Installation¶

Conda¶

For the most customized way of installing RAPIDS and cuxfilter, visit the selector on the RAPIDS Get Started Page.

cuxfilter conda example installation:

conda install -c rapidsai -c conda-forge -c nvidia \
    cuxfilter=23.02 python=3.10 cudatoolkit=11.8

Docker container¶

For the most customized way of installing RAPIDS and cuxfilter, visit the selector on the RAPIDS Get Started Page.

cuxfilter docker example installation:

# ex. for CUDA 11.8
docker pull rapidsai/rapidsai:cuda11.8-runtime-ubuntu20.04
docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
    rapidsai/rapidsai:cuda11.8-runtime-ubuntu20.04

# open http://localhost:8888

Build/Install from Source¶

See build instructions.

Troubleshooting¶

1. Install jupyterlab dependencies¶

conda install -c conda-forge jupyterlab
jupyter labextension install @pyviz/jupyterlab_pyviz
jupyter labextension install @bokeh/jupyter_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')

3. libxcomposite.so.1 not found error¶

If using 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.

2. 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
conda 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://data.rapids.ai/viz-data/146M_predictions_v2.arrow.gz --create-dirs -o ./146M_predictions_v2.arrow.gz
curl https://data.rapids.ai/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='./')"