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Installation#

sktime currently supports:

  • Python versions 3.8, 3.9, 3.10, 3.11, and 3.12.

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

See here for a full list of precompiled wheels available on PyPI.

For frequent issues with installation, consult the `Release versions - troubleshooting`_ section.

There are three different installation types: * Installing sktime releases * Installing the latest sktime development version * For developers of sktime and 3rd party extensions: Developer setup

Each of these three setups are explained below.

Release versions#

Installing sktime from PyPI#

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

pip install sktime

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

pip install sktime[all_extras]

sktime also comes with dependency sets specific to learning task, i.e., estimator scitype. These are curated selections of the most common soft dependencies for the respective learning task. The available dependency sets are of the same names as the respective modules: forecasting, transformations, classification, regression, clustering, param_est, networks, annotation, alignment.

Warning

Some of the soft dependencies included in all_extras and the curated soft dependency sets 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.

Warning

The soft dependencies with all_extras are only necessary to have all estimators available, or to run all tests. However, this slows down the downloads, and multiples test time. For most user or developer scenarios, downloading all_extras will not be necessary. If you are unsure, install sktime with core dependencies, and install soft dependencies as needed. Alternatively, install dependency sets specific to learning task, see above.

Installing sktime from conda#

sktime releases are available via conda from conda-forge. To install sktime with core dependencies, excluding soft dependencies via conda type:

conda install -c conda-forge sktime

To install sktime with maximum dependencies, including soft dependencies, install with the all-extras recipe:

conda install -c conda-forge sktime-all-extras

Note: not all soft dependencies of sktime are also available on conda-forge, sktime-all-extras includes only the soft dependencies that are available on conda-forge. The other soft dependencies can be installed via pip, after conda install pip.

Development versions#

To install the latest development version of sktime, or earlier versions, the sequence of steps is as follows:

Step 1 - git clone the sktime repository, the latest version or an earlier version. Step 2 - ensure build requirements are satisfied Step 3 - pip install the package from a git clone, with the editable parameter.

Detail instructions for all steps are given below. For brevity, we discuss steps 1 and 3 first; step 2 is discussed at the end, as it will depend on the operating system.

Step 1 - clone the git repository#

The sktime repository should be cloned to a local directory, using a graphical user interface, or the command line.

Using the git command line, the sequence of commands to install the latest version is as follows:

git clone https://github.com/sktime/sktime.git
cd sktime
git checkout main
git pull

To build a previous version, replace line 3 with:

git checkout <VERSION>

This will checkout the code for the version <VERSION>, where <VERSION> is a valid version string. Valid version strings are the repository’s git tags, which can be inspected by running git tag.

You can also download a zip archive of the version from GitHub.

Step 2 - building sktime from source#

To build and install sktime from source, navigate to the local clone’s root directory and type:

pip install .

Alternatively, the . may be replaced with a full or relative path to the root directory.

For a developer install that updates the package each time the local source code is changed, install sktime in editable mode, via:

pip install --editable .[dev]

This allows editing and extending the code in-place. See also pip reference on editable installs).

Note

You will have to re-run:

pip install --editable .

every time the source code of a compiled extension is changed (for instance when switching branches or pulling changes from upstream).

Building binary packages and installers#

The .whl package and .exe installers can be built with:

pip install build
python -m build --wheel

The resulting packages are generated in the dist/ folder.

Contributor or 3rd party extension developer setup#

  1. Follow the Git workflow: Fork and clone the repository as described in [Git and GitHub workflow](https://www.sktime.net/en/stable/developer_guide/git_workflow.html)

2. Set up a new virtual environment. Our instructions will go through the commands to set up a conda environment which is recommended for sktime development. This relies on an anaconda installation. The process will be similar for venv or other virtual environment managers.

In the anaconda prompt terminal:

  1. Navigate to your local sktime folder, cd sktime or similar

  2. Create a new environment with a supported python version: conda create -n sktime-dev python=3.8 (or python=3.11 etc)

    Warning

    If you already have an environment called “sktime-dev” from a previous attempt you will first need to remove this.

  3. Activate the environment: conda activate sktime-dev

6. Build an editable version of sktime. In order to install only the dev dependencies, pip install -e .[dev] If you also want to install soft dependencies, install them individually, after the above, or instead use: pip install -e .[all_extras,dev] to install all of them.

Note

If this step results in a “no matches found” error, it may be due to how your shell handles special characters.

  • Possible solution: use quotation marks:

    pip install -e ."[dev]"

  1. If everything has worked you should see message “successfully installed sktime”

Some users have experienced issues when installing NumPy, particularly version 1.19.4.

Note

Another option under Windows is to follow the instructions for `Unix-like OS`_, using the Windows Subsystem for Linux (WSL). For installing WSL, follow the instructions here.

Troubleshooting#

Module not found#

The most frequent reason for module not found errors is installing sktime with minimum dependencies 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 sktime with maximum dependencies (see above).

ImportError#

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. If you are using Jupyter Notebooks, follow these instructions for adding your virtual environment as a new kernel for your notebook.

Installing all_extras on mac with ARM processor#

If you are using a mac with an ARM processor, you may encounter an error when installing sktime[all_extras]. This is due to the fact that some libraries included in all_extras 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:

    • esig

    • prophet

    • tsfresh

    • tslearn

  • Replace tensorflow package with the following packages:

    • tensorflow-macos

    • tensorflow-metal (optional)

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.

Other Startup Resources#

Virtual environments#

Two good options for virtual environment managers are:

  • conda (many sktime community members us this)

  • venv (also quite good!).

Be sure to link your new virtual environment as the python kernel in whatever IDE you are using. You can find the instructions for doing so in VScode here.

References#

The installation instruction are adapted from scikit-learn’s advanced installation instructions.

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