Installation¶

There are several ways to install Modin. Most users will want to install with pip or using conda tool, but some users may want to build from the master branch on the GitHub repo. The master branch has the most recent patches, but may be less stable than a release installed from pip or conda.

Installing with pip¶

Stable version¶

Modin can be installed with pip on Linux, Windows and MacOS. 2 engines are available for those platforms: Ray and Dask To install the most recent stable release run the following:

pip install -U modin # -U for upgrade in case you have an older version

If you don’t have Ray or Dask installed, you will need to install Modin with one of the targets:

pip install modin[ray] # Install Modin dependencies and Ray to run on Ray
pip install modin[dask] # Install Modin dependencies and Dask to run on Dask
pip install modin[all] # Install all of the above

Modin will automatically detect which engine you have installed and use that for scheduling computation!

Release candidates¶

Before most major releases, we will upload a release candidate to test and check if there are any problems. If you would like to install a pre-release of Modin, run the following:

pip install --pre modin

These pre-releases are uploaded for dependencies and users to test their existing code to ensure that it still works. If you find something wrong, please raise an issue or email the bug reporter: bug_reports@modin.org.

Installing specific dependency sets¶

Modin has a number of specific dependency sets for running Modin on different execution engines and storage formats or for different functionalities of Modin. Here is a list of dependency sets for Modin:

pip install "modin[dask]" # If you want to use the Dask execution engine

Installing on Google Colab¶

Modin can be used with Google Colab via the pip command, by running the following code in a new cell:

!pip install modin[all]

Since Colab preloads several of Modin’s dependencies by default, we need to restart the Colab environment once Modin is installed by either clicking on the "RESTART RUNTIME" button in the installation output or by run the following code:

# Post-install automatically kill and restart Colab environment
import os
os.kill(os.getpid(), 9)

Once you have restarted the Colab environment, you can use Modin in Colab in subsequent sessions.

Note that on the free version of Colab, there is a limit on the compute resource. To leverage the full power of Modin, you may have to upgrade to Colab Pro to get access to more compute resources.

Installing with conda¶

Using conda-forge channel¶

Modin releases can be installed using conda from conda-forge channel. Starting from 0.10.1 it is possible to install modin with chosen engine(s) alongside. Current options are:

Package name in conda-forge	Engine(s)	Supported OSs
modin	Dask	Linux, Windows, MacOS
modin-dask	Dask	Linux, Windows, MacOS
modin-ray	Ray	Linux, Windows
modin-omnisci	OmniSci	Linux
modin-all	Dask, Ray, OmniSci	Linux

So for installing Dask and Ray engines into conda environment following command should be used:

conda install -c conda-forge modin-ray modin-dask

All set of engines could be available in conda environment by specifying

conda install -c conda-forge modin-all

or explicitly

conda install -c conda-forge modin-ray modin-dask modin-omnisci

Using Intel® Distribution of Modin¶

With conda it is also possible to install Intel Distribution of Modin, a special version of Modin that is part of Intel® oneAPI AI Analytics Toolkit. This version of Modin is powered by OmniSci engine that contains a bunch of optimizations for Intel hardware. More details can be found on Intel Distribution of Modin page.

Installing from the GitHub master branch¶

If you’d like to try Modin using the most recent updates from the master branch, you can also use pip.

pip install git+https://github.com/modin-project/modin

This will install directly from the repo without you having to manually clone it! Please be aware that these changes have not made it into a release and may not be completely stable.

Windows¶

All Modin engines except OmniSci are available both on Windows and Linux as mentioned above. Default engine on Windows is Ray. It is also possible to use Windows Subsystem For Linux (WSL), but this is generally not recommended due to the limitations and poor performance of Ray on WSL, a roughly 2-3x cost.

Building Modin from Source¶

If you’re planning on contributing to Modin, you will need to ensure that you are building Modin from the local repository that you are working off of. Occasionally, there are issues in overlapping Modin installs from pypi and from source. To avoid these issues, we recommend uninstalling Modin before you install from source:

pip uninstall modin

To build from source, you first must clone the repo. We recommend forking the repository first through the GitHub interface, then cloning as follows:

git clone https://github.com/<your-github-username>/modin.git

Once cloned, cd into the modin directory and use pip to install:

cd modin
pip install -e .