<reponame>amaajemyfren/data<filename>pycon-ca-2016/videos/managing-large-ensembles-and-batch-execution-with-python.json<gh_stars>10-100
{
  "description": "In many scientific applications it is necessary to run a large number of\nsimilar experiments (i.e. ensembles). Using examples from my own\nresearch, I will show how an Ensemble container class can be constructed\nin Python to simplify many of the tedious aspects of performing large\nsets of numerical experiments or simulations and to easily parallelize\nbatch execution.\n\n<NAME> Bio\n------------------\n\nAndre is an early-career researcher in climate modeling and hydrology;\nthat means he runs climate models and hydrological models on high\nperformance computing facilities and analyses their output. He uses\nprimarily uses the scientific software stack in Python for data analysis\nand visualization, but also uses Python to manage experiments with large\nvolumes of data (or \"data plumbing\").\n\nAndre is also interested in machine learning and the use of data science\ntechniques in and outside of climate science, and he is somewhat\nconcerned about the state of software development in science.\n\nOpen source software, open science, the environment and sustainable\nglobal development are the causes he cares most about.\n",
  "duration": 597,
  "recorded": "2016-11-12",
  "speakers": [
    "<NAME>"
  ],
  "thumbnail_url": "https://i.ytimg.com/vi/JzAjjo_E6iY/maxresdefault.jpg",
  "title": "Managing large ensembles and batch execution with Python",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=JzAjjo_E6iY"
    }
  ]
}
