as: 
#
# sudo -u $USER-NAME compute-start.sh host-name

srv_zone="us-zone-b"
srv_name="$1"

if [ -z "$srv_name" ] || [ -z "$srv_zone" ] ;
then
  echo "ERROR: invalid (empty) server name or zone: $srv_name $srv_zone"
  exit 1
fi

gcloud compute instances start $srv_name --zone $srv_zone
status=$?

if [ $status -ne 0 ];
then
  echo "ERROR $status at start of: $srv_name"
  exit $status
fi

# wait until MPI is ready

for i in 1 2 3 4; do

  sleep 10

  echo "[$i] mpirun -n 1 -H $srv_name hostname"

  mpirun -n 1 -H $srv_name hostname
  status=$?

  if [ $status -eq 0 ] ; then break; fi
done

if [ $status -ne 0 ];
then
  echo "ERROR $status from MPI at start of: $srv_name"
  exit $status
fi

echo "Start OK: $srv_name"

#!/bin/bash
#
# stop computational server, run as: 
#
# sudo -u $USER-NAME compute-stop.sh host-name

# set -e

srv_zone="us-zone-b"
srv_name="$1"

if [ -z "$srv_name" ] || [ -z "$srv_zone" ] ;
then
  echo "ERROR: invalid (empty) server name or zone: $srv_name $srv_zone"
  exit 1
fi

for i in 1 2 3 4 5 6 7; do

  gcloud compute instances stop $srv_name --zone $srv_zone
  status=$?

  if [ $status -eq 0 ] ; then break; fi

  sleep 10
done

if [ $status -ne 0 ];
then
  echo "ERROR $status at stop of: $srv_name"
  exit $status
fi

echo "Stop OK: $srv_name"

Azure cloud: front-end server and and auto scale of multiple back-end servers

In example below we are using small front-end server with 4 cores and 2 back-end servers: dc1, dc2 with 4 cores each. You are using public cloud and want to pay only for actual usage of back end servers:

server(s) must be started automatically when user (Alice or Bob) want to run the model;
server(s) must stop after model run completed to reduce cloud cost

Scripts below are also available at our GitHub↗

[Common]
LocalCpu      = 4     ; localhost CPU cores limit, localhost limits are applied only to non-MPI jobs
LocalMemory   = 0     ; gigabytes, localhost memory limit, zero means unlimited
MpiMaxThreads = 8     ; max number of modelling threads per MPI process
MaxErrors     = 10    ; errors threshold for compute server or cluster
IdleTimeout   = 900   ; seconds, idle time before stopping server or cluster
StartTimeout  = 90    ; seconds, max time to start server or cluster
StopTimeout   = 90    ; seconds, max time to stop server or cluster

Servers   = dc1, dc2     ; computational servers or clusters for MPI jobs

StartExe  = /bin/bash                     ; default executable to start server, if empty then server is always ready, no startup
StopExe   = /bin/bash                     ; default executable to stop server, if empty then server is always ready, no shutdown
StartArgs = ../etc/az-start.sh-@-dm_group ; default command line arguments to start server, server name will be appended
StopArgs  = ../etc/az-stop.sh-@-dm_group  ; default command line arguments to stop server, server name will be appended

ArgsBreak = -@-                    ; arguments delimiter in StartArgs or StopArgs line
                                   ; delimiter can NOT contain ; or # chars, which are reserved for # comments
                                   ; it can be any other delimiter of your choice, e.g.: +++

[dc1]
Cpu    = 4    ; default: 1 CPU core
Memory = 0

[dc2]
Cpu    = 4    ; default: 1 CPU core
Memory = 0

; OpenMPI hostfile
;
; dcm slots=1 max_slots=1
; dc1 slots=2
; dc2 slots=4
;
[hostfile]
HostFileDir = models/log
HostName    = @-HOST-@
CpuCores    = @-CORES-@
RootLine    = dm slots=1 max_slots=1
HostLine    = @-HOST-@ slots=@-CORES-@

Oms is using StartExe and StartArgs in order to start each server. On Linux result of above job.ini is similar to:

/bin/bash etc/az-start.sh dm_group dc1

Start and stop scripts can look like (Azure cloud version):

#!/bin/bash
#
# start Azure server, run as: 
#
# sudo -u $USER-NAME az-start.sh resource-group host-name

# set -e

res_group="$1"
srv_name="$2"

if [ -z "$srv_name" ] || [ -z "$res_group" ] ;
then
  echo "ERROR: invalid (empty) server name or resource group: $srv_name $res_group"
  exit 1
fi

# login

az login --identity
status=$?

if [ $status -ne 0 ];
then
  echo "ERROR $status from az login at start of: $res_group $srv_name"
  exit $status
fi

# Azure VM start 

az vm start -g "$res_group" -n "$srv_name"
status=$?

if [ $status -ne 0 ];
then
  echo "ERROR $status at: az vm start -g $res_group -n $srv_name"
  exit $status
fi

# wait until MPI is ready

for i in 1 2 3 4 5; do

  sleep 10

  echo "[$i] mpirun -n 1 -H $srv_name hostname"

  mpirun -n 1 -H $srv_name hostname
  status=$?

  if [ $status -eq 0 ] ; then break; fi
done

if [ $status -ne 0 ];
then
  echo "ERROR $status from MPI at start of: $srv_name"
  exit $status
fi

echo "Start OK: $srv_name"

#!/bin/bash
#
# stop Azure server, run as: 
#
# sudo -u $USER-NAME az-stop.sh resource-group host-name

# set -e

res_group="$1"
srv_name="$2"

if [ -z "$srv_name" ] || [ -z "$res_group" ] ;
then
  echo "ERROR: invalid (empty) server name or resource group: $srv_name $res_group"
  exit 1
fi

# login

az login --identity
status=$?

if [ $status -ne 0 ];
then
  echo "ERROR $status from az login at start of: $res_group $srv_name"
  exit $status
fi

# Azure VM stop

for i in 1 2 3 4; do

  az vm deallocate -g "$res_group" -n "$srv_name"

  if [ $status -eq 0 ] ; then break; fi

  sleep 10
done

if [ $status -ne 0 ];
then
  echo "ERROR $status at stop of: $srv_name"
  exit $status
fi

echo "Stop OK: $srv_name"

Linux cluster in cloud

Security consideration:

In wiki I am describing the most simple but least secure configuration, for your production environment you may want to:

use a separate web front-end server, separate oms control server with firewall in between
never use front-end web-server OS user as oms control server OS user
do not use the same OS user, like oms, but create a different for each of your model users, like Alice and Bob in example above.

Of course web front-end UI of your production environment must be protected by https:// with proper authentication and authorization. All that is out of scope of our wiki, please consult your organization security guidelines for it.

Also I am not describing here how to configure web-servers, how to create reverse proxy, install SSL certificates, etc. There are a lot of great materials on those topics around, just please think about security in a first place.

Cloud examples here assume Debian or Ubuntu Linux servers setup, you can use it for RedHat Linux with minimal adjustment. OpenM++ do support Microsoft Windows clusters, but configuring it is a more complex task and out of scope for that wiki.

Our simple cluster consist of from-end web-UI server with host name dm and multiple back-end computational servers: dc1, dc2,....

Front-end server OS setup

Front-end dm server must have some web-server installed, Apache or nginx for example, static IP and DNS records for your domain.

Choose Debian-11, Ubuntu 22.04 or RedHat 9 (Rocky, AlmaLinux) as your base system and create dm cloud virtual machine, at least 4 cores recommended. We will create two disks on dm: boot disk and fast SSD data disk where all users data and models are stored.

Set timezone, install openMPI and (optional) SQLite:

sudo timedatectl set-timezone America/Toronto

sudo apt-get install openmpi-bin
sudo apt-get install sqlite3

# check result:
mpirun hostname -A

Create and mount on /mirror SSD data disk to store all users data and models:

# init new SSD, use lsblk to find which /dev it is
lsblk

sudo mkfs.ext4 -m 0 -E lazy_itable_init=0,lazy_journal_init=0,discard /dev/sda

sudo mkdir /mirror
sudo mount -o discard,defaults /dev/sda /mirror

# check results:
ls -la /mirror

# add new disk to fstab, mount by UUID:
sudo blkid /dev/sda
sudo nano /etc/fstab

# add your UUID mount:
UUID=98765432-d09a-4936-b85f-a61da123456789 /mirror ext4 discard,defaults 0 2

Create NFS shares:

sudo mkdir -p /mirror/home
sudo mkdir -p /mirror/data

sudo apt install nfs-kernel-server

# add shares into exports:
sudo nano /etc/exports

# export user homes and data, data can be exported read-only, rw is not required
/mirror/home *(rw,sync,no_root_squash,no_subtree_check)
/mirror/data *(rw,sync,no_root_squash,no_subtree_check)

sudo systemctl restart nfs-kernel-server

# check results:
/sbin/showmount -e dm

systemctl status nfs-kernel-server

Create 'oms' service account, login disabled. I am using 1108 as user id and group id, but it is an example only and 1108 have no special meaning:

export OMS_UID=1108
export OMS_GID=1108

sudo addgroup --gid $OMS_GID oms
sudo adduser --home /mirror/home/oms --disabled-password --gecos "" --gid $OMS_GID -u $OMS_UID oms

sudo chown -R oms:oms /mirror/data

# increase stack size for models to 65 MB = 65536

sudo -u oms nano /mirror/home/oms/.bashrc

# ~/.bashrc: executed by bash(1) for non-login shells.
# openM++
# some models require stack size:
#
ulimit -S -s 65536

#
# end of openM++

Password-less ssh for oms service account:

sudo su -l oms
cd ~

mkdir .ssh

ssh-keygen -f .ssh/id_rsa -t rsa -N '' -C oms

# create .ssh/config with content below:
nano .ssh/config

Host *
    StrictHostKeyChecking no
    UserKnownHostsFile /dev/null
    LogLevel ERROR

cp -p .ssh/id_rsa.pub .ssh/authorized_keys

chmod 700 .ssh
chmod 600 .ssh/id_rsa
chmod 644 .ssh/id_rsa.pub
chmod 644 .ssh/config
chmod 644 .ssh/authorized_keys

exit    # logout from 'oms' user

# check ssh for oms user, it should work without any prompts, without any Yes/No questions:

sudo -u oms ssh dm

Check openMPI under 'oms' service account:

sudo -u oms mpirun hostname
sudo -u oms mpirun -H dm hostname

Done with dm server OS setup, reboot it and start dc1, dc2,... creating back-end servers.

Back-end computational servers setup

I am describing it for dc1, assuming you will create base image from it and use for all other back-end servers. On Azure it is make sense to create virtual machine scale set instead of individual servers.

Choose Debian-11, Ubuntu 22.04 or RedHat 9 (Rocky, AlmaLinux) as your base system and create dc1 cloud virtual machine, at least 16 cores recommended. It does not require a fast SSD, use regular small HDD because there are no model data stored in back-end, it is only OS boot disk, nothing else. Back-end servers should not be visible from the internet, it should be visible only from front-end dm server.

Set timezone and install openMPI::

sudo timedatectl set-timezone America/Toronto

sudo apt-get install openmpi-bin

# check result:
mpirun hostname -A

Mount NFS shares from dm server:

sudo mkdir -p /mirror/home
sudo mkdir -p /mirror/data

sudo apt install nfs-common

/sbin/showmount -e dm

sudo mount -t nfs dm:/mirror/home /mirror/home
sudo mount -t nfs dm:/mirror/data /mirror/data

systemctl status mirror-home.mount
systemctl status mirror-data.mount

# if above OK then add nfs share mounts into fstab:

sudo nano /etc/fstab

# fstab records:
dm:/mirror/home /mirror/home nfs defaults 0 0
dm:/mirror/data /mirror/data nfs defaults 0 0

# (optional) reboot node and make sure shares are mounted:

systemctl status mirror-home.mount
systemctl status mirror-data.mount

Create 'oms' service account, login disabled. It must have exactly the same user id and group id as oms user on dm, I am using 1108 as an example:

export OMS_UID=1108
export OMS_GID=1108

sudo /sbin/addgroup --gid $OMS_GID oms
sudo adduser --no-create-home --home /mirror/home/oms --disabled-password --gecos "" --gid $OMS_GID -u $OMS_UID oms

# check 'oms' sevice account access to shared files:

sudo -u oms -- ls -la /mirror/home/oms/.ssh/

Optional: if you are using Azure virtual machine scale set then cloud.init config can be:

#cloud-config
#
runcmd:
 - addgroup --gid 1108 oms
 - adduser --no-create-home --home /mirror/home/oms --disabled-password --gecos "" --gid 1108 -u 1108 oms

Check openMPI under 'oms' service account:

sudo -u oms mpirun hostname
sudo -u oms mpirun -H dc1 hostname
sudo -u oms mpirun -H dm hostname

Done with dc1 OS setup, clone it for all other back-end servers. After you created all back-end servers check openMPI from entire cluster, for example:

sudo -u oms mpirun -H dm,dc1,dc2,dc3,dc4,dc5,dc6,dc7,dc8,dc9,dc10 hostname

Now login back to your dm front-end and create standard openM++ directory structure at /mirror/data/, copy models, create user directories as it is described for "users" Alice and Bob above. Bob and Alice are your model users, they should not have OS login, user oms with disabled login is used to run the models on behalf of Alice and Bob.

I would also recommend to create:

at least one test "user" for your own tests, to verify system status and test and run the models when you publish it.
global administarator admin "user" to view your cluster status in cloud administrator UI
Models Library model-lib "user" to provide shared read-only storage from where users can copy models Models Library UI

/mirror/data/
    bin/
        oms    -> oms web service executable
        dbcopy -> dbcopy utility executable
    html/    -> web-UI directory with HTML, js, css, images...
    etc/     -> config files directory, contain template(s) to run models
             disk.ini  -> (optional) define shared models library and set disk storage quotas Bob and Alice
    log/     -> recommended log files directory
    alice/   -> user Alice "root" directory
        log/     -> Alice oms log files directory
        models/
              bin/  -> Alice's model.exe and model.sqlite directory
              log/  -> Alice's directory for models run log files
              doc/  -> models documentation directory
              home/ -> Alice's personal home directory
                  io/download  -> Alice's directory for download files
                  io/upload    -> Alice's directory to upload files
    bob/     -> user Bob "root" directory
        log/     -> Bob oms log files directory
        models/
              bin/  -> Bob's model.exe and model.sqlite directory
              log/  -> Bob's directory for models run log files
              doc/  -> models documentation directory
              home/ -> Bob's personal home directory
                  io/download  -> Bob's directory for download files
                  io/upload    -> Bob's directory to upload files
    model-lib/ -> Models Library "root" directory
        log/      -> Models Library oms log files directory
        models/
              bin/  -> Models Library model.exe and model.sqlite directory $BIN_DIR
              log/  -> Models Library directory for models run log files   $LOG_DIR
              doc/  -> Models Library documentation directory              $DOC_DIR
              home/ -> Models Library settings directory to store UI views
                  io/download  -> (optional) Models Library directory for download files
    job/  -> model run jobs control directory, it must be shared between all users
          job.ini   -> (optional) job control settings
          active/   -> active model run state files
          history/  -> model run history files
          past/     -> (optional) shadow copy of history folder, invisible to the end user
          queue/    -> model run queue files
          state/    -> jobs state and computational servers state files
    oms/    -> oms init.d files, see examples on our GitHub
    oms.ini -> oms config, see content above
    test/   -> user test "root" directory, for admin internal use
            -> .... user test subdirectories here

Above there is also oms/ directory with init.d files to restart oms when front-end dm server is rebooted. You can find examples of it at our GitHub↗.

Oms in cloud setup - openmpp/openmpp.github.io GitHub Wiki

Google cloud: front-end server and and auto scale of multiple back-end servers

Azure cloud: front-end server and and auto scale of multiple back-end servers

Linux cluster in cloud

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