Skip to content

Linux Quick Start Developer

amc1999 edited this page Oct 22, 2021 · 14 revisions

Where is OpenM++

It is recommended to start from desktop version of openM++.

You need to use cluster version of openM++ to run the model on multiple computers in your network, in cloud or HPC cluster environment. OpenM++ is using MPI to run the models on multiple computers. Please check Model Run: How to Run the Model page for more details.

Build on Linux

Tested platforms:

  • Debian 10, MX Linux 19, Ubuntu 20.04
  • RedHat (CentOS) 8 supported up to openM++ release 05 October 2021, v1.9.2, next supported RedHat (RockyLinux) version is 9
  • g++ >= 8.3
  • (optional) MPI, i.e.: OpenMPI >= 3.1 or MPICH (other MPI implementations expected to work but not tested)
  • (optional) OpenMPI >= 4.0 on RedHat (CentOS) >= 8.3 (OpenMPI was broken on CentOS 8.1)

It is also occasionally tested on openSUSE, Mint, Manjaro, Solus and others.

It is not supported, but may also work on older versions, for example Ubuntu 18.04.

Build on Ubuntu 20.04

There is a minor incompatibility of shared libraries between Ubuntu 20.04 and Debian 10. As result you need to rebuild our model run-time libraries before building you own model:

  • download and unpack openM++ into any folder:
wget https://github.com/openmpp/main/releases/download/v1.8.6/openmpp_debian_20210415.tar.gz
tar xzf openmpp_debian_20210415.tar.gz
  • rebuild model run-time libraries:
cd openmpp_debian_20210415/openm
wget https://github.com/openmpp/main/releases/download/v1.8.6/openmpp_debian_20210415.tar.gz
tar xzf openmpp_debian_20210415.tar.gz

Build debug version of the model

You can use any of test models makefile, except of modelOne, as starting point to develop your own model. Below we are using NewCaseBased model as example.

To build and run debug version of the model use desktop (non-MPI) version of openM++:

  • check your g++ --version:
g++ (Debian 8.3.0-6) 8.3.0
g++ (Ubuntu 9.3.0-10ubuntu2) 9.3.0
g++ (GCC) 8.3.1 20191121 (Red Hat 8.3.1-5)
  • download and unpack openM++
wget https://github.com/openmpp/main/releases/download/v1.8.3/openmpp_debian_20210304.tar.gz
tar xzf openmpp_debian_20210304.tar.gz
  • build debug version of NewCaseBased model and "publish" it ("publish" do create NewCaseBased.sqlite database with default input data set)
cd openmpp_debian_20210304/models/NewCaseBased/
make all publish
  • run the model
cd ompp-linux/bin
./NewCaseBasedD
2017-06-06 19:59:12.0429 NewCaseBased
2017-06-06 19:59:12.0449 Run: 103
2017-06-06 19:59:12.0449 Get fixed and missing parameters
2017-06-06 19:59:12.0449 Get scenario parameters
2017-06-06 19:59:12.0450 Sub-value 0
2017-06-06 19:59:12.0450 compute derived parameters
2017-06-06 19:59:12.0450 Initialize invariant entity data
2017-06-06 19:59:12.0450 Member=0 simulation progress=0%
..........
2017-06-06 19:59:12.0505 member=0 write output tables - finish
2017-06-06 19:59:12.0508 Writing Output Tables Expressions
2017-06-06 19:59:12.0520 Done.

Build release version of the model

Make executable, "publish" and run NewCaseBased test model:

cd openmpp_debian_20210304/models/NewCaseBased/
make RELEASE=1 clean-all
make RELEASE=1 all publish
cd ompp-linux/bin
./NewCaseBased

Rebuild all test models

Make executables, "publish" (create model.sqlite database file) and run all test models:

cd openmpp_debian_20210304/models/
make RELEASE=1 clean-all
make RELEASE=1 all publish run publish-all

results are in openmpp_debian_20210304/models/bin directory

OM_ROOT: How to separate model folder and openM++ release folder

If you want to keep model development folder(s) outside of openM++ release directory then set OM_ROOT environment variable to specify openM++ release location. For example if your model is in $HOME/my-models/BestModel then to build it do any of:

cd my-models/BestModel
OM_ROOT=openmpp_debian_20210304 make all publish run

Or edit $HOME/my-models/BestModel/makefile to set OM_ROOT:

ifndef OM_ROOT
  OM_ROOT = $(HOME)/openmpp_debian_20210304
endif

Or add export OM_ROOT=$HOME/openmpp_debian_20210304 into your .bash_profile

Build cluster version of the model to run on multiple computers over network

Make sure you have MPI installed and configured. For example, on RedHat (CentOS) you may need to load MPI module: module load mpi/openmpi-x86_64

  • download and unpack cluster version of openM++, i.e.:
wget https://github.com/openmpp/main/releases/download/v1.8.3/openmpp_debian_mpi_20210304.tar.gz
tar xzf openmpp_debian_mpi_20210304.tar.gz

please notice name of cluster version archive has mpi in it, i.e. openmpp_debian_mpi_20210304.tar.gz

  • make executable and "publish" (create model.sqlite database file) of NewCaseBased test model:
cd openmpp_debian_mpi_20210304/models/NewCaseBased/
make RELEASE=1 OM_MSG_USE=MPI all publish
  • run 3 instances of NewCaseBased on 3 hosts to compute 16 subsamples using 4 threads
cd ompp-linux/bin
mpirun -n 3 -H omm,om1,om2 NewCaseBased_mpi -OpenM.Subvalues 16 -OpenM.Threads 4
2017-06-06 20:15:12.0050 NewCaseBased
2017-06-06 20:15:12.0173 NewCaseBased
2017-06-06 20:15:12.0200 NewCaseBased
2017-06-06 20:15:13.0148 Parallel run of 3 modeling processes, 4 thread(s) each
2017-06-06 20:15:13.0162 Run: 102
2017-06-06 20:15:13.0163 Get fixed and missing parameters
2017-06-06 20:15:13.0163 Get scenario parameters
2017-06-06 20:15:13.0164 compute derived parameters
2017-06-06 20:15:13.0164 Initialize invariant entity data
2017-06-06 20:15:13.0161 Run: 102
..........
2017-06-06 20:15:13.0224 member=0 write output tables - finish
2017-06-06 20:15:13.0354 Done.
2017-06-06 20:15:13.0352 Done.
2017-06-06 20:15:13.0353 Done.

You can use any of test models makefile, except of modelOne, as starting point to develop your own model.

Home

Getting Started

Model development in OpenM++

Using OpenM++

Model Development Topics

OpenM++ web-service: API and cloud setup

Using OpenM++ from Python and R

Docker

OpenM++ Development

OpenM++ Design, Roadmap and Status

OpenM++ web-service API

GET Model Metadata

GET Model Extras

GET Model Run results metadata

GET Model Workset metadata: set of input parameters

Read Parameters, Output Tables or Microdata values

GET Parameters, Output Tables or Microdata values

GET Parameters, Output Tables or Microdata as CSV

GET Modeling Task metadata and task run history

Update Model Profile: set of key-value options

Update Model Workset: set of input parameters

Update Model Runs

Update Modeling Tasks

Run Models: run models and monitor progress

Download model, model run results or input parameters

Upload model runs or worksets (input scenarios)

Download and upload user files

User: manage user settings

Model run jobs and service state

Administrative: manage web-service state

Clone this wiki locally