aeon currently supports Python versions 3.8, 3.9, 3.10 and 3.11. Prior to these
instructions, please ensure you have a compatible version of Python installed
(i.e. from https://www.python.org).
aeon is available for most well-known operating systems, and is frequently tested
on macOS, Ubuntu and Windows servers by our development CI.
When it comes to installing
aeon, there are currently three primary options:
Install the latest release from PyPi. This is the recommended option for most users.
Install the latest release from conda-forge. An alternative release installation using conda.
Install the latest development version from GitHub via pip. This will include the latest features and bug fixes, but can be more unstable than the latest release.
Building the package from source. This is a requirement for users who wish to develop the
aeoncodebase and for most other contributions to the project. Our developer installation guide is available here.
All installation options include the core dependencies required to run the framework
aeon. Some estimators and functionality require optional dependencies.
Without these dependencies, you may find that you will be prompted to install an
additional package when trying to use certain functionality.
For each installation option, we provide a method to install only the core dependencies
and a method to install all dependencies (barring certain unstable ones). Installing
all dependencies can take a while to process the installation and introduce limitations
on the versioning of other packages, but will allow all
aeon functionality to be used
Install the latest release from PyPi#
We recommend creating a virtual environment for your
installation. This will ensure that the dependencies of
aeon do not conflict with
other packages you may have installed.
aeon releases are available via PyPI. To install
aeon release with core dependencies via
pip install -U aeon
aeon with all stable dependencies, install with the
modifier. This will also install core dependencies, so the above command is not
pip install -U aeon[all_extras]
Some of the dependencies included in
all_extras do not work on Mac ARM-based
processors, such as M1, M2, M1Pro, M1Max or M1Ultra. This may cause an error during
installation. Mode details can be found in the troubleshooting section below.
After installation, you can verify that
aeon has been installed correctly by
running the following commands:
pip show aeon # see information about the installation i.e. version and file location pip freeze # see all installed packages for the current environment
For more information on the dependencies of
aeon and more dependencies groups (such
as only dependencies for deep learning, or a list less stable dependencies excluded
all_extras), see the
Install the latest release from conda-forge#
aeon releases are also available via conda-forge.
Run the following to create a new environment for aeon and install the package:
conda create -n aeon-env -c conda-forge aeon conda activate aeon-env
Post-installation you can verify that
aeon has been installed correctly by running
conda list aeon # see information about the installation i.e. version and file location conda list # see all installed packages for the current environment
conda installations, optional dependencies must be installed
Install the latest development version using pip#
Like the above method, we recommend creating a virtual environment
If you already have the latest
aeon release or the
installed, you will have to uninstall it prior to following these instructions:
pip uninstall aeon
The latest developments and bugfixes for
aeon are available on the aeon
main branch. The
main branch can be
installed directly from GitHub using
pip install -U git+https://github.com/aeon-toolkit/aeon.git@main
aeon from GitHub
main branch with all stable dependencies, the following
command can be used.
pip install -U "aeon[all_extras] @ git+https://github.com/aeon-toolkit/aeon.git@main"
The same warnings and information regarding the MacOS ARM processor, checking install versioning and pyproject.toml dependencies given in the previous section apply here as well.
Using a pip venv#
In order to avoid potential conflicts with other packages, we strongly recommended
using a virtual environment (venv)
or a fresh
conda environment for the above installation options.
You can create a virtual environment using the following commands. The name virtual
aeon-venv can be replaced with a name of your choosing.
Windows and macOS:
python -m venv aeon-venv
python3 -m venv aeon-venv
This environment can then be activated using the following commands:
macOS and Linux:
Note that this will only activate the environment for the current terminal session. If you wish to use the environment in a different terminal session, you will need to activate it again.
If the common errors below do not help, it may be worth checking out the scikit-learn troubleshooting section
The most frequent reason for
ModuleNotFoundError is installing
minimum dependencies (i.e. just
pip install aeon) and using an estimator which
interfaces a package that has not been installed in the environment. To resolve this,
install the missing package, or install
aeon with maximum dependencies (see above)
or install the individual packages as prompted by the error.
Import errors are often caused by an improperly linked virtual environment. Make sure that your environment is activated and linked to whatever IDE you are using. You can find the instructions for doing so in VScode here. If you are using Jupyter Notebooks, follow these instructions for adding your virtual environment as a new kernel for your notebook.
all_extras on Mac with an ARM processor#
If you are using a Mac with an ARM processor, you may encounter an error when installing
aeon[all_extras]. This is due to the fact that some libraries included in
are not compatible with ARM-based processors.
The workaround is not to install some of the packages in
all_extras and install ARM
compatible replacements for others:
Do not install the following packages:
tensorflowpackage with the following packages:
Also, ARM-based processors have issues when installing packages distributed as source
distributions instead of Python wheels. To avoid this issue when installing a package,
you can try installing it through
conda or use a prior version of the package that
was distributed as a wheel.