Install EMOD on Linux¶
To install EMOD on Linux computers, follow the steps below. You will install the pre-built Eradication binary for Linux and all supporting software needed to run simulations locally. Optionally, you can install Python virtual environments, software to plot the output of simulations, and EMOD input files for various regions.
The Eradication binary for Linux is tested and supported on a CentOS 7.1 virtual machine on Azure. It has also been successfully built and run on Ubuntu, SUSE, and Arch, but has not been tested and is not supported on those Linux distributions.
If you want to download and modify the EMOD source code and build the Eradication binary for Linux yourself, see EMOD source code installation.
The setup script installs most prerequisite software, including Python and the Python packages dateutil, six, pyparsing, NumPy, and matplotlib. Other prerequisites, such as Boost 1.61.0 and Microsoft MPI v8, are declared by the script as required. Because the installation instructions for these packages will vary based on the particular Linux distribution you are running, installation instructions are not included here.
IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.
The script provides the option to install input files that describe the demographics, migration patterns, and climate of many different locations across the world. While the script installs a pre-built version of the Eradication binary for Linux, it also provides the option of installing the EMOD source code. For information on building the Eradication binary for Linux from source code, see EMOD source code installation.
Before you begin, you must have the following:
sudo privileges to install packages
15 GB free in your home directory (if you install the EMOD source code and input files)
An Internet connection
On GitHub on the EMOD releases page, download and run the PrepareLinuxEnvironment.sh script.
Respond to the prompts for information while the script is running.
Set the EMOD_ROOT environment variable to the path to the EMOD source path:
Include Scripts and . in the path:
If you want to run simulations in the same session that you updated EMOD_ROOT and the Scripts path, reload the .bashrc file using
Download the Eradication binary for Linux for CentOS 7.1 (not Eradication.exe for Windows). See on EMOD releases on GitHub. Save to a local drive, such as the desktop.
Python virtual environments enable you to isolate your Python environments from one
another and give you the option to run multiple versions of Python on the same computer. When using a
virtual environment, you can indicate the version of Python you want to use and the packages you
want to install, which will remain separate from other Python environments. You may use
venv is recommended and included with Python 3.3+.
Navigate to the location where you want to create the virtual environment directory and enter the following to create a environment called “idm”, or other desired name:
python -m venv idm
To activate the virtual environment, enter the following:
Verify that the command prompt displays “idm” at the beginning, which indicates that you are in the idm virtual environment. For example:
To verify you are using Python 3.6 64-bit, enter the following:
When you are done working in this virtual environment, deactivate the environment. The environment will be saved and you can reactivate it at any time. To deactivate it, enter the following:
None of the following plotting software is required to run simulations with EMOD, but they are useful for creating graphs from and evaluating the model output. In addition, EMOD provides many Python scripts for analyzing data.
R is a free software environment for statistical computing and graphics.
Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).
MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.
Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.
Install the Git LFS plugin, if it is not already installed.
For Windows users, download the plugin from https://git-lfs.github.com.
For CentOS users, the plugin is included with the PrepareLinuxEnvironment.sh script.
Using a Git client or Command Prompt window, clone the input data repository to retrieve the metadata:
git clone https://github.com/InstituteforDiseaseModeling/EMOD-InputData.git
Navigate to the directory where you downloaded the metadata for the input files.
Download the actual data on your local machine:
git lfs fetch
Replace the metadata in the files with the actual data:
git lfs checkout