Installing dask and distributed packages can be confusing

Our installation docs recommend that people do the following

conda install dask distributed -c conda-forge

or

pip install dask[complete] distributed --upgrade

However we shouldn’t expect most people to do the proper diligence of reading installation docs. We all tend to just guess that conda install name-of-project works pretty well most of the time. Unfortunately, if you’ve heard that Dask does distributed computing, you conda install dask, and then try out any distributed example then you’re likely to receive an import error, which makes for a bad first impression.

There are a few ways that we could resolve this problem:

1. We could provide informative errors whenever someone tries to do a dask.distributed thing. These would point them to installation docs. This wouldn’t help if the just did import distributed though I think that most of the public materials we produce at this point always import from dask.distributed.
2. We could switch out the conda package dask with a metapackage that included both dask and distributed. This would be foolproof in the conda case but would be a bit of an organizational hassle from a packaging perspective. We would rename the existing package dask-core (or something similar) and then switch in the dask metapackage. We would have to do this on conda-forge at the same time.
3. We could find some way within conda to have a cycle (dask includes distributed, distributed includes dask)
4. Other suggestions?

I’m in favor of starting with option 1, though would love to find a more thorough alternative.

cc @pzwang @ilanschnell

[TomAugspurger]

We’ve all made that mistake at least once 😃

On Sun, Apr 29, 2018 at 6:09 AM, YorT [e-mail] wrote:

@TomAugspurger https://github.com/TomAugspurger sorry, ignore me, total noob mistake on my part. I had a testing file called dask.py being used which was being imported instead of actual dask! Lesson learned!

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/dask/distributed/issues/962#issuecomment-385243388, or mute the thread https://github.com/notifications/unsubscribe-auth/ABQHIiW8iFeliNnwupPCJflBJDV-ox7hks5ttZ9rgaJpZM4Ml1Rb .

[jzwinck]

Why is the Conda package for dask.distributed not called dask.distributed or dask-distributed (what I would expect to install, but does not exist)? It’s called distributed which is super-confusing and there is nothing helpful in the output of conda info distributed -c conda-forge. Further, distributed does not express a dependency on dask. Am I even looking at the right package?

distributed 1.18.0 py35_0
-------------------------
file name   : distributed-1.18.0-py35_0.tar.bz2
name        : distributed
version     : 1.18.0
build string: py35_0
build number: 0
channel     : defaults
size        : 632 KB
arch        : x86_64
date        : 2017-08-16
license     : BSD 3-Clause
license_family: BSD
md5         : d0c3b75432a2037425478d76ba0870bc
noarch      : None
platform    : linux
url         : https://repo.continuum.io/pkgs/free/linux-64/distributed-1.18.0-py35_0.tar.bz2
dependencies:
    bokeh >=0.12.3
    click >=6.6
    cloudpickle >=0.2.2
    msgpack-python
    psutil
    python 3.5*
    six
    sortedcontainers
    tblib
    toolz >=0.7.4
    tornado >=4.4
    zict >=0.1.2