Installation

NOTE: cuxfilter is in ongoing development and the installation instructions will be updated in the near future.

Install cuxfilter Nightly(0.12)

# for CUDA 9.2
conda install -c rapidsai-nightly cuxfilter=0.12 cudatoolkit=9.2

# or, for CUDA 10.0
conda install -c rapidsai-nightly cuxfilter=0.12 cudatoolkit=10.0

To run the bokeh server in a jupyter lab

  1. Install jupyterlab dependencies

conda install -c conda-forge jupyterlab
jupyter labextension install @pyviz/jupyterlab_pyviz
jupyter labextension install jupyterlab_bokeh

2.running the server

#enter ip address without http://
#current port is the port at which jupyterlab is running
d.app(notebook_url='ip.addr:current_port')
# OR for a separate web app
d.show(notebook_url='ip.addr:current_port')

Troubleshooting

  1. If the await d.preview() throws a libxcomposite.so.1 not found error, execute the following commands:

apt-get update
apt-get install libxcomposite1 libxcursor1 libxdamage1 libxfixes3 libxi6 libxrandr2 libxtst6 libcups2 libxss1 libasound2 libpangocairo-1.0-0 libpango-1.0-0 libatk1.0-0 libgtk-3-0 libgdk-pixbuf2.0-0

Download datasets

  1. Auto download datasets

The notebooks inside python/notebooks already have a check function which verifies whether the example dataset is downloaded, and downloads it if it’s not.

  1. Download manually

While in the directory you want the datasets to be saved, execute the following

#go the the environment where cuxfilter is installed. Skip if in a docker container
source activate test_env

#download and extract the datasets
curl https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2015-01.csv --create-dirs -o ./nyc_taxi.csv
curl https://s3.us-east-2.amazonaws.com/rapidsai-data/viz-data/146M_predictions_v2.arrow.gz --create-dirs -o ./146M_predictions_v2.arrow.gz
curl https://s3.us-east-2.amazonaws.com/rapidsai-data/viz-data/auto_accidents.arrow.gz --create-dirs -o ./auto_accidents.arrow.gz

python -c "from cuxfilter.sampledata import datasets_check; datasets_check(base_dir='./')"