juliet can be used in multiple cores in order to speed up the data fitting processes. If using MultiNest this is done via OpenMPI, whereas via dynesty this is done using internal python multi-threading capabilities. In what follows, we explain how to perform multiple core runs with juliet.

Multithreading with MultiNest

In order to use the multi-threading capabilities with juliet, you have to have OpenMPI in your computer. You can check if this is available in your system by opening a terminal and writing mpirun. If this command prompts you to something similar to:

mpirun could not find anything to do.

It is possible that you forgot to specify how many processes to run
via the "-np" argument.

Then that’s it, you have OpenMPI. If not, installing it is simple. You just have to follow the instructions to compile OpenMPI [here]. Once this is done, you have to install mpi4py, which is easily done via pip:

pip install mpi4py

Once all this is done you are good to go! To run a juliet run on X number of cores, simply do:

mpirun -np X python

Multithreading with dynesty

Applying multi-threading capabilities for dynesty is much simpler than for MultiNest. This can be automatically activated once a juliet.load object is made to fit the data — simply define the number of threads you want to use and juliet will assume you need multi-threading capabilities. So, for example, to use juliet with 6 number of cores, in a session you would do:

# Load and fit dataset with juliet:
dataset = juliet.load(priors=priors, t_lc = times, y_lc = fluxes, \
                   yerr_lc = fluxes_error, out_folder = 'hats46')

results =, dynesty_nthreads = 6)