Running on the Amazon EC2 cloud

Note

This section is work in progress. Let us know about possible corrections/improvements if you try the methods presented here.

Installing and running single jobs

This is the preferred method for running individual jobs.

First of all, configure and launch a Linux image. For most cosmological applications, we recommend choosing an Ubuntu 18.04 instance with about 16 cores (4 MPI processes threading across 4 cores each) and 32 Gb of RAM (8 Gb per chain). A good choice, following that logic, would be a c5d.4xlarge (compute optimized) instance. Set up for it at least 10Gb of storage.

Now install the requisites with

$ sudo apt update && sudo apt install gcc gfortran g++ openmpi-bin openmpi-common libopenmpi-dev libopenblas-base liblapack3 liblapack-dev make
$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
$ bash miniconda.sh -b -p $HOME/miniconda
$ export PATH="$HOME/miniconda/bin:$PATH"
$ conda config --set always_yes yes --set changeps1 no
$ conda create -q -n cobaya-env python=3.7 scipy matplotlib cython PyYAML pytest pytest-forked flaky
$ source activate cobaya-env
$ pip install mpi4py

And install cobaya (and optionally PolyChord and some cosmology requisites) with

$ pip install cobaya

$ cobaya-install cosmo --packages-path cobaya_packages

Now you are ready to run some samples.

As an example, you can just copy the input at Basic cosmology runs, paste it in a file with nano and save it to planck.yaml.

To run with X MPI processes, each creating at most Y threads (in our recommended configuration, X=Y=4), do

$ mpirun -n X --map-by socket:PE=Y  cobaya-run planck.yaml -p cobaya_packages -o chains/planck