NOTE: For the latest stable README.md ensure you are on the
- cuDF Reference Documentation: Python API reference, tutorials, and topic guides.
- libcudf Reference Documentation: C/C++ CUDA library API reference.
- Getting Started: Instructions for installing cuDF.
- RAPIDS Community: Get help, contribute, and collaborate.
- GitHub repository: Download the cuDF source code.
- Issue tracker: Report issues or request features.
Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
For example, the following snippet downloads a CSV, then uses the GPU to parse it into rows and columns and run calculations:
import cudf, io, requests from io import StringIO url = "https://github.com/plotly/datasets/raw/master/tips.csv" content = requests.get(url).content.decode('utf-8') tips_df = cudf.read_csv(StringIO(content)) tips_df['tip_percentage'] = tips_df['tip'] / tips_df['total_bill'] * 100 # display average tip by dining party size print(tips_df.groupby('size').tip_percentage.mean())
size 1 21.729201548727808 2 16.571919173482897 3 15.215685473711837 4 14.594900639351332 5 14.149548965142023 6 15.622920072028379 Name: tip_percentage, dtype: float64
For additional examples, browse our complete API documentation, or check out our more detailed notebooks.
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.
- CUDA 10.1+
- NVIDIA driver 418.39+
- Pascal architecture or better (Compute Capability >=6.0)
cuDF can be installed with conda (miniconda, or the full Anaconda distribution) from the
cudf version == 0.18 :
# for CUDA 10.1 conda install -c rapidsai -c nvidia -c numba -c conda-forge \ cudf=0.18 python=3.7 cudatoolkit=10.1 # or, for CUDA 10.2 conda install -c rapidsai -c nvidia -c numba -c conda-forge \ cudf=0.18 python=3.7 cudatoolkit=10.2
For the nightly version of
# for CUDA 10.1 conda install -c rapidsai-nightly -c nvidia -c numba -c conda-forge \ cudf python=3.7 cudatoolkit=10.1 # or, for CUDA 10.2 conda install -c rapidsai-nightly -c nvidia -c numba -c conda-forge \ cudf python=3.7 cudatoolkit=10.2
Note: cuDF is supported only on Linux, and with Python versions 3.7 and later.
See the Get RAPIDS version picker for more OS and version info.
Build/Install from Source
See build instructions.
Please see our guide for contributing to cuDF.
Find out more details on the RAPIDS site
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.
Apache Arrow on GPU
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.