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Would you be interested to add support for https://johannesbuchner.github.io/UltraNest/ ?
The interface should be very similar to dynesty.
UltraNest is a very reliable tuning-parameter-free algorithm. It can be faster than dynesty, especially if you take advantage of vectorization.
It also supports resuming from disk and MPI parallelisation, if that is useful to you.
The text was updated successfully, but these errors were encountered:
Very Cool! I was not aware of UltraNest, thanks a lot! including another sampler to the Exostriker should be easy in the core, but a bit time-consuming on the GUI part. Yet, I will definitely have a look and experiment! Comments are likely to follow, so stay tuned.
After a long time on this request, I am ready to experiment! @JohannesBuchner I could not find out of the box whether is possible with UltraNest to control the number of Threads/CPUs to be used in a multiprocessing. For instance, with dynesty I use something like:
from pathos.pools import ProcessPool as Pool
thread = Pool(ncpus=N_threads)
sampler = dynesty.NestedSampler(partial_func, prior_transform, ndim, nlive=nwalkers, pool = thread,
queue_size=threads, sample = dynesty_samp, bound = ns_bound)
sampler.run_nested(print_progress=print_progress,dlogz=stop_crit,
maxiter = ns_maxiter, maxcall = ns_maxcall )
thread.close()
thread.join()
thread.clear()
Can you please comment/advice how can I pass a pool to UltraNest, similar to what Dynesty and emcee support?
Sorry if this was obvious and I didn't see in in your documentation!
UltraNest can be transparently parallelized with MPI. Instead, the program is run with mpiexec (or mpirun). This runs N separate processes, which communicate with each other. No parallelisation code in exostriker is required. MPI is more powerful for distributing than python pools, which I recall often have the issue of serializing and sending the code to be executed to other cores.
Would you be interested to add support for https://johannesbuchner.github.io/UltraNest/ ?
The interface should be very similar to dynesty.
UltraNest is a very reliable tuning-parameter-free algorithm. It can be faster than dynesty, especially if you take advantage of vectorization.
It also supports resuming from disk and MPI parallelisation, if that is useful to you.
The text was updated successfully, but these errors were encountered: