Install¶
EvalML is available for Python 3.7 and 3.8 with experimental support 3.9. It can be installed with pip or conda.
Pip with all dependencies¶
To install evalml with pip, run the following command:
pip install evalml
Pip with core dependencies¶
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, and optional requirements are isted in requirements.txt.
To install EvalML with only the core required dependencies, download the EvalML source from pypi to access the requirements files. Then run the following:
pip install evalml --no-dependencies
pip install -r core-requirements.txt
Add-ons¶
You can install add-ons individually or all at once by running:
pip install evalml[complete]
Time Series Support
Add time series support with Facebook’s Prophet
pip install evalml[prophet]
Please note that this may take a few minutes. Prophet is currently only supported via pip installation in EvalML.
Update checker
Receive automatic notifications of new EvalML releases
pip install evalml[update_checker]
Conda with all dependencies¶
To install evalml with conda run the following command:
conda install -c conda-forge evalml
Conda with core dependencies¶
To install evalml with only core dependencies run the following command:
conda install -c conda-forge evalml-core
Windows¶
Additionally, if you are using pip to install EvalML, 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.
Mac¶
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
Python 3.9 support¶
Evalml can still be installed with pip in python 3.9 but note that sktime, one of our dependencies, will not be installed because that library does not yet support python 3.9. This means the PolynomialDetrending component will not be usable in python 3.9. You can try to install sktime from source in python 3.9 to use the PolynomialDetrending component but be warned that we only test it in python 3.7 and
3.8.