Installation#

skpro currently supports:

  • Python versions 3.10, 3.11, 3.12, 3.13, and 3.14.

  • Operating systems Mac OS X, Unix-like OS, Windows 8.1 and higher.

See the full list of precompiled wheels available on PyPI.

For frequent issues with installation, consult the Troubleshooting section.

There are three different installation types, depending on your use case:

  • Installing stable skpro releases - for most users and production environments.

  • Installing the latest unstable skpro development version - for pre-release tests.

  • Full developer setup - for contributors and extension developers.

Each setup is explained below.

Installing release versions#

For:

  • Most users

  • Use in production environments

Installing skpro from PyPI#

skpro releases are available via PyPI. To install skpro with core dependencies, excluding soft dependencies, via pip type:

pip install skpro

To install skpro with maximum dependencies, including soft dependencies, install with the all_extras modifier:

pip install "skpro[all_extras]"

Warning

The soft dependencies included in all_extras are only necessary to have all optional estimators and integrations available, or to run all tests. For most user or developer scenarios, installing all_extras is not necessary. If you are unsure, install skpro with core dependencies and install soft dependencies as needed.

Installing skpro from conda#

skpro releases are available via conda from conda-forge. To install skpro with core dependencies via conda type:

conda install -c conda-forge skpro

Note

The conda-forge package can lag behind the latest PyPI release and may support a different set of Python versions. Check the conda-forge package metadata if you need a specific skpro or Python version.

Installing latest unstable development version#

For:

  • pre-release tests, for example early testing of new features

  • not for reliable production use

  • not for contributors or extenders

This type of skpro installation obtains a latest static snapshot of the repository. It is intended for users who want to build or test code using a version of the library that contains the latest updates.

Note

For a full editable developer setup, read the section Full developer setup for contributors and extension developers below.

To install the latest version of skpro directly from the repository, use pip:

pip install git+https://github.com/sktime/skpro.git

To install from a specific branch, use:

pip install git+https://github.com/sktime/skpro.git@<branch_name>

Alternatively, install the latest version from a local clone of the repository. For steps on how to obtain a local clone, follow the git workflow.

pip install .

The . may be replaced with a full or relative path to the root directory of the local clone.

Full developer setup for contributors and extension developers#

For:

  • contributors to the skpro project

  • developers of extensions in closed code bases

  • developers of 3rd party extensions released as open source

To develop skpro locally, or to contribute to the project, set up:

  • a local clone of the skpro repository

  • a virtual environment with an editable install of skpro and its developer dependencies

The following steps guide you through the process.

  1. Follow the git workflow to fork and clone the repository.

  2. Set up a new virtual environment. The following commands use conda, which tends to be beginner friendly. The process is similar for venv or other virtual environment managers.

    Warning

    Using conda via one of the commercial distributions such as Anaconda is in general not free for commercial use and may incur costs or liabilities. Consider using free distributions and channels for package management, and be aware of applicable terms and conditions.

In the conda terminal:

  1. Navigate to your local skpro folder:

    cd skpro

  2. Create a new environment with a supported Python version:

    conda create -n skpro-dev python=3.11

    Warning

    If you already have an environment called skpro-dev from a previous attempt, remove it first or choose a different environment name.

  3. Activate the environment:

    conda activate skpro-dev

  4. Build an editable version of skpro with developer dependencies:

    pip install -e ".[dev]"

    If you also want to install all optional soft dependencies, install them individually after the developer install, or install all of them with:

    pip install -e ".[all_extras,dev]"

    If you are working on documentation, install the documentation dependencies:

    pip install -e ".[dev,docs]"

  5. If everything has worked, you should see a message that skpro was successfully installed.

Troubleshooting#

Module not found#

The most frequent reason for module not found errors is installing skpro with minimum dependencies and using functionality that requires a soft dependency. To resolve this, install the missing package, or install skpro with maximum dependencies as described above.

ImportError#

Import errors are often caused by an improperly linked virtual environment. Make sure that your environment is activated and linked to the IDE or notebook kernel you are using. If you are using Jupyter notebooks, follow the Jupyter virtual environment instructions for adding your virtual environment as a new kernel.

Other Startup Resources#

Virtual environments#

Two good options for virtual environment managers are:

  • conda - beginner friendly, but may incur license fees for commercial use if using a commercial distribution.

  • venv - included with Python and suitable for many local workflows.

Be sure to link your new virtual environment as the Python kernel in whatever IDE you are using. For VS Code, see the VS Code Python environments documentation.

References#

The installation instructions are adapted from sktime’s installation instructions.