Troubleshooting in cosmological runs

This section will be progressively filled with the most common problems that our users encounter, so don’t hesitate to open an issue/PR in GitHub if you think there is something worth including here.

General troubleshooting advice

If you are getting an error whose cause is not immediately obvious, try evaluating your model at a point in parameter space where you expect it to work. To do that, either substitute your sampler for the dummy sampler evaluate, or use the model wrapper instead of a sampler and call its logposterior method.

You can increase the level of verbosity running with debug: True (or adding the --debug flag to cobaya-run). Cobaya will print what each part of the code is getting and producing, as well as some other intermediate info. You can pipe the debug output to a file with cobaya-run [input.yaml] --debug > file or by setting debug: [filename].

MPI runs: getting one MCMC chain only

If your MPI run is only producing one chain (just [prefix].1.txt, no higher numbers), your Python MPI wrapper is not working correctly. Please take a look at this section.

Using PolyChord in cosmological runs

PolyChord explores the full posterior domain much more thoroughly than MCMC does, in order to get an accurate estimation of the model evidence. Due to that a couple of caveats are in order:

  • The Planck likelihood does not often deal gracefully with extreme values of the power spectrum: some times, when at the tails of the posterior, it may produce a segfault (the error message will contain something like Segmentation Fault or Signal 11, and a references to a files such as clik/lkl.[some_text].so). Not much can be done about this, except for reducing the prior boundaries for the cosmological parameters by hand.

  • For most cases, CAMB and CLASS should produce very similar evidences for the same models, but only as long as the posterior does not extend towards regions which are unphysical for one of the Boltzmann codes and not the other one. You should not see any effect for most LCDM extensions, but keep an eye open if comparing CAMB and CLASS results for exotic models.

  • [WIP: typical running time]

Sampling stuck or not saving any point

If your sampler appears to be making no progress, your likelihood or theory code may be failing silently, and thus assuming a null likelihood value (this is intended default behaviour, since cosmological theory codes and likelihoods tend to fail for extreme parameter values). If that is the case, you should see messages about errors being ignored when running with debug: True. To stop when one of those errors occur, set the option stop_at_error: True for the relevant likelihood or theory code.

Low performance on a cluster

If you notice that Cobaya is performing unusually slow on your cluster compared to your own computer, run a small number of evaluations using the evaluate sampler sampler with timing: True: it will print the average computation time per evaluation and the total number of evaluations of each part of the code, so that you can check which one is the slowest. You can also combine timing: True with any other sampler, and it will print the timing info once every checkpoint, and once more at the end of the run.

  • If CAMB or CLASS is the slowest part, they are probably not using OpenMP parallelisation. To check that this is the case, try running top on the node when Cobaya is running, and check that CPU usage goes above 100% regularly. If it does not, you need to allocate more cores per process, or, if it doesn’t fix it, make sure that OpenMP is working correctly (ask your local IT support for help with this).

  • If it is some other part of the code written in pure Python, numpy may not be taking advantage of parallelisation. To fix that, follow these instructions.

Running out of memory

If your job runs out of memory at at initialisation of the theory or likelihoods, you may need to allocate more memory for your job.

If, instead, your jobs runs out of memory after a number of iterations, there is probably a memory leak somewhere. Python rarely leaks memory, thanks to its garbage collector, so the culprit is probably some external C or Fortran code.

If you have modified C code, e.g. CLASS, make sure that every alloc is followed by the corresponding free. Otherwise, a new array will be created at each iteration while the old one will not be deleted.

You can use e.g. Valgrind to monitor memory usage.


In particular, for CLASS, check out this warning concerning moving the CLASS folder after compilation.

Secondary MPI processes not dying

We have noticed that hitting Control-c twice in a row prevents the termination signal to be propagated among processes, letting some or all secondary ones running after the primary one is killed, so that they need to be killed manually. Please, be patient!

Secondary processes not dying is something that should not happen when running on a cluster. If this happens, please report to us via GitHub, including as much information about the run as possible.