EvalML is available for Python 3.9 - 3.11. It can be installed from pypi, conda-forge, or from source.

To install EvalML on your platform, run one of the following commands:

$ pip install evalml
$ conda install -c conda-forge evalml
# See the EvalML with core dependencies only section
$ pip install evalml --no-dependencies
$ pip install -r core-requirements.txt
# See the EvalML with core dependencies only section
$ conda install -c conda-forge evalml-core

EvalML with core dependencies only#

EvalML includes several optional dependencies. The xgboost and catboost packages support pipelines built around those modeling libraries. The plotly and ipywidgets packages support plotting functionality in automl searches. These dependencies are recommended, and are included with EvalML by default but are not required in order to install and use EvalML.

EvalML’s core dependencies are listed in core-requirements.txt in the source code, while the default collection of requirements is specified in pyproject.toml’s dependencies.

To install EvalML with only the core-required dependencies with pypi, first download the EvalML source from pypi or github to access the requirements files before running the following command.

$ pip install evalml --no-dependencies
$ pip install -r core-requirements.txt
$ conda install -c conda-forge evalml-core


EvalML allows users to install add-ons individually or all at once:

  • Update Checker: Receive automatic notifications of new EvalML releases

  • Time Series: Use EvalML with Facebook’s Prophet library for time series support.

$ pip install evalml[complete]
$ pip install evalml[prophet]
$ pip install evalml[updater]
$ conda install -c conda-forge alteryx-open-src-update-checker

Time Series support with Facebook’s Prophet#

To support the Prophet time series estimator, be sure to install it as an extra requirement. Please note that this may take a few minutes.

pip install evalml[prophet]

Another option for installing Prophet with CmdStan as a backend is to use make installdeps-prophet.

Windows Additional Requirements & Troubleshooting#

If you are using pip to install EvalML on Windows, it is recommended you first install the following packages using conda:

  • numba (needed for shap and prediction explanations). Install with conda install -c conda-forge numba

  • graphviz if you’re using EvalML’s plotting utilities. Install with conda install -c conda-forge python-graphviz

The XGBoost library may not be pip-installable in some Windows environments. If you are encountering installation issues, please try installing XGBoost from Github before installing EvalML or install evalml with conda.

Note: there are two graphviz, python-graphviz and graphviz. If you run into issues, ensure that python-graphviz version is >= 0.20.3. If there are still issues related to graphviz, you can try conda install -c conda-forge graphviz where graphviz version >= 9.0.0

Mac Additional Requirements & Troubleshooting#

In order to run on Mac, LightGBM requires the OpenMP library to be installed, which can be done with HomeBrew by running:

brew install libomp

Additionally, graphviz can be installed by running:

brew install graphviz

Installing EvalML on an M1 Mac#

Not all of EvalML’s dependencies support Apple’s new M1 chip. For this reason, pip or conda installing EvalML will fail. The core set of EvalML dependencies can be installed in the M1 chip, so we recommend you install EvalML with core dependencies.

Alternatively, there is experimental support for M1 chips with the Rosetta terminal. After setting up a Rosetta terminal, you should be able to pip or conda install EvalML.

For Docker fans, an included Dockerfile.arm can be built and run to provide an environment for testing. Details are included within.