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installation_nmma [2022/02/01 10:40]
theoastro 		installation_nmma [2023/06/10 18:51] (current)
theoastro
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 ===== Installation ===== ===== Installation =====
  
-=== Setting up a virtual environment (Optional) ===+For the installation of NMMA on smaller servers (e.g. our Uni Potsdam machines) a conda environment can be used for installation, while for larger cluster such as HAWK or Supermuc, a python virtual-environment is the better way to install NMMA. The main reason for the latter is that the mpi4py python package installed within a conda environment can be incompatible with default modules given by a specific cluster.
  
-While it is not strictly necessary, it reduces possible conflicts if one installs python packages that do not need to be available system-wide into local, virtual environments. An easy option to setup such virtual environments is the [[https://docs.python.org/3/library/venv.html#module-venv|venv]] module.+=== Installation on Uni Potsdam machines ===
  
-  python3 -m venv /path/to/new/virtual/environment +Here, we use a [[https://docs.conda.io/en/latest/miniconda.html#| mini conda environment]], in which separate virtual environments can be created with: 
- +  conda create --name <name_of_env> python=3.8 
-This creates a local environment under /path/to/new/virtual/environment, which can be loaded via: +For the conda environment creation, make sure to use python=3.8 as all dependencies of all included packages work best with this python version. 
-  source /path/to/new/virtual/environment/bin/activate +
- +
-Another way is to install a [[https://docs.conda.io/en/latest/miniconda.html#| mini conda environment]], in which separate virtual environments can be created with: +
-  conda create --name <name> python=<version>+
  
 In order to activate the conda environment, you can use: In order to activate the conda environment, you can use:
   conda activate <name_of_env>   conda activate <name_of_env>
  
 +In order to allow python programs to exploit multiple processors, the [[https://mpi4py.readthedocs.io/en/stable/#| mpi4py]] package needs to be installed first using 
 +  conda install mpi4py
  
-=== Cloning and installation ===+NMMA can be source cloned from the git repo with the following command: 
 +  git clone https://github.com/nuclear-multimessenger-astronomy/nmma.git
  
-With activated virtual environment, basic packages need to be installed with: +We change into the cloned NMMA directory and pip install it using: 
-  pip install afterglowpy extinction dill multiprocess bilby lalsuite+  pip install -r requirements.txt 
 +  pip install .
  
-In order to allow python programs to exploit multiple processors, the [[https://mpi4py.readthedocs.io/en/stable/#| mpi4py]] packages needs to be installed using: +=== Installation on Super-Computer Clusters (e.g. Supermuc) === 
-  conda install mpi4py+For larger computer clusters, a virtual python-based environment is recommended which can be created using: 
 +  python3 -m venv /path/to/new/virtual/environment
  
-In addition, install: +This creates a local environment under /path/to/new/virtual/environment, which can be loaded via: 
-  conda install -c conda-forge parallel_bilby+  source /path/to/new/virtual/environment/bin/activate
  
-Currently, in order to install NMMA you would need to have an account at bitbucket and you need to get invited by Peter T. H. Pang (mailto: <thopang@nikhef.nl>) to get access.  With your username at hand, you can clone the NMMA python package. For that, it is recommendable to create a separate source directory (src/) and clone it in there with: +With activated virtual environment, we now install **mpi4py** this time with pip: 
-  git clone https://<your_user_name>@bitbucket.org/petertsunhopang/nmma.git+  pip install mpi4py 
  
-This will create an nmma folder, in which you can install the NMMA package using: +If this is done, all the other steps can be taken from the installation version explained above, meaning source cloning NMMA and pip installing it with potential on-top installations explained below. 
-  pip install .+ 
 +=== Optional on-top installations (applicable for both installation versions) === 
 +Some further installations (use-case dependent) might be needed. For GRB based analyses make sure to install 
 +  pip install afterglowpy 
 +For using tensorflow-based interpolation during an inference make sure to install 
 +  pip install imgaug==0.2.6 
 +  pip install tensorflow-cpu 
  
-=== Optional on-top installations === 
-Several tasks require the Bayesian inference tool [[https://github.com/JohannesBuchner/MultiNest|Multinest]], which can be installed using: 
-  conda install pymultinest 
Last modified: le 2022/02/01 10:40

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