The information below is only for backup and some install info might still be useful to other people. The new system is Jureca and you can find the info here:
http://www.lofar.org/wiki/doku.php?id=public:lofar_processing_juelich

Here will be some notes on the Juropa installation and how to use the software on Juropa at the Juelich Supercomuting Centre.
Work in progress but useful anyhow. The next section Using Juropa for LOFAR Processing contains the information you need to use the system as of May 2014. The sections below these up to date information are kept for archiving purpose (still useful).

Here are the most recent information on how to make use of Juropa for LOFAR Processing. Last edit June 2014.

Account

First of all you need an account on the system. The Project leader is Matthias Hoeft and the Project ID is HTB00 (needed for registration). The following website contains all necessary links for allocating computing time in the Jülich Supercomputing Centre (JSC). Click on the link “User Accounts for projects on JUQUEEN, JUROPA,…” and follow the instructions.
http://www.fz-juelich.de/ias/jsc/EN/Expertise/Services/JSConline/ComputingTime/_node.html
german version:
http://www.fz-juelich.de/ias/jsc/DE/Leistungen/Dienstleistungen/JSCOnline/Rechenzeitvergabe/_node.html
Get in contact with Matthias so he can sign your account application and initiate the next steps.

Acquiring Data

Take a look at this site on how to get the data from the LTA
http://www.lofar.org/operations/doku.php?id=public:lta_howto
To download data from the web you need the full filename. You can look those up in the catalog
http://lofar.target.rug.nl/Lofar
The Juelich Http download server is here
https://lofar-download.fz-juelich.de/
For Sara
https://lofar-download.grid.sara.nl/

The recommended way to copy data is via srm copy. For doing this you need a Grid Certificate and to Register in the Virtual Organization (VO) as a Lofar User.

Register with the Virtual Organization

You can register with the Lofar VO here: https://voms.grid.sara.nl:8443/voms/lofar

Grid Certificate

To get direct srm copy access to the LTA storage you need a Grid Certificate.
Its best to ask around in your institute where to get and how to install such a certificate. General information about german grid certificates can be found here:
http://dgi-2.d-grid.de/zertifikate.php

SRM Copy from Juropa

There are two possible ways to create proxies on Juropa. With the ltools and grid-proxy-init commands or with voms-proxy-init.
The main difference is the authentication at the VO server. The voms-proxy-init can directly get a user role as input where as the grid-proxy-init can not. For grid-proxy-init the user roles are compared to a local file that is synchronized with the SARA server once per day. So you have to wait until the next day after registering with the Lofar VO in order to use your proxies.

grid-proxy-init

To use the grid-proxies simple follow these steps:
Store your private key in $HOME/.globus/userkey.pem
Execute:

chmod 600 $HOME/.globus/userkey.pem

Store your signed certificate in $HOME/.globus/usercert.pem
Then you have to generate a proxy. Simply source the script

. /lustre/jhome17/htb00/htb003/env_srm.sh

and then you can create your proxy

grid-proxy-init -bits 2048

Test data retrieval:

srmcp -server_mode=passive srm://srm.grid.sara.nl/pnfs/grid.sara.nl/data/lofar/ops/fifotest/file1M file:///file1M

When copying data with the –jobfile option, keep in mind that there is a 30min cpu limit on the login node. Meaning your srmcp should be shorter that 30min.

voms-proxy-init

To use voms-proxies handle your keys and certificate as above and source the script

. /lustre/jhome17/htb00/htb003/lofar_grid/init_java6.sh

Optional: Set proxy environment variable to custom location:

export X509_USER_PROXY=<proxy_location>

Generate a proxy:

voms-proxy-init -voms lofar:/lofar/user

Test data retrieval:

srmcp -server_mode=passive srm://srm.grid.sara.nl/pnfs/grid.sara.nl/data/lofar/ops/fifotest/file1M file:///file1M

LOFAR Software

The LOFAR Software Framework is installed in the home directory of user htb003. You load the environment with

. /lustre/jhome17/htb00/htb003/env_lofar.sh


This loads Release version 2.1 (in the future probably always the latest release). You can also load a 2.3 version (env_lofar_2.3.sh).
There is more software available:

  • Casapy 4.2 → env_casapy.sh
  • Karma → env_karma.sh
  • losoto → env_losoto.sh

In addition you might need a copy of the measurement data
/lustre/jhome17/htb00/htb003/dataCEP
Put it in your home directory and point to it in a file .casarc (just contains:“measures.directory: [yourhome]/dataCEP”)

If you require access to the GlobalSkyModel database, there is a copy of the database from the CEP Cluster (hopefully) running on the Juropa login node juropa02. Access the databse “gsm” on port 51000 with user “gsm” and pass “msss”

You can now run and test the executables on the login node from “lofar/release/bin” or run python scripts (your own or pipeline scripts in “local/lib/python2.7/site-packages/lofarpipe/recipes”).

To run your jobs on the compute nodes you first have to setup and submit a job via the batch system. A detailed description can be found on the Juropa homepage 'http://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUROPA/UserInfo/QuickIntroduction.html'

Here is a simple example of the procedure. Basically you use two scripts. One to configure the job and one to setup the environment for your program and run it.
Do not get confused by the use of comments '#'. The '#' in front of MSUB commands is necessary for the command to be recognized from the Moab batch system.
You submit the job with the command “msub [yourscript]”. Check your status with “showq -u 'username' ”. To see the whole machine with a gui try the “llview” program.

Contents of 'lofarmsub.sh':

#!/bin/bash -x
#MSUB -N Lofar-test
# just the name
#MSUB -l nodes=1:ppn=16
#MSUB -l walltime=00:30:00
#MSUB -e error.txt
# if keyword omitted : default is submitting directory
#MSUB -o output.txt
# if keyword omitted : default is submitting directory
#MSUB -M your@mail.de
#Mailadress
#MSUB -m eab
#send mail on end, abort, begin
./lofarCalibratorPipelinePy2.7.sh

The walltime is the time your job will be running on the machine. If it is to low and the job is not finished it will be killed. Is it to high your job might have to wait longer in queue but only the real computing time will be booked.
The maximum walltime is 24h.
It is a good practice to name the log files error and output with some job specific parameters and maybe the date.
You can choose to have mails send to you about the status of your job.

Contents of 'lofarCalibratorPipelinePy2.7.sh' (the environment variables are the same as in the env_lofar.sh. you could use just that script first before using your custom environments):

#/bin/sh!
#start of jobscript
export OMP_NUM_THREADS=16
#
#
export PYTHONPATH=/lustre/jhome17/htb00/htb003/local/lib/python2.7/site-packages:$PYTHONPATH
export PYTHONPATH=/lustre/jhome17/htb00/htb003/lofar/release/lib/python2.7/site-packages:$PYTHONPATH
#
export PATH=/lustre/jhome17/htb00/htb003/local/bin:$PATH
export PATH=/lustre/jhome17/htb00/htb003/lofar/release/bin:$PATH
export PATH=/lustre/jhome17/htb00/htb003/lofar/release/sbin:$PATH
#
export LD_LIBRARY_PATH=/lustre/jhome17/htb00/htb003/lofar/release/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/lustre/jhome17/htb00/htb003/lofar/release/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/lustre/jhome17/htb00/htb003/local/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/lustre/jhome17/htb00/htb003/local/lib64:$LD_LIBRARY_PATH
#
export LOFARROOT=/lustre/jhome17/htb00/htb003
#
module load gsl
module load GCC/4.6.3
#
/lustre/jhome17/htb00/htb003/lofar/release/bin/msss_target_pipeline.py /lustre/jhome17/htb00/htb003/pipeline_tests/Pipeline/target_pipeline/Observation64406 -c /lustre/jhome17/htb00/htb003/lofar/release/share/pipeline/pipeline.cfg --job target_test_omp16_descfile -d

Simply replace the pipeline call with the command you want to run in your job. Example of Alexanders bbs test: /lustre/jhome9/lofar/zdv596/LOFAR-Release-1_14/lofar/release/bin/calibrate-stand-alone -v -n -f L104244_SB200_uv.dppp.MS BBS.parset skymodel.parset
One important remark for your working directory. Use the Filesystem mounted under $WORK for your data and jobs.
From the Juropa home page:
$WORK
File system for large temporary files with high I/O bandwidth demands (scratch file system). No backup of files residing here. Files not used for more than 28 days will be automatically deleted!

Jobs in parallel

(this section has to be reedited because of a bug involving the PSI_WAIT parameter. for working multinoe compatible scripts as Bjoern for the moment. I will edit this section with the correct information after 16.6.14) You can start one job for every independent piece of data. You can use your old scripts and the pipeline scripts but every subtask will be processed in serial on one node. So typically you only allocate one node for your jobs.
To circumvent this, start the subprocesses in the python scripts in a different manner. Use the mpiexec command to start your subprocess. The Parastation MPI Demon will then allocate free resources to your subprocess when available. For this behavior the environment variable PSI_WAIT has to be set. This means you can allocate the partition you want to work on with more than one node. Run your script and whenever you use a subprocess call use mpiexec with number of processes equal to one (np=1).
You can have up to 16 processes per node (eight cpus + smt mode). How many of these processes are allocated to your np=1 option depends on the number of threads you want to have for openMP. So for OMP_NUM_THREADS=4 you will be able to run 4 subprocesses on one node. With OMP_NUM_THREADS=16 one subprocess per node and with OMP_NUM_THREADS=1 you will have 16.
As an example lets look at a part of a script from Andreas (run_NDPPPs.py):(in progress)
The snippet shows the the subprocess call with subprocess.Popen and how their return is handled. What has to be executed is in the list of tupels “cmds”. Where the first entry is the executable and the second a temporary parset file. Hence the os.remove after the return. The process is put into a list of processes and the function returns when this list is empty.

    while True:
        while cmds and len(processes) < max_task:
            task = cmds.pop()
            print time.asctime()," : ",list2cmdline(task)
            processes.append([Popen(task,env=myenv),task[1]])
            if waittime:
                break
        for p in processes:
            if done(p[0]):
                if success(p[0]):
                    os.remove(p[1])
                    processes.remove(p)
                else:
                    fail()
        if not processes and not cmds:
            break
        else:
            time.sleep(sleeptime)

To use multiple nodes on Juropa the command that is passed to popen has to be changed in the following way. The first argument is the executable followed by the arguments. The argument for “/bin/sh” has to be passed as one string and not as additional argument in the list. In this example the command we want to run consists of the executable and its argument written as tupels in “cmds”. The mpiexec is executed on one available slot “-np=1” which has the number of processes you specified with OMP_NUM_THREADS. The argument “-x” passes all environment variables to the process executed with mpiexec. Then we wait while there are elements left in the list of processes until all have returned. With the env variable PSI_WAIT=1 we can call more mpiexec than we have available slots. The mpi demon will handle the execution for us.

    for task in cmds:
        command = ["mpiexec","-x","-np=1","/bin/sh", "-c", "hostname && "+task[0]+" "+task[1]]
        print command
        processes.append([Popen(command,env=myenv),task[1]])

    while True:
        for p in processes:
            if done(p[0]):
                if success(p[0]):
                    os.remove(p[1])
                    processes.remove(p)
                else:
                    print "Error in: ",p[1]
                    os.remove(p[1])
                    processes.remove(p)

        if not processes:
            break

I hope these information are sufficient for some first tests and experiments.
Good luck and let me know of any problems and feel free to give some feedback.

The following libraries with given versions are installed in the home of user htb003 on Juropa.
/lustre/jhome17/htb00/htb003/ [local and lofar]

Library Version
bison 2.5
blitz 0.10
boost 1.44.0
cmake 2.8.5
casacore trunk
casarest 8741
cfitsio 3240
fftw 3.2.2
flex 2.5.35
hdf5 1.8.4
libpng 1.5.6
libpqxx 3.1
log4cplus 1.0.4
matplotlib 1.2.1
m4 1.4.16
monetdb python client 11.15.7
numpy 1.7.1
OpenBLAS 0.2.5
PostgreSQL 9.1.2
pyrap trunk
python 2.7.5
scons 1.3.0
setuptools 0.6c11
wcslib 4.4.4
unittest-xml-reporting 1.5.0
pyfits 3.1.2
pywcs 1.10.2
scipy 0.12.0
argparse 1.2.1
libiberty
LOFAR 1.16

Additional software for post processing requested by users:

Package Version
SIP 4.15.1
PyQt4 4.10.3
iPython 1.1.0
casapy 41.0.24668

LTA Installation on Juropa

The operating system is:

SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 1

With kernel version:

Linux 2.6.32.59-0.3-default x86_64 GNU/Linux

The current working installation is in:

/lustre/jhome17/htb00/htb003/lofar/release

Some things have to be Changed in order to compile and run everything on Juropa.

General Compile settings

There are unresolved issues with older versions. Tests with gcc4.3.4 and gcc4.4.6 gave the error:

File "[install_dir]/lofar/release/lib/python2.6/site-packages/lofar/parmdb/__init__.py", line 112, in getDefValues
return self._getDefValues (parmnamepattern)
TypeError: No to_python (by-value) converter found for C++ type: casa::Record

The compiler suite has to be changed to gcc4.6.3. You do this with the command:

module load GCC/4.6.3

You also have to load the gsl module

module load gsl

Set environment variables:

export CC=/usr/local/gcc/gcc-4.6.3/bin/gcc
export CXX=/usr/local/gcc/gcc-4.6.3/bin/g++

Using gcc4.6.3 gives the error

  File "[install_dir]/local/lib/python2.6/site-packages/pyrap.tables-trunk_r332-py2.6-linux-x86_64.egg/pyrap/tables/table.py", line 1023, in addcols
    self._addcols (tdesc, dminfo, addtoparent)
TypeError: No registered converter was able to produce a C++ rvalue of type int from this Python object of type numpy.int32

This can be corrected by changing the order of the “pyrap.tables” import in the node script “imager_prepare.py”

Change from:

from __future__ import with_statement
import sys
import shutil
import os
import subprocess
import copy
from lofarpipe.support.pipelinelogging import CatchLog4CPlus
from lofarpipe.support.pipelinelogging import log_time
from lofarpipe.support.utilities import patch_parset
from lofarpipe.support.utilities import catch_segfaults
from lofarpipe.support.lofarnode import  LOFARnodeTCP
from lofarpipe.support.utilities import create_directory
from lofarpipe.support.data_map import DataMap
from lofarpipe.support.subprocessgroup import SubProcessGroup

import pyrap.tables as pt

to

from __future__ import with_statement
import sys
import shutil
import os
import subprocess
import copy
import pyrap.tables as pt
from lofarpipe.support.pipelinelogging import CatchLog4CPlus
from lofarpipe.support.pipelinelogging import log_time
from lofarpipe.support.utilities import patch_parset
from lofarpipe.support.utilities import catch_segfaults
from lofarpipe.support.lofarnode import  LOFARnodeTCP
from lofarpipe.support.utilities import create_directory
from lofarpipe.support.data_map import DataMap
from lofarpipe.support.subprocessgroup import SubProcessGroup

Libiberty

The change of the compiler suite brings additional problems. The system paths change to custom locations which prevents the finding of the correct version of the library “libiberty.a”. The one found is missing the compiler flag -fPIC so it can be linked dynamically. You have to compile it yourself (or copy the correct one from the old path ←- needs checking).

PYRAP

Pyrap will not compile because it will not find the glibc libraries as they are not in the standard location anymore. To correct this you have to edit the pyrap SConsript in pyrap/libpyrap/trunk from

env = Environment(ENV = { 'PATH' : os.environ[ 'PATH' ],
                          'HOME' : os.environ[ 'HOME' ] 
                          },

to

env = Environment(ENV = os.environ,

Note: you have to use the trunk version and not the latest release because otherwise pyrap tables wont work. Also add the the compile option –enable-rpath. Use “batchbuild-trunk.py” for installation instead of “batchbuild.py”.

FFTW

For the fftw library you have to add the option “–enable-threads” for the compiler.

SCIPY

For scipy add the environment variable UMFPACK=“None”.

Blitz

Version 0.9 did not compile. Version 0.10 works fine.

CMake

Tried using the system installed CMake which is also version 2.8.5. There are Problems with some “find” scripts (LAPACK,BLAS). I remember casacore and Lofar would not install as they should, forgot the details though as the self installed version does what it should. Maybe one should look into it in the future because what CMake to use should not matter.

Casacore

Need to use the trunk version instead of release 1.5 for functionality. Using the buildscript options, Casacore is missing support for hdf5 which is needed for the final steps of the imaging pipeline. Added hdf5 support as well as OpenMP and Threads by hand in the ccmake interface. ToDo: add options to buildscript

LOFAR

The system installed libpng was hiding the selfcompiled version which lead to problems during program execution. The proper path to libpng library and include dir have to be set in cmake. Same goes for the selfcompiled libiberty. ToDo: add options in cmake.build file (at least for libpng since its part of standard installation).
The variants file GNU.cmake has to be edited to set the path to the new compiler version

# GNU compiler suite
set(GNU_COMPILERS GNU_C GNU_CXX GNU_Fortran GNU_ASM)
set(GNU_C         /usr/local/gcc/gcc-4.6.3/bin/gcc)      # GNU C compiler
set(GNU_CXX       /usr/local/gcc/gcc-4.6.3/bin/g++)      # GNU C++ compiler
set(GNU_Fortran   /usr/local/gcc/gcc-4.6.3/bin/gfortran) # GNU Fortran compiler
set(GNU_ASM       /usr/local/gcc/gcc-4.6.3/bin/gcc)      # GNU assembler

SSH support

Logging into compute nodes via ssh is not permitted on the system. Subprocesses have to be started on the one rented compute for now via shell or mpiexec command. Distribution to multiple nodes is in the works.

lofarpipe/support/remotecommand.py has to be edited to circumvent ssh for locahost job spawning (svn diff, see extra section)

File copy

Since the Juropa cluster uses a shared filesystem every data should be (read: HAS to be) present at job start to not waste computing time. The login nodes are supposed to be used for job preparation and analysis afterwards. So we do not need to copy data to the working directory (quota is limited!). The change for that is in lofarpipe/recipes/nodes/imager_prepare.py (svn diff, see extra section).

Imaging Pipeline

Because the datacopy to the working directory will not be done automatically the data has to present in your working directory set in pipeline.cfg plus subfolder jobname. Somthing like working_dir/imaging_pipeline/subbands
In lofarpipe/recipes/nodes/imager_prepare.py in the call to rfi_console the “indirect_read” option has to be removed because of insufficient write access on the target machine (some folder you are not supposed to use as normal user)

GSM Database

A local version of the GSM Database has to be used. At the moment it is running on Juropa02 but has to be restarted after downtimes or after the demon used up its 30min wallclock time. The database is in the home folder of user zdv596 and can be started with

cd /lustre/jhome9/lofar/zdv596
monetdbd start gsm
monetdb start gsm

monetdb is installed in /lustre/jhome9/lofar/zdv596/LOFAR-Release-1_14/local/bin
How to install a local GSM Database take a look at this http://www.lofar.org/wiki/doku.php?id=lta:software_stack_installation#gsm_database_installation

gsmutils.py

The changes made during release 1.14 (after initial release) break the functionality of the database access on Juropa. You can revert back to the initial release of 1.14 or use the fix mentioned below. The error is as follows:

ERROR:node.jj29l09.imager_create_dbs:expected_fluxes_in_fov raise exception: GDK reported error.
!BATfetchjoin(tmpr_2277,tmp_4347) does not hit always (|bn|=0 != 46216=|l|) => can't use fetchjoin.

ERROR:node.jj29l09.imager_create_dbs:failed creating skymodel
Traceback (most recent call last):
  File "/lustre/jhome17/htb00/htb003/LOFAR-R14-P275/lofar/release/lib/python2.7/site-packages/lofarpipe/recipes/nodes/imager_create_dbs.py", line 470, in <module>
    _jobid, _jobhost, _jobport).run_with_stored_arguments())
  File "/lustre/jhome17/htb00/htb003/LOFAR-R14-P275/lofar/release/lib/python2.7/site-packages/lofarpipe/support/lofarnode.py", line 85, in run_with_stored_arguments
    returnvalue = self.run_with_logging(*self.arguments)
  File "/lustre/jhome17/htb00/htb003/LOFAR-R14-P275/lofar/release/lib/python2.7/site-packages/lofarpipe/support/lofarnode.py", line 59, in run_with_logging
    return self.run(*args)
  File "/lustre/jhome17/htb00/htb003/LOFAR-R14-P275/lofar/release/lib/python2.7/site-packages/lofarpipe/recipes/nodes/imager_create_dbs.py", line 71, in run
    monet_db_password, assoc_theta)
TypeError: 'int' object is not iterable

Bart Scheers provided a temporary fix for this issue. You need to change the configuration of your database.

Stop gsm database set nthreads property to 1:

monetdb stop gsm
monetdb set nthreads=1 gsm
monetdb start gsm

imager_prepare.py

Prevent datacopy when working on local host only. No “indirect_read” supported on Juropa.
SVN diff for lofarpipe/recipes/nodes/imager_prepare.py

Index: CEP/Pipeline/recipes/sip/nodes/imager_prepare.py
===================================================================
--- CEP/Pipeline/recipes/sip/nodes/imager_prepare.py    (revision 25127)
+++ CEP/Pipeline/recipes/sip/nodes/imager_prepare.py    (working copy)
@@ -10,6 +10,7 @@
 import os
 import subprocess
 import copy
+import pyrap.tables as pt
 from lofarpipe.support.pipelinelogging import CatchLog4CPlus
 from lofarpipe.support.pipelinelogging import log_time
 from lofarpipe.support.utilities import patch_parset
@@ -19,7 +20,7 @@
 from lofarpipe.support.data_map import DataMap
 from lofarpipe.support.subprocessgroup import SubProcessGroup
 
-import pyrap.tables as pt
+#import pyrap.tables as pt
 
 # Some constant settings for the recipe
 _time_slice_dir_name = "time_slices"
@@ -140,37 +141,44 @@
             if input_item.skip == True:
                 exit_status = 1 # 
 
-            # construct copy command
-            command = ["rsync", "-r", "{0}:{1}".format(
-                            input_item.host, input_item.file),
-                               "{0}".format(processed_ms_dir)]
+
+           self.logger.debug(input_item.host)
+           self.logger.debug(self.host)
+            # skip the copy if machine is the same (execution on localhost).
+           # make sure data is in the correct directory. for now: working_dir/trunk_imager_regression/subbands
+           if input_item.host != "localhost":
+            
+                       # construct copy command
+               command = ["rsync", "-r", "{0}:{1}".format(
+                           input_item.host, input_item.file),
+                           "{0}".format(processed_ms_dir)]
 
-            self.logger.debug("executing: " + " ".join(command))
+               self.logger.debug("executing: " + " ".join(command))
 
-            # Spawn a subprocess and connect the pipes
-            # The copy step is performed 720 at once in that case which might
-            # saturate the cluster.
-            copy_process = subprocess.Popen(
-                        command,
-                        stdin=subprocess.PIPE,
-                        stdout=subprocess.PIPE,
-                        stderr=subprocess.PIPE)
+               # Spawn a subprocess and connect the pipes
+               # The copy step is performed 720 at once in that case which might
+               # saturate the cluster.
+               copy_process = subprocess.Popen(
+                               command,
+                               stdin=subprocess.PIPE,
+                               stdout=subprocess.PIPE,
+                               stderr=subprocess.PIPE)
 
-            # Wait for finish of copy inside the loop: enforce single tread
-            # copy
-            (stdoutdata, stderrdata) = copy_process.communicate()
+               # Wait for finish of copy inside the loop: enforce single tread
+               # copy
+               (stdoutdata, stderrdata) = copy_process.communicate()
 
-            exit_status = copy_process.returncode
+               exit_status = copy_process.returncode
 
             #if copy failed log the missing file and update the skip fields 
-            if  exit_status != 0:
-                input_item.skip = True
-                copied_item.skip = True
-                self.logger.warning(
+                if  exit_status != 0:
+                       input_item.skip = True
+                       copied_item.skip = True
+                       self.logger.warning(
                             "Failed loading file: {0}".format(input_item.file))
-                self.logger.warning(stderrdata)
+                       self.logger.warning(stderrdata)
 
-            self.logger.debug(stdoutdata)
+                       self.logger.debug(stdoutdata)
 
         return copied_ms_map
 
@@ -298,7 +306,8 @@
 
                 # construct copy command
                 self.logger.info(time_slice)
-                command = [rficonsole_executable, "-indirect-read",
+                command = [rficonsole_executable,
+                           ## "-indirect-read",
                             time_slice]
                 self.logger.info("executing rficonsole command: {0}".format(
                             " ".join(command)))

remotecommand.py

Extra Path variable for remote systems where python is not installed in the same place as on the master node.
Prevent ssh commands entirely as they are not supported on Juropa. Just a switch for localhost. SVN diff:

Index: CEP/Pipeline/framework/lofarpipe/support/remotecommand.py
===================================================================
--- CEP/Pipeline/framework/lofarpipe/support/remotecommand.py   (revision 25127)
+++ CEP/Pipeline/framework/lofarpipe/support/remotecommand.py   (working copy)
@@ -111,13 +111,29 @@
     process.kill = lambda : os.kill(process.pid, signal.SIGTERM)
     return process
 
+def run_via_local(logger, command, arguments):
+    commandstring = ["/bin/sh","-c"]
+    for arg in arguments:
+        command = command + " " + str(arg)
+    commandstring.append(command)
+    process = spawn_process(commandstring, logger)
+    process.kill = lambda : os.kill(process.pid, signal.SIGKILL)
+    return process
+
 def run_via_ssh(logger, host, command, environment, arguments):
     """
     Dispatch a remote command via SSH.
 
     We return a Popen object pointing at the SSH session, to which we add a
     kill method for shutting down the connection if required.
+
+    hack/
+    if host is localhost run without ssh
+    /hack
     """
+    if host == "localhost":
+        logger.debug("Running command locally")
+        return run_via_local(logger, command, arguments)
     logger.debug("Dispatching command to %s with ssh" % host)
     ssh_cmd = ["ssh", "-n", "-tt", "-x", host, "--", "/bin/sh", "-c"]
 
@@ -214,6 +230,7 @@
                 self.host,
                 self.command,
                 {
+                   "PATH": os.environ.get('PATH'),
                     "PYTHONPATH": os.environ.get('PYTHONPATH'),
                     "LD_LIBRARY_PATH": os.environ.get('LD_LIBRARY_PATH')
                 },

copier.py

The copy process of the Intrument files used in the target pipeline has to be changed because rsync is not supported between nodes. Change to a simple copy command.
recipes/nodes/copier.py

53,56c53
<         if source_node=="localhost":
<             command = ["cp", "-r","{0}".format(source_path),"{0}".format(target_path)]
<         else:
<             command = ["rsync", "-r", 
---
>         command = ["rsync", "-r",

parset.py

Changed the “output_dir” in “patch_parset” to a directory visible from all nodes.
Should maybe be changed to the working directory?!

  • Last modified: 2017-03-08 15:27
  • (external edit)