I ran into the same problem. I searched the wiki but couldn't find a solution. From the description provided in the thread is not obvious how to proceed. Is there a working script that could be used
as a starting point to run parallel jobs in a cluster using PBS?
I managed to submit jobs using independent modeller.py scripts differing in the start and end model parameter only: in file modeller01.py start=1 end=1
in file modeller02.py start=2 end=2 each one of these run in a different node (jobs submitted with qsub), but the two resulting models 1s58.B99990001.pdb and 1s58.B99990002.pdb are exactly the same, which brings me to my second question:
Is there a way to specify the initial random seed used by each modeller script?
Starr Hazard wrote:
The references to parallelization seem to point rather strongly
to the SGE scheduler...
Not at all - the 'job' class is simply a bag of 'slave' objects. There
is no requirement that you use any particular resource management
system. For example, local_slave starts up a slave on the local machine
(ideal if you have a multi-core machine). ssh_slave starts up a slave on
a machine accessible by ssh, ideal if you have a cluster set up to allow
passwordless ssh (or rsh) to individual nodes. The only slave classes
which use SGE are sge_pe_slave and sge_qsub_slave. I wrote those because
we happen to have an SGE cluster. But there's no reason why you couldn't
write your own slaves to use PBS mechanisms.
Well, obviously not sge_qsub_job, as the name would suggest, since that
is a convenience class for SGE. Just use the regular job base class. If
you then have a traditional ssh-to-any-node setup, all you then need to
do is loop over the nodes in your machine file and make an ssh_slave for
each one. Alternatively, it would be pretty simple to write a class that
used the PBS TM mechanism via something like mpiexec. I suggest you put
the result into the Modeller wiki, so that other PBS users can use or
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