distributed-ucxx-cu13 0.48.0


pip install distributed-ucxx-cu13

  Latest version

Released: Feb 06, 2026


Meta
Author: NVIDIA Corporation
Requires Python: >=3.10

Classifiers

Intended Audience
  • Developers

Programming Language
  • Python :: 3
  • Python :: 3.10
  • Python :: 3.11
  • Python :: 3.12
  • Python :: 3.13

UCX Communication Module for Distributed

This is the UCX communication backend for Dask Distributed, providing high-performance communication capabilities using the UCX (Unified Communication X) framework. It enables efficient GPU-to-GPU communication via NVLink (CUDA IPC), InfiniBand support, and various other high-speed interconnects.

Installation

This package is typically installed as part of the UCXX project build process. It can also be installed separately via conda-forge:

mamba install -c conda-forge distributed-ucxx

Or via PyPI (requires selection of CUDA version):

pip install distributed-ucxx-cu13  # For CUDA 13.x
pip install distributed-ucxx-cu12  # For CUDA 12.x

Configuration

This package provides its own configuration system that replaces the UCX configuration previously found in the main Distributed package. Configuration can be provided via:

  1. YAML configuration files: distributed-ucxx.yaml
  2. Environment variables: Using the DASK_DISTRIBUTED_UCXX_ prefix
  3. Programmatic configuration: Using Dask's configuration system

Configuration Schema

The configuration schema is defined in distributed-ucxx-schema.yaml and supports various options:

  • UCX transport configuration: tcp, nvlink, infiniband, cuda-copy, etc.
  • RMM configuration: rmm.pool-size
  • Advanced options: multi-buffer, environment

Example Configuration

New schema:

distributed-ucxx:
  tcp: true
  nvlink: true
  infiniband: false
  cuda-copy: true
  create-cuda-context: true
  multi-buffer: false
  environment:
    log-level: "info"
  rmm:
    pool-size: "1GB"

Legacy schema (may be removed in the future):

distributed:
  comm:
    ucx:
      tcp: true
      nvlink: true
      infiniband: false
      cuda-copy: true
      create-cuda-context: true
      multi-buffer: false
      environment:
        log-level: "info"
      rmm:
        pool-size: "1GB"

Environment Variables

New schema:

export DASK_DISTRIBUTED_UCXX__TCP=true
export DASK_DISTRIBUTED_UCXX__NVLINK=true
export DASK_DISTRIBUTED_UCXX__RMM__POOL_SIZE=1GB

Legacy schema (may be removed in the future):

export DASK_DISTRIBUTED__COMM__UCX__TCP=true
export DASK_DISTRIBUTED__COMM__UCX__NVLINK=true
export DASK_DISTRIBUTED__RMM__POOL_SIZE=1GB

Python Configuration

New schema:

import dask

dask.config.set({
    "distributed-ucxx.tcp": True,
    "distributed-ucxx.nvlink": True,
    "distributed-ucxx.rmm.pool-size": "1GB"
})

Legacy schema (may be removed in the future):

import dask

dask.config.set({
    "distributed.comm.ucx.tcp": True,
    "distributed.comm.ucx.nvlink": True,
    "distributed.rmm.pool-size": "1GB"
})

Usage

The package automatically registers itself as a communication backend for Distributed using the entry point ucxx. Once installed, you can use it by specifying the protocol:

from distributed import Client

# Connect using UCXX protocol
client = Client("ucxx://scheduler-address:8786")

Or when starting a scheduler/worker:

dask scheduler --protocol ucxx
dask worker ucxx://scheduler-address:8786

Migration from Distributed

If you're migrating from the legacy UCX configuration in the main Distributed package, update your configuration keys:

  • distributed.comm.ucx.* is now distributed-ucxx.*
  • distributed.rmm.pool-size is now distributed-ucxx.rmm.pool-size

The old configuration schema is still valid for convenience, but may be removed in a future version.

See Also

Wheel compatibility matrix

Platform Python 3
manylinux_2_28_aarch64
manylinux_2_28_x86_64

Files in release

Extras:
Dependencies:
numba-cuda[cu13] (<0.23.0,>=0.22.1)
pyyaml (>=6)
rapids-dask-dependency (==26.2.*)
ucxx-cu13 (==0.48.*)