12.6. Test the Python/LAMMPS interface
To test if LAMMPS is callable from Python, launch Python interactively and type:
>>> from lammps import lammps >>> lmp = lammps()
If you get no errors, you’re ready to use LAMMPS from Python. If the 2nd command fails, the most common error to see is
OSError: Could not load LAMMPS dynamic library
which means Python was unable to load the LAMMPS shared library. This typically occurs if the system can’t find the LAMMPS shared library or one of the auxiliary shared libraries it depends on, or if something about the library is incompatible with your Python. The error message should give you an indication of what went wrong.
You can also test the load directly in Python as follows, without first importing from the lammps.py file:
>>> from ctypes import CDLL >>> CDLL("liblammps.so")
If an error occurs, carefully go through the steps on the Build_basics doc page about building a shared library and the Python_install doc page about insuring Python can find the necessary two files it needs.
12.6.1. Test LAMMPS and Python in serial:
To run a LAMMPS test in serial, type these lines into Python interactively from the bench directory:
>>> from lammps import lammps >>> lmp = lammps() >>> lmp.file("in.lj")
Or put the same lines in the file test.py and run it as
% python test.py
Either way, you should see the results of running the in.lj benchmark on a single processor appear on the screen, the same as if you had typed something like:
lmp_g++ -in in.lj
12.6.2. Test LAMMPS and Python in parallel:
To run LAMMPS in parallel, assuming you have installed the PyPar package as discussed above, create a test.py file containing these lines:
import pypar from lammps import lammps lmp = lammps() lmp.file("in.lj") print "Proc %d out of %d procs has" % (pypar.rank(),pypar.size()),lmp pypar.finalize()
To run LAMMPS in parallel, assuming you have installed the mpi4py package as discussed above, create a test.py file containing these lines:
from mpi4py import MPI from lammps import lammps lmp = lammps() lmp.file("in.lj") me = MPI.COMM_WORLD.Get_rank() nprocs = MPI.COMM_WORLD.Get_size() print "Proc %d out of %d procs has" % (me,nprocs),lmp MPI.Finalize()
You can either script in parallel as:
% mpirun -np 4 python test.py
and you should see the same output as if you had typed
% mpirun -np 4 lmp_g++ -in in.lj
Note that if you leave out the 3 lines from test.py that specify PyPar commands you will instantiate and run LAMMPS independently on each of the P processors specified in the mpirun command. In this case you should get 4 sets of output, each showing that a LAMMPS run was made on a single processor, instead of one set of output showing that LAMMPS ran on 4 processors. If the 1-processor outputs occur, it means that PyPar is not working correctly.
Also note that once you import the PyPar module, PyPar initializes MPI for you, and you can use MPI calls directly in your Python script, as described in the PyPar documentation. The last line of your Python script should be pypar.finalize(), to insure MPI is shut down correctly.
12.6.3. Running Python scripts:
Note that any Python script (not just for LAMMPS) can be invoked in one of several ways:
% python foo.script % python -i foo.script % foo.script
The last command requires that the first line of the script be something like this:
#!/usr/local/bin/python #!/usr/local/bin/python -i
where the path points to where you have Python installed, and that you have made the script file executable:
% chmod +x foo.script
Without the “-i” flag, Python will exit when the script finishes. With the “-i” flag, you will be left in the Python interpreter when the script finishes, so you can type subsequent commands. As mentioned above, you can only run Python interactively when running Python on a single processor, not in parallel.