“What this architecture helps to do is to say, you tell me where your data is, and I’ll give you a computer right there.”įor data scientists, Jupyter has emerged as a de facto standard, says Lorena Barba, a mechanical and aeronautical engineer at George Washington University in Washington DC.
“In many cases, it’s much easier to move the computer to the data than the data to the computer,” says Pérez of Jupyter’s cloud-based capabilities. In part, says Pérez, that growth is due to improvements in the web software that drives applications such as Gmail and Google Docs the maturation of scientific Python and data science and, especially, the ease with which notebooks facilitate access to remote data that might otherwise be impractical to download - such as from the LSST. One analysis of the code-sharing site GitHub counted more than 2.5 million public Jupyter notebooks in September 2018, up from 200,000 or so in 2015.
This rapid uptake has been aided by an enthusiastic community of user–developers and a redesigned architecture that allows the notebook to speak dozens of programming languages - a fact reflected in its name, which was inspired, according to co-founder Fernando Pérez, by the programming languages Julia (Ju), Python (Py) and R. Computational notebooks have been around for decades, but Jupyter in particular has exploded in popularity over the past couple of years. Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document. And to crunch those data, astronomers will use a familiar and increasingly popular tool: the Jupyter notebook. When it comes online in 2022, the telescope will generate terabytes of data each night as it surveys the southern skies automatically. Perched atop the Cerro Pachón ridge in the Chilean Andes is a building site that will eventually become the Large Synoptic Survey Telescope (LSST).
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