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Released versions

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PyPI

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Latest release

To install the latest release of mlforecast from PyPI you just have to run the following in a terminal: pip install mlforecast

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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 version
  • pip install "mlforecast<0.4.0" to install any version prior to 0.4.0

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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]"
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]"

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Conda

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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.

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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 version
  • conda install -c conda-forge "mlforecast<0.4.0" to install any version prior to 0.4.0

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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.