---
id: dask-cuda
version: "26.8.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# dask-cuda — Utilities for Dask and CUDA interactions

License: permissive · Maintenance: active · Downloads: 302.3K/mo

## What it is and what it does

Dask CUDA is a utility library that handles the deployment and lifecycle management of Dask workers on systems with multiple CUDA-enabled GPUs. It provides a LocalCUDACluster abstraction that simplifies spinning up a distributed Dask cluster where each worker is bound to a specific GPU, allowing you to parallelize work across available hardware without manually managing worker-to-GPU assignment.

The library does not automatically convert existing Dask code to run on GPUs—that remains the responsibility of your application code and the libraries you use (such as RAPIDS or CuPy). Instead, it focuses on the infrastructure layer: making it straightforward to create and manage a multi-GPU Dask cluster, handle worker placement, and coordinate resource allocation. It depends on click, numpy, pandas, zict, nvidia-ml-py for GPU monitoring, and rapids-dask-dependency for integration with the RAPIDS ecosystem.

Use it for:

- Set up a multi-GPU Dask cluster on a single machine or cluster for distributed data processing with GPU acceleration.
- Manage worker-to-GPU affinity automatically when scaling Dask workloads across available CUDA devices.
- Integrate GPU-accelerated RAPIDS libraries (cuDF, cuML) with Dask's distributed scheduler for large-scale data science.
- Monitor and coordinate GPU resource allocation across multiple Dask workers in a heterogeneous cluster.
- Prototype and deploy distributed GPU workloads without manually writing worker spawning and GPU binding logic.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Dask CUDA provides utilities for deploying and managing Dask workers on multi-GPU systems, enabling distributed computation across CUDA-enabled hardware.

Yes, if you need to deploy Dask across multiple GPUs. The library is actively maintained, has no known vulnerabilities, and uses a permissive license. Install friction is moderate due to CUDA/driver dependencies, but those are prerequisites for any GPU work anyway. Not suitable if you lack GPU hardware or are looking for automatic GPU code conversion.

## Install

pip install dask-cuda
uv add dask-cuda
poetry add dask-cuda

## Installing dask-cuda

Before you install: Medium install friction due to dependencies on cuda-core and nvidia-ml-py, which require CUDA toolkit and NVIDIA drivers to be present on the system. Actively maintained with recent releases; last commit 2026-08-13.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most deployment scenarios.

Quickstart:

pip install dask-cuda

from dask_cuda import LocalCUDACluster
from dask.distributed import Client

cluster = LocalCUDACluster()
client = Client(cluster)

Requires CUDA-enabled GPU hardware, NVIDIA drivers, and CUDA toolkit installed on the system; Python >= 3.11.

Verify before relying:

- Whether the package automatically handles GPU memory management and spilling between GPUs and host memory.
- Performance characteristics and scalability limits for typical multi-GPU cluster sizes.
- Compatibility with specific RAPIDS libraries (e.g., cuDF, cuML) beyond the documented integration points.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 302.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

dask gpu cluster management, multi-gpu distributed computing, cuda worker deployment, dask cuda cluster, gpu-accelerated dask, distributed gpu computing, dask rapids integration, gpu-computing, distributed-systems, dask-ecosystem

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