Python software on Archer: Difference between revisions
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module load pc-numpy |
module load pc-numpy |
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module load pc-scipy |
module load pc-scipy |
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2) Enter virtual environment containing all additional modules (AFTER loading python and the central modules |
2) Enter virtual environment containing all additional modules (AFTER loading python and the central modules) |
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source /work/e507/e507/ap837/code/venv_pyscf/bin/activate |
source /work/e507/e507/ap837/code/venv_pyscf/bin/activate |
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3) If code uses intel libraries: (errors like: |
3) If code uses intel libraries: (errors like: |
Revision as of 15:43, 30 August 2017
For running Python on work nodes on Archer, there are python-compute (native) and anaconda-compute modules available. Users are discouraged from using anaconda-compute because it is not optimised for running on Archer. There are some preinstalled packages for python-compute and they begin with pc-. For installation of additional packages, virtual environments are encouraged.
For more info see: http://www.archer.ac.uk/documentation/user-guide/python.php
When compiling Python software (like PySCF) Archer by default builds all libraries as static libraries. This leads to errors like:
ImportError Cannot import name ...
or
File "/work/y07/y07/cse/numpy/1.9.2-libsci/lib/python2.7/site-packages/numpy/ctypeslib.py", line 128, in load_library raise OSError("no file with expected extension") OSError: no file with expected extension
and other errors.
To prevent dynamic libraries from becoming static you must:
export CRAYPE_LINK_TYPE=dynamic
before the compilation. For more information see: http://www.archer.ac.uk/documentation/user-guide/development.php#sec-4.6
In submission script itself, do not forget to:
1) Load python and all modules supplied centrally
module load python-compute module load pc-numpy module load pc-scipy
2) Enter virtual environment containing all additional modules (AFTER loading python and the central modules)
source /work/e507/e507/ap837/code/venv_pyscf/bin/activate
3) If code uses intel libraries: (errors like:
OSError: libmkl_intel_lp64.so: cannot open shared object file: No such file or directory OSError: libiomp5.so: cannot open shared object file: No such file or directory
) do:
source /opt/intel/bin/compilervars.sh intel64
4) When using numpy, the following error may occur:
Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so
Tha solution is to do:
export LD_PRELOAD=/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so
as found here http://debugjournal.tumblr.com/post/98401758462/intel-mkl-dynamic-link-library-error.
5) For PySCF, since we are using old numpy, you need to comment out warning in __init__.py in the pyscf folder
#if LooseVersion(numpy.__version__) <= LooseVersion('1.8.0'): # raise SystemError("You're using an old version of Numpy (%s). " # "It is recommended to upgrad numpy to 1.8.0 or newer. \n" # "You still can use all features of PySCF with the old numpy by removing this warning msg. " # "Some modules (DFT, CC, MRPT) might be affected because of the bug in old numpy." % # numpy.__version__)