---
blogpost: true
date: Nov 29, 2018
title: Dask Version 1.0
author: the Dask Team
tags: Programming, Python, scipy, dask
---

We are pleased to announce the release of Dask version 1.0.0!

Usually in release blogposts we outline important features and changes since
the last major version. Because of the 1.0 version number, this post will be a
bit different. Instead we'll talk about what this version number means to us,
and discuss the broader context of Dask projects more generally.

## What 1.0 means to us

Version 1.0 software means different things to different groups.
In some communities it might mean ...

- The first version of a package
- When a package is first ready for production use
- When a package has reached API stability
- ...

It is common in the PyData ecosystem to wait a _long time_ before releasing a
version 1.0. For example neither Pandas nor Scikit-Learn, arguably two of the
most well used PyData packages in production, have yet declared a 1.0 version
number (today they are at versions 0.23 and 0.20 respectively). And yet each
package is widely used in production by organizations that demand high degrees
of stability.

Dask is not as API-stable as Pandas or Scikit-Learn, but it's pretty close.
The project rarely invents new APIs, instead preferring to implement
pre-existing APIs (like the NumPy/Pandas/Scikit-Learn APIs) or standard language
protocols (like async-await, concurrent.futures, Queues, Locks, and so on).
Additionally, Dask is well used in production today across sectors ranging from
risk-tolerant industries like startups and quantitative finance shops, to
risk-averse institutions like banks, large enterprises, and governments.

_When we say that Dask has reached 1.0 we mean that it is ready to be used in
production. We are late in saying this. This happened a long time ago._

## Development will continue as before

Dask is living software that exists in a rapidly evolving space. Nothing is
changing about our internal stability practices. We will continue to add new
features, deprecate old ones, and fix bugs with the same policies. We always
try to minimize negative effects on users when making these internal changes
while maximizing the speed at which we can deliver new bugfixes and features.
This is hard and requires care, but we believe that we've done this decently in
the past so hopefully you haven't noticed much. We will continue to operate
the same way into the future.

_The 1.0 version change does not affect our development cycle.
There are no LTS versions beyond what we already provide._

## Different Dask packages move at different speeds

Dask is able to evolve and experiment rapidly while maintaining a stable core
because it is split into sub-packages, each of which evolves independently, has
its own maintainers, its own versions, and its own release cycle. Some Dask
subprojects have had versions above 1.0 for a long time, while others are still
unstable.

_Dask's version number is hard to define today because it is composed of so
many independent efforts by different groups. This is similar to situation in
Jupyter, or in the Numeric Python ecosystem itself._

## Thanks

Finally, we're grateful to everyone who has contributed to the project over the
years, either by contributing code, reviews, documentation, discussion, bug
reports, well written questions and answers, visual designs, and well wishes.
This means a lot to us.

Today there are dozens of [`dask-*` packages on
PyPI](https://pypi.org/search/?q=dask) that support thousands of users and
several more that incorporate Dask for parallelism. We're thankful to play a
role in such a vibrant community.