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DTSTAMP:20260522T150121Z
LOCATION:D220
DTSTART;TZID=America/Chicago:20181112T110000
DTEND;TZID=America/Chicago:20181112T113000
UID:submissions.supercomputing.org_SC18_sess172_ws_phpsc103@linklings.com
SUMMARY:Managing Python in HPC Environments
DESCRIPTION:Daniel Gall (Engility Corporation) and Frank Indiviglio (Natio
 nal Oceanic and Atmospheric Administration)\n\nPython has seen a rapid ado
 ption in the weather and climate modeling science communities.  This swift
  rise has taken HPC system administrators by surprise, leading to inadequa
 te support.  These trends, like those in other sciences, led to the develo
 pment and widespread adoption of user managed binary distributions.  An ex
 ample being Anaconda in 2012, which comes with security risks.  We present
  a system for mirroring Anaconda Python that embeds PyRats, a dependency a
 nalyzer and logger descended from Blais’ Snakefood into the Anaconda insta
 llers.  We show that Anaconda performance, reliability, security, and avai
 lability can be improved dramatically and enable timely integration into a
 utomated test environments.  The dependency logging yields insights into w
 hich packages users rely on most.  It can help prioritize optimization eff
 orts such as building packages for the CPU families used in a given HPC en
 vironment.  We also discuss related work, including a complimentary automa
 ted Python provisioning effort by Oak Ridge National Laboratory that they 
 call PythonEnv-noaa.\n\nTag: Parallel Application Frameworks, Reproducibil
 ity, Scientific Computing\n\nRegistration Category: Workshop Reg Pass\n\n
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