The RAPIDS cuDF library is a GPU DataFrame manipulation library based on Apache Arrow that accelerates loading, filtering, and manipulation of data for model training data preparation. The RAPIDS GPU DataFrame provides a pandas-like API that will be familiar to data scientists, so they can now build GPU-accelerated workflows more easily.

Please see the Demo Docker Repository, choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize cuDF.

You can get a minimal conda installation with Miniconda or get the full installation with Anaconda.

You can install and update cuDF using the conda command:

conda install -c numba -c conda-forge -c rapidsai -c defaults cudf=0.2.0

Note: This conda installation only applies to Linux and Python versions 3.5/3.6.

You can create and activate a development environment using the conda command:

conda env create --name cudf --file conda_environments/testing_py35.yml
source activate cudf

Support is coming soon, please use conda for the time being.

The following instructions are tested on Linux Ubuntu 16.04 & 18.04, to enable from source builds and development. Other operatings systems may be compatible, but are not currently supported.

Compiler requirements:

• g++ 5.4
• cmake 3.12

CUDA/GPU requirements:

• CUDA 9.2+
• NVIDIA driver 396.44+
• Pascal architecture or better

You can obtain CUDA from https://developer.nvidia.com/cuda-downloads

Since cmake will download and build Apache Arrow (version 0.7.1 or 0.8+) you may need to install Boost C++ (version 1.58+) before running cmake:

# Install Boost C++ for Ubuntu 16.04/18.04
$ sudo apt-get install libboost-all-dev

or

# Install Boost C++ for Conda
$ conda install -c conda-forge boost

To install cuDF from source, ensure the dependencies are met and follow the steps below:

1. Clone the repository

git clone --recurse-submodules https://github.com/rapidsai/cudf.git
cd cudf

2. Create the conda development environment cudf as detailed above
3. Build and install libgdf

source activate cudf
mkdir -p libgdf/build
cd libgdf/build
cmake .. -DHASH_JOIN=ON -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX
make -j install
make copy_python
python setup.py install

4. Build and install cudf from the root of the repository

cd ../..
python setup.py install

A Dockerfile is provided with a preconfigured conda environment for building and installing cuDF from source based off of the master branch.

• Install nvidia-docker2 for Docker + GPU support
• Verify NVIDIA driver is 396.44 or higher
• Ensure CUDA 9.2+ is installed

From cudf project root run the following, to build with defaults:

docker build -t cudf .

After the container is built run the container:

docker run --runtime=nvidia -it cudf bash

Activate the conda environment cudf to use the newly built cuDF and libgdf libraries:

root@3f689ba9c842:/# source activate cudf
(cudf) root@3f689ba9c842:/# python -c "import cudf"
(cudf) root@3f689ba9c842:/#

Several build arguments are available to customize the build process of the container. These are spcified by using the Docker build-arg flag. Below is a list of the available arguments and their purpose:

This project uses py.test

In the source root directory and with the development conda environment activated, run:

py.test --cache-clear --ignore=libgdf

The libgdf tests require a GPU and CUDA. CUDA can be installed locally or through the conda packages of numba & cudatoolkit. For more details on the requirements needed to run these tests see the libgdf README.

libgdf has two testing frameworks py.test and GoogleTest:

# Run py.test command inside the /libgdf folder
py.test

# Run GoogleTest command inside the /libgdf/build folder after cmake
make -j test

The RAPIDS suite of open source software libraries aim to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

The GPU version of Apache Arrow is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.