cuxfilter with multi-GPU using dask_cudf ======================================== `Dask-cuDF `_ extends Dask where necessary to allow its DataFrame partitions to be processed by cuDF GPU DataFrames as opposed to Pandas DataFrames. For instance, when you call dask_cudf.read_csv(…), your cluster’s GPUs do the work of parsing the CSV file(s) with underlying cudf.read_csv(). When to use cuDF and Dask-cuDF ------------------------------ If your workflow is fast enough on a single GPU or your data comfortably fits in memory on a single GPU, you would want to use cuDF. If you want to distribute your workflow across multiple GPUs, have more data than you can fit in memory on a single GPU, or want to analyze data spread across many files at once, you would want to use Dask-cuDF. A very useful guide to using Dask-cudf can be found `here `_ Cuxfilter with Dask-cudf ------------------------ Using cuxfilter with Dask-cudf is a very seamless experience, and passing in a `dask_cudf.DataFrame` object, instead of `cudf.DataFrame` object should just work, without any other modifications. The `dask_cudf.DataFrame` should however be initialized with it's partitions set, before passing it the the `cuxfilter.DataFrame.from_dataframe` function. For more information and examples, please visit the cuxfilter repository with `dask_cudf notebooks `_ .. list-table:: Currently Supported Charts :widths: 50 50 :header-rows: 1 * - Library - Chart type * - bokeh - bar, line * - datashader - scatter, scatter_geo, line, stacked_lines, heatmap, graph(note: edge rendering support is limited for now) * - panel_widgets - range_slider, date_range_slider, float_slider, int_slider, drop_down, multi_select, card, number * - custom - view_dataframe * - deckgl - choropleth(3d and 2d)