Access our current docs for all RAPIDS projects below. Docs are available in both “stable” and “nightly” versions. The description of each is below to help select the docs that fit your needs.
- Current release docs; considered to be stable.
- Work-in-progress release docs; considered to be unstable and released nightly.
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.
libnvstrings is the C/C++ library for cuStrings enabling string manipulation on the GPU.
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.