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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 additional examples, browse our complete API documentation, or check out our more detailed notebooks. cuDF can be installed with conda (miniconda, or the full Anaconda distribution) from the rapidsai channel. cuDF is supported only on Linux, and with Python versions 3.7 and later. The RAPIDS suite of open-source software libraries aims to enable the 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.
Features
- cuDF is supported only on Linux, and with Python versions 3.7 and later
- The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs
- Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask
- Accelerate your Python data science toolchain with minimal code changes and no new tools to learn
- cuDF provides a pandas-like API that will be familiar to data engineers & data scientists
- Built based on the Apache Arrow columnar memory format
Project Samples
