Running on the Amazon EC2 cloud¶
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=3.9 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
To run with
X MPI processes, each creating at most
Y threads (in our recommended configuration,
$ mpirun -n X --map-by socket:PE=Y cobaya-run planck.yaml -p cobaya_packages -o chains/planck