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=24.02 python=3.10 cuda-version=12.0

# for CUDA 11.8
conda install -c rapidsai -c conda-forge -c nvidia \
    cuxfilter=24.02 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='./')"