my automodel run well serial, but molpdf is inmediatly exceeded when run in parallel
![](https://secure.gravatar.com/avatar/7ad49ce814f0d692413c19ca814f5724.jpg?s=120&d=mm&r=g)
Hello All
The script below exceeds molpdf limits immediately when run this automodel job on parallel, but run starts with a low molpdf and ends well when commenting the line "a.use_parallel_job(j)" Any hint of how to solve will be appreciate? Mario
from modelito import MyModel from modeller.parallel import * icycle=50 log.minimal() # request minimal output env = Environ() # create a new MODELLER environment to build this model in icycle=str(int(icycle)+1) # directories for input atom files env.io.atom_files_directory = ['.', 'trajs/'] # Give less weight to all soft-sphere restraints: env.schedule_scale = physical.values(default=1.0, soft_sphere=0.7) # Very thorough VTFM optimization:
j = Job() for i in range(1): j.append(local_slave())
# Comparative modeling by the AutoModel class env.io.hetatm=True a = MyModel(env, alnfile = 'test.ali', # alignment filename knowns = ('ro','odo'), # codes of the templates sequence = 'TEST') # code of the target a.starting_model= int(icycle) # index of the first model a.ending_model = int(icycle)+ 0 # index of the last model # (determines how many models to calculate) a.library_schedule = autosched.slow #a.restraints.write(file='restraints.rsr') # Thorough MD optimization: a.md_level = refine.slow # Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6 a.use_parallel_job(j) a.repeat_optimization = 3 a.max_molpdf = 1e6 a.make() # do the actual comparative modeling
![](https://secure.gravatar.com/avatar/88936d77693bc91ead290eb113b83e4a.jpg?s=120&d=mm&r=g)
On 4/7/24 6:47 AM, Mario Bianchet via modeller_usage wrote: > The script below exceeds molpdf limits immediately when run this > automodel job on parallel, but run starts with a low molpdf and ends > well when commenting the line "a.use_parallel_job(j)" Any hint of how > to solve will be appreciate?
The model building procedure is the same for both serial and parallel jobs; however, parallel jobs run in a slightly different environment (a separate process, with the random number generator in a different state). So you should not expect to get exactly the same models.
There is always a possibility when building models that the randomized starting coordinates are such that the optimizer cannot recover (e.g. a knot in the backbone, or atoms placed on top of each other) and in some cases this will trigger the max_molpdf cutoff. The solution is the same though for both serial and parallel jobs - build multiple models and discard the bad ones.
Ben Webb, Modeller Caretaker
participants (2)
-
Mario Bianchet
-
Modeller Caretaker