dask-image
Distributed image processing
Decision gist · record as of 2026-08-14
Yes, if you have large image datasets or need to parallelize image processing across multiple cores or machines. The low install friction, active maintenance, and permissive license make it a reasonable choice. However, Pre-Alpha status means the API may change and feature coverage is incomplete—evaluate whether the available operations match your use case before committing to it in production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; best suited for workflows where image data exceeds available RAM or where parallelization across multiple workers is beneficial.
- Low friction installation with a pure-Python wheel and five well-established dependencies (dask, numpy, scipy, pims, tifffile).
- Actively maintained with a recent release and ongoing commits; classified as Pre-Alpha, so expect API changes and incomplete feature coverage.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive, allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
last release 2026-05-27 (79 days) · last repo commit 2026-08-03 · 227 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 326,771 downloads/mo, #7,574 on PyPI
Alternatives
Verify before relying
pip install dask-image
import dask_image
import dask.array as da
# Load and process images using dask arrays for distributed computation- Specific image formats and I/O operations supported beyond pims and tifffile integration.
- Performance characteristics and scalability limits for typical distributed image workloads.
- Maturity of individual algorithms given Pre-Alpha classification.
What it is and what it does
dask-image brings distributed computing to image processing by building on Dask's lazy evaluation and task scheduling. It allows you to work with image data as Dask arrays, automatically parallelizing operations across available CPU cores or a cluster of machines. The package integrates with numpy, scipy, pims, and tifffile to provide image I/O and standard processing routines in a distributed context.
It is designed for workflows where image datasets are too large to fit in memory on a single machine, or where you want to exploit parallelism to speed up batch processing. Since it is classified Pre-Alpha, the API and feature set are still evolving, and not all image processing operations may be available yet. It targets developers working with scientific imaging, remote sensing, or other domains involving large-scale image analysis.
Use it for
- Process multi-gigabyte satellite or microscopy image stacks that exceed single-machine RAM.
- Parallelize batch image transformations (filtering, resizing, segmentation) across a compute cluster.
- Build reproducible image analysis pipelines that scale from laptop to cloud without code changes.
- Integrate distributed image operations into existing Dask workflows for data science projects.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have large image datasets or need to parallelize image processing across multiple cores or machines.
The low install friction, active maintenance, and permissive license make it a reasonable choice. However, Pre-Alpha status means the API may change and feature coverage is incomplete—evaluate whether the available operations match your use case before committing to it in production.
Install
dask-image on PyPI
Before you install
Low friction installation with a pure-Python wheel and five well-established dependencies (dask, numpy, scipy, pims, tifffile). Actively maintained with a recent release and ongoing commits; classified as Pre-Alpha, so expect API changes and incomplete feature coverage.
Requires Python 3.9 or later; best suited for workflows where image data exceeds available RAM or where parallelization across multiple workers is beneficial.
License in practice
BSD-3-Clause is permissive, allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install dask-image
import dask_image
import dask.array as da
# Load and process images using dask arrays for distributed computationVerify before relying
- Specific image formats and I/O operations supported beyond pims and tifffile integration.
- Performance characteristics and scalability limits for typical distributed image workloads.
- Maturity of individual algorithms given Pre-Alpha classification.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesdasknumpyscipypimstifffile |
| Maintenance | Actively maintained 79 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 326,771 / month, #7,574 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 2 - Pre-AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9 |
Evidence: dask_image-2026.5.0-py3-none-any.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 › “distributed image processing”
- dask-imageDistributed image processing using Dask, enabling large-scale image…
- daftDaft is a distributed dataframe engine for processing images, audio,…
- spatial_imagespatial-image provides an N-dimensional spatial image data structure…
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 dask · dask-glm · dask-ml · dask-geopandas · distributed · dask-awkward · dask-cuda · prefect-dask · dask-cudf-cu12 · coiled