pyunicoremanager 0.0.5

Python wrapper of PyUnicore to deploy jobs on UNICORE systems

# PyUnicoreManager Library

It is a Python wrapper of PyUnicore library (linked to UNICORE) that tries to minimize the lines of code and makes it flexible enough to be a interconnection piece of workflows. It is designed to be used from Notebooks of EBRAINS or a personal laptop with a LDAP authentication. In both cases, the user can launch jobs on the HPC systems, upload and download files and make complex workflow.

Development of this library was funded in part by the Human Brain Project

For more information about the Human Brain Project, see https://www.humanbrainproject.eu/

See LICENSE file for licensing information

# First steps

Install it from Pypi:

pip install pyunicore==0.14.0 pip install -U pyunicoremanager

How to use it:

from pyunicoremanager.core import *

# Important configuration for the framework For this example we use a certain Server with a particular partition and project, allocating 1 node for 10 minutes for a single job.

setup={} setup[“server”] = myServer setup[“server_endpoint”] = myPartition setup[“server_project”] = myProject folder setup[“JobArguments”] = {“Project”: myProject name, ‘Resources’: {“Queue”: myQueue, “Nodes” : “1”,”Runtime” : “10m”}}

# Authentication from EBRAINS notebook on the HPC system

authentication = Authentication(token=clb_oauth.get_token(), access=Method.ACCESS_COLLAB, server=setup[‘server’])

# Authentication from personal PC on the HPC system

myToken = Utils.generateToken(myLDAPuser, myLDAPpassword) authentication = Authentication(token=myToken, access=Method.ACCESS_LDAP, server=setup[‘server’])

# Environemnt for the framework

env = Environment_UNICORE(auth=authentication, env_from_user=setup)

# Instanciate the framework

It would check if the jobs storage list is full, in that case would clean it up.

pym = PyUnicoreManager(environment=env, clean_job_storages=True, verbose=False)

# Launching a simple job

All the job steps would be a list of command lines. The “job” object contains important information where the user can access later on. Each job has its Storage mapping and an unique identification number, in other words, jobs result can be accessible at any time. The “result” is a dictionary with the keys “stderr” and “stdout” and the whole output accumulated during the execution of the job.

job_steps = [“ls -la”,”pwd”] job, result= pym.one_run(steps=job_steps, parameters=setup[“JobArguments”], wait_process=True) print(result)