Getting and Installing yt

Disclaimer

The Python ecosystem offers many viable tools to setup isolated Python environments, including but not restricted to

We strongly recommend you choose and learn one. However, it is beyond the scope of this page to cover every situation.

We will show you how to install a stable release or from source, using conda or pip, and we will assume that you do so in an isolated environment.

Also note that each yt release supports a limited range of Python versions. Here’s a summary for most recent releases

yt release

Python 2.7

Python3 min | Python3 max

4.1.x

no

3.7 (expected)

3.10 (expected)

4.0.x

no

3.6

3.9

3.6.x

no

3.5

3.8

3.5.x

yes

3.4

3.5

Where the Python3 max column is purely indicative and reflects the newest guaranteed compatible version.

Getting yt

In this document we describe several methods for installing yt. The method that will work best for you depends on your precise situation:

Installing a stable release

The latest stable release can be obtained from Pypi with pip

$ python -m pip install --upgrade pip
$ python -m pip install --user yt

Or using the Anaconda/Miniconda Python distributions

$ conda install --channel conda-forge yt

Building from source

To build yt from source, you need git, and a C compiler (such as gcc or clang).

Then run

$ git clone https://github.com/yt-project/yt
$ cd yt
$ python -m pip install --upgrade pip
$ python -m pip install --user -e .

Leveraging optional yt runtime dependencies

Some relatively heavy runtime dependencies are not included in your build by default as they may be irrelevant in your workflow. Common examples include h5py, mpi4py, astropy or scipy. yt implements a on-demand import mechanism that allows it to run even when they are not installed until they’re needed, in which case it will raise an ImportError, pointing to the missing requirement.

If you wish to get everything from the start, you may specify it when building yt as by appending [full] to the target name when calling pip, i.e.,

$ # stable release
$ python -m pip install --user yt[full]
$ # from source
$ python -m pip install --user -e .[full]

Testing Your Installation

To test to make sure everything is installed properly, try running yt at the command line:

$ python -c "import yt"

If this runs without raising errors, you have successfully installed yt. Congratulations!

Otherwise, read the error message carefully and follow any instructions it gives you to resolve the issue. Do not hesitate to contact us so we can help you figure it out.

Updating yt

For pip-based installations:

$ python -m pip install --upgrade yt

For conda-based installations:

$ conda update yt

For git-based installations (yt installed from source), we provide the following one-liner facility

$ yt update

This will pull any changes from GitHub, and recompile yt if necessary.

Uninstalling yt

If you’ve installed via pip (either from Pypi or from source)

$ python -m pip uninstall yt

Or with conda

$ conda uninstall yt

TroubleShooting

If you are unable to locate the yt executable (i.e. executing yt version at the bash command line fails), then you likely need to add the $HOME/.local/bin (or the equivalent on your OS) to your PATH. Some Linux distributions do not include this directory in the default search path.

Additional Resources

yt Distribution Packages

Some operating systems have yt pre-built packages that can be installed with the system package manager. Note that the packages in some of these distributions may be out of date.

Note

Since the third-party packages listed below are not officially supported by yt developers, support should not be sought out on the project mailing lists or Slack channels. All support requests related to these packages should be directed to their official maintainers.

While we recommended installing yt with either pip or conda, a number of third-party packages exist for the distributions listed below.

https://repology.org/badge/vertical-allrepos/python:yt.svg?header=yt%20packaging%20status

Intel distribution for Python

A viable alternative to the installation based on Anaconda is the use of the Intel Distribution for Python. For Parallel Computation on Intel architectures, especially on supercomputers, a large performance and scalability improvement over several common tasks has been demonstrated. See Parallel Computation for a discussion on using yt in parallel. Leveraing this specialized distribution for yt requires that you install some dependencies from the intel conda channel before installing yt itself, like so

$ conda install -c intel numpy scipy mpi4py cython git sympy ipython matplotlib netCDF4
$ python -m install --user yt