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dask-cudf-cu12

library26.6.0pypypi✓ verified 86d ago

Utilities for integrating Dask with cuDF on CUDA 12.x. This package provides the distributed DataFrame functionality backed by cuDF, leveraging cuDF's GPU-accelerated columnar operations. Version 26.4.0 requires Python >=3.11 and is part of the RAPIDS 26.04 release. Releases follow a quarterly cadence aligned with RAPIDS.

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pip install dask-cudf-cu12

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · DASK-CUDF-CU12

dask-cudf-cu12

datapythonv26.6.0

Install

51.1s avg

Import

6685ms

Disk

2478MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v26.2.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 51.1s · import 5.348s · 2560MB

2478MB installed

● package 2478MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

dask_cudf

✓ import dask_cudf

DataFrame

✓ from dask_cudf import DataFrame

✗ from cudf import DataFrame

cudf.DataFrame is local GPU DataFrame, not distributed.

Creates a dask_cudf DataFrame from a cuDF DataFrame and computes the result.

import dask_cudf import cudf # Create a cuDF Series and then a dask_cudf DataFrame df_cudf = cudf.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) ddf = dask_cudf.from_cudf(df_cudf, npartitions=2) print(ddf.compute())

Debug

Known issues

breakingdask-cudf-cu12 is CUDA 12.x only. Use dask-cudf for CUDA 11.x or older. Installing the wrong variant for your CUDA version will cause import errors.

fix

Check your CUDA version with nvidia-smi. Install dask-cudf-cu12 if CUDA >=12.0, otherwise dask-cudf.

affects: all

deprecatedDataFrame.apply_chunks and Groupby.apply_grouped have been removed since v25.12.00. Use map_partitions or groupby.apply instead.

fix

Replace df.apply_chunks(func, ...) with df.map_partitions(func). For grouped operations, use groupby_obj.apply(func, meta=...).

affects: >=25.12.00

gotchaConda environment with both dask-cudf-cu12 and dask-cudf leads to import confusion. Pip similarly can mix packages. Only one variant should be installed.

fix

Use separate conda environments for CUDA 11.x and 12.x, or pip install only the correct variant.

affects: all

gotchadask_cudf.from_cudf() does not automatically repartition data. If the source cuDF DataFrame has too few rows, Dask may underutilize GPUs.

fix

Use npartitions parameter explicitly: dask_cudf.from_cudf(df, npartitions=len(gpu_devices)).

affects: all

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'dask_cudf'

Package not installed or wrong variant installed.

fix

pip install dask-cudf-cu12 (for CUDA 12.x) or pip install dask-cudf (for CUDA 11.x).

AttributeError: module 'dask_cudf' has no attribute 'from_cudf'

Old or mismatched version of dask-cudf/cudf.

fix

Upgrade packages: pip install --upgrade dask-cudf-cu12 cudf-cu12

Upgrade

Version history

26.6.0latest on PyPI · released Jun 9, 2026

Audit

Dependencies

cudf-cu12requiredCore cuDF library required for GPU DataFrames

daskrequiredDask distributed scheduling and DataFrame API

cupy-cuda12xrequiredGPU-backed array operations for cuDF

Agent activity

14 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/rapidsai/cudf ↗

packagehttps://pypi.org/project/dask-cudf-cu12/ ↗

homepagehttps://rapids.ai ↗