![]() ![]() RAPIDS with the GPU-powered workflow alleviates all these hurdles. Typical workday for a developer using a GPU- vs. ![]() This results in lost productivity and, likely, a coffee addiction if we take a look at the chart below.įigure 1. Anyone who has tried to read and process a 2GB dataset on a CPU knows what we’re talking about.Īdditionally, since we’re human and we make mistakes, rerunning a pipeline might quickly turn into a full day exercise. One of the reasons is because the questions we ask the dataset take too long to answer. The fundamental data science task, and the one that all data scientists complain about, is cleaning, featurizing and getting familiar with the dataset. In fact, cuDF can store data in all the formats it can read.Īll of these capabilities make it possible to get up and running quickly no matter what your task is or where your data lives.Įxtracting, transforming, and summarizing data ![]() In addition, cuDF supports saving the data stored in a DataFrame into multiple formats and file systems.
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