distributed-ucxx-cu12
UCX communication module for Dask Distributed
Decision gist · record as of 2026-08-14
Yes, if you are running Dask on GPU clusters with NVLink or InfiniBand and need lower-latency communication than the default backend. The package is actively maintained, has no known vulnerabilities, and uses a permissive license. Install friction is moderate due to CUDA 12.x and compiled dependencies, but this is expected for GPU-accelerated computing. Not needed for CPU-only Dask workloads or single-machine development.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA 12.x runtime and compatible GPU hardware; ucxx-cu12 is a compiled dependency that must match your CUDA installation.
- Released 1 day ago with active maintenance.
- Requires CUDA 12.x and depends on ucxx-cu12, rapids-dask-dependency, and pyyaml.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows use in commercial and private projects with minimal restrictions.
last release 2026-08-13 (1 days) · last repo commit 2026-08-14 · 67 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 167,448 downloads/mo, #10,468 on PyPI
Alternatives
Verify before relying
pip install distributed-ucxx-cu12
from distributed import Client
client = Client("ucxx://scheduler-address:8786")- Whether legacy configuration schema (distributed.comm.ucx.*) will actually be removed or maintained long-term.
- Performance benchmarks comparing UCX backend to default Dask communication for typical workloads.
- Compatibility matrix with specific GPU models and InfiniBand hardware versions.
What it is and what it does
distributed-ucxx-cu12 is a communication backend plugin for Dask Distributed that replaces the default network layer with UCX (Unified Communication X), a framework optimized for high-performance computing. It enables direct GPU-to-GPU transfers via NVLink and CUDA IPC, plus support for InfiniBand and other specialized interconnects. The package registers itself automatically as the 'ucxx' protocol and can be configured via YAML files, environment variables, or Dask's programmatic configuration API.
This is a specialized tool for distributed GPU computing workloads where network communication is a bottleneck. It requires CUDA 12.x and compatible hardware (GPUs with NVLink or InfiniBand-connected nodes). Configuration options cover transport selection (TCP, NVLink, InfiniBand, CUDA copy), RMM memory pool sizing, and UCX environment tuning. The package is actively maintained and part of the RAPIDS ecosystem.
Use it for
- Accelerate multi-GPU Dask workloads on a single node using NVLink for direct GPU memory transfers.
- Enable efficient distributed GPU computing across nodes connected via InfiniBand.
- Replace default Dask networking with UCX for latency-sensitive GPU-accelerated analytics.
- Configure RMM memory pools and transport protocols for custom GPU cluster topologies.
- Migrate from legacy distributed.comm.ucx configuration to the new distributed-ucxx namespace.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are running Dask on GPU clusters with NVLink or InfiniBand and need lower-latency communication than the default backend.
The package is actively maintained, has no known vulnerabilities, and uses a permissive license. Install friction is moderate due to CUDA 12.x and compiled dependencies, but this is expected for GPU-accelerated computing. Not needed for CPU-only Dask workloads or single-machine development.
Install
distributed-ucxx-cu12 on PyPI
Before you install
Released 1 day ago with active maintenance. Requires CUDA 12.x and depends on ucxx-cu12, rapids-dask-dependency, and pyyaml. Medium install friction due to compiled GPU dependencies and CUDA version specificity.
Requires CUDA 12.x runtime and compatible GPU hardware; ucxx-cu12 is a compiled dependency that must match your CUDA installation.
License in practice
BSD-3-Clause permissive license allows use in commercial and private projects with minimal restrictions.
Quickstart
pip install distributed-ucxx-cu12
from distributed import Client
client = Client("ucxx://scheduler-address:8786")Verify before relying
- Whether legacy configuration schema (distributed.comm.ucx.*) will actually be removed or maintained long-term.
- Performance benchmarks comparing UCX backend to default Dask communication for typical workloads.
- Compatibility matrix with specific GPU models and InfiniBand hardware versions.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagespyyamlrapids-dask-dependencyucxx-cu12 |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 167,448 / month, #10,468 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 :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: distributed_ucxx_cu12-0.51.1-py3-none-manylinux_2_28_aarch64.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 › “dask distributed ucx communication”
- distributed-ucxx-cu12Provides a high-performance UCX communication backend for Dask…
- libucxx-cu12Provides Python bindings for UCX (Unified Communication X), enabling…
- ucxx-cu12ucxx-cu12 provides Python bindings for the Unified Communication X…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also libucx-cu12 · ucxx-cu12 · libucxx-cu12 · nvidia-nccl-cu13 · raft-dask-cu12 · nvidia-nccl-cu12 · nvidia-nccl-cu11 · mpich · rapids-dask-dependency · dask-cuda