API Docs

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.

Intended audience





stable | nightly | changelog | github

cuDF is a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating data.


stable | nightly | changelog | github

cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.


stable | nightly | changelog | github

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.


stable | nightly | changelog | github

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.

RAPIDS Libraries


stable | nightly | changelog | github

libcudf is a C/C++ CUDA library for implementing standard dataframe operations.


stable | nightly | changelog | github

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.