Skip to main contentReleased 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
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]"
Distributed training
If you want to perform distributed training you can use either dask, ray
or spark. Once you know which framework you want to use you can include
its extra:
- 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.
