Instructions to install the package from different sources.
Released versions
PyPI
Latest release
To install the latest release of mlforecast from PyPI you just have to run the following in a terminal:pip install mlforecast
Specific version
If you want a specific version you can include a filter, for example:pip install "mlforecast==0.3.0"to install the 0.3.0 versionpip install "mlforecast<0.4.0"to install any version prior to 0.4.0
Extras
polars Using polars dataframes:pip install "mlforecast[polars]"
Saving to remote storages
If you want to save your forecast artifacts to a remote storage like S3
or GCS you can use the following extras:
- Saving to S3:
pip install "mlforecast[aws]" - Saving to Google Cloud Storage:
pip install "mlforecast[gcp]" - Saving to Azure Data Lake:
pip install "mlforecast[azure]"
- dask:
pip install "mlforecast[dask]" - ray:
pip install "mlforecast[ray]" - spark:
pip install "mlforecast[spark]"
Conda
Latest release
The mlforecast package is also published to conda-forge, which you can install by running the following in a terminal:conda install -c conda-forge mlforecast
Note that this happens about a day later after it is published to PyPI,
so you may have to wait to get the latest release.
Specific version
If you want a specific version you can include a filter, for example:conda install -c conda-forge "mlforecast==0.3.0"to install the 0.3.0 versionconda install -c conda-forge "mlforecast<0.4.0"to install any version prior to 0.4.0
Development version
If you want to try out a new feature that hasn’t made it into a release yet you have the following options:- Install from github:
pip install git+https://github.com/Nixtla/mlforecast - Clone and install:
git clone https://github.com/Nixtla/mlforecast mlforecast-dev && pip install mlforecast-dev/, which will install the version from the current main branch.

