pylibcudf-cu12
pylibcudf - Python bindings for libcudf
Decision gist · record as of 2026-08-14
Yes, if you have an NVIDIA GPU with CUDA 12 and need GPU-accelerated tabular data processing. Install it as a dependency of cudf or higher-level RAPIDS libraries rather than directly unless you need low-level libcudf access. Active maintenance, no known vulnerabilities, and permissive Apache 2.0 license make it production-ready. Medium install friction is expected given the specialized CUDA runtime requirements.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA 12 runtime environment and compatible NVIDIA GPU; CUDA version must match the cu12 suffix in the package name.
- Medium install friction due to CUDA 12 runtime dependencies (libcudf-cu12, cuda-bindings, rmm-cu12, nvtx).
- Requires matching your CUDA version to the cu12 suffix.
License · maintenance · safety
Apache-2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most production environments.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 9,730 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 410,959 downloads/mo, #6,861 on PyPI
Alternatives
Verify before relying
pip install pylibcudf-cu12
import pylibcudf
# Access libcudf functionality through Cython bindings
# Typically used indirectly via cudf or other RAPIDS libraries- Whether pylibcudf-cu12 is typically used directly or primarily as a dependency of higher-level RAPIDS libraries (cudf, dask-cudf).
- Performance characteristics and typical speedup factors compared to CPU-based alternatives for specific workloads.
What it is and what it does
pylibcudf-cu12 is a low-level Python binding layer for libcudf, the core CUDA C++ library in RAPIDS that implements GPU-accelerated tabular data operations. It exposes libcudf's Arrow-compliant data structures and algorithms to Python via Cython, serving as the foundation for higher-level RAPIDS libraries like cudf and dask-cudf.
This package is designed for developers building GPU-accelerated data processing applications. It requires a CUDA 12 environment and compatible NVIDIA GPU hardware. The package has four runtime dependencies: libcudf-cu12 (the C++ library itself), cuda-bindings, rmm-cu12 (RAPIDS memory manager), and nvtx (NVIDIA tracing tools). Most users interact with pylibcudf indirectly through cudf's pandas-like API or cudf.pandas, which provides a zero-code-change accelerator for existing pandas code.
Use it for
- Building custom GPU-accelerated data processing pipelines that need direct access to libcudf algorithms.
- Integrating GPU tabular operations into Spark via Spark RAPIDS or other distributed computing frameworks.
- Developing specialized data transformations where cudf's high-level API is insufficient.
- Creating GPU-native SQL engines or query processors that execute on tabular data.
- Accelerating ETL workflows for large datasets on systems with NVIDIA GPUs and CUDA 12.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have an NVIDIA GPU with CUDA 12 and need GPU-accelerated tabular data processing.
Install it as a dependency of cudf or higher-level RAPIDS libraries rather than directly unless you need low-level libcudf access. Active maintenance, no known vulnerabilities, and permissive Apache 2.0 license make it production-ready. Medium install friction is expected given the specialized CUDA runtime requirements.
Install
pylibcudf-cu12 on PyPI
Before you install
Medium install friction due to CUDA 12 runtime dependencies (libcudf-cu12, cuda-bindings, rmm-cu12, nvtx). Requires matching your CUDA version to the cu12 suffix. Active maintenance with recent releases; repository is well-maintained with 9730 stars.
Requires CUDA 12 runtime environment and compatible NVIDIA GPU; CUDA version must match the cu12 suffix in the package name.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most production environments.
Quickstart
pip install pylibcudf-cu12
import pylibcudf
# Access libcudf functionality through Cython bindings
# Typically used indirectly via cudf or other RAPIDS librariesVerify before relying
- Whether pylibcudf-cu12 is typically used directly or primarily as a dependency of higher-level RAPIDS libraries (cudf, dask-cudf).
- Performance characteristics and typical speedup factors compared to CPU-based alternatives for specific workloads.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagescuda-bindingslibcudf-cu12nvtxrmm-cu12 |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 410,959 / month, #6,861 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersProgramming Language :: PythonProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: DatabaseTopic :: Scientific/Engineering |
Evidence: pylibcudf_cu12-26.8.0-cp311-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; pylibcudf_cu12-26.8.0-cp311-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “rapids libcudf python bindings”
- pylibcudf-cu12pylibcudf-cu12 provides Python bindings for libcudf, a CUDA C++…
- cudf-cu12cuDF is a GPU-accelerated DataFrame library that provides pandas-like…
- cuvs-cu12Provides GPU-accelerated approximate nearest neighbor search and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also cudf-cu12 · libcudf-cu12 · dask-cudf-cu12 · libkvikio-cu12 · pylibraft-cu12 · libraft-cu12 · libcuvs-cu12 · nvidia-cudnn-cu12 · nvidia-cublas-cu11 · libcuml-cu12