Access our current “stable” API docs for all RAPIDS libraries below. In addition, explore our “nightly” docs containing the latest features and updates for the next release.
cuDF is a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating data.
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.
cuGraph is a collection of graph analytics that process data found in a GPU Dataframe - see cuDF. cuGraph aims at provides a NetworkX-like API that will be familiar to data scientists, so they can now build GPU-accelerated workflows more easily.
nvStrings (the Python bindings for cuStrings), 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.
libcudf is a C/C++ CUDA library for implementing standard dataframe operations.
RAPIDS Memory Manager (RMM) is a central place for all device memory allocations in cuDF (C++ and Python) and other RAPIDS libraries. In addition, it is a replacement allocator for CUDA Device Memory (and CUDA Managed Memory) and a pool allocator to make CUDA device memory allocation / deallocation faster and asynchronous.