Installation

Conda

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

# for CUDA 12.0
conda install -c rapidsai -c conda-forge -c nvidia \
    cuxfilter=23.12 python=3.10 cuda-version=12.0

# for CUDA 11.8
conda install -c rapidsai -c conda-forge -c nvidia \
    cuxfilter=23.12 python=3.10 cuda-version=11.8

PyPI

Install cuxfilter from PyPI using pip:

# for CUDA 12.0
pip install cuxfilter-cu12 -extra-index-url=https://pypi.nvidia.com

# for CUDA 11.8
pip install cuxfilter-cu11 -extra-index-url=https://pypi.nvidia.com

Docker container

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

cuxfilter docker example installation for cuda 12.0, ubuntu 20.04:

# ex. for CUDA 11.8
docker pull rapidsai/rapidsai:cuda12.0-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 on our GitHub.

Troubleshooting

If the guide below doesn’t help you resolve your issue, please file an issue on our GitHub.

Install jupyterlab dependencies

If you have issues with charts not rendering in the jupyterlab notebook, please make sure you have the following installed in your environment:

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

Download datasets

1. Automatically download datasets

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

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='./')"