Installing the latest release

Installing PyCaret is the first step towards building your first machine learning model in PyCaret. Installation is easy and takes only a few minutes. All hard dependencies are also installed with PyCaret. Click here to see the complete list of hard dependencies.

In order to avoid potential conflicts with other packages, it is strongly recommended to use a virtual environment, e.g. python3 virtualenv (see python3 virtualenv documentation) or conda environments. Using an isolated environment makes it possible to install a specific version of pycaret and its dependencies independently of any previously installed Python packages. See an example below of how to create a conda environment and install PyCaret.

# create a conda environment
conda create --name yourenvname python=3.8

# activate conda environment
conda activate yourenvname

# install pycaret
pip install pycaret

# create notebook kernel connected with the conda environment
python -m ipykernel install --user --name yourenvname --display-name "display-name"

Installing the full version

PyCaret’s default installation is a slim version of pycaret which only installs hard dependencies that are listed here. To install the full version of pycaret, use the following command:

# install the full version of pycaret
pip install pycaret[full]

Installing the nightly build

PyCaret is a fast-evolving machine learning library. Often, you want to have access to the latest features but want to avoid waiting for the next release. In order to do that, you can install the nightly version of PyCaret directly from GitHub.

# install the nightly build
pip install ""

# or install the full version of the nightly build
pip install "[full]"

PyCaret on GPU

PyCaret >= 2.2 provides the option to use GPU for select model training and hyperparameter tuning. There is no change in the use of the API, however, in some cases, additional libraries have to be installed as they are not installed with the default slim version or the full version. The following estimators can be trained on GPU.

  • Extreme Gradient Boosting (requires no further installation)

  • CatBoost (requires no further installation)

  • Light Gradient Boosting Machine (requires GPU installation:

  • Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression (requires cuML >= 0.15

If you are using Google Colab you can install Light Gradient Boosting Machine for GPU but first you have to uninstall LightGBM on CPU. Use the below command to do that:

# uninstall lightgbm CPU
pip uninstall lightgbm -y

# install lightgbm GPU
pip install lightgbm --install-option=--gpu --install-option="--opencl-include-dir=/usr/local/cuda/include/" --install-option="--opencl-library=/usr/local/cuda/lib64/"

CatBoost is only enabled on GPU when dataset has > 50,000 rows.

cuML >= 0.15 cannot be installed on Google Colab. Instead use blazingSQL which comes pre-installed with cuML 0.15. Use following command to install pycaret:

# install pycaret
!/opt/conda-environments/rapids-stable/bin/python -m pip install --upgrade pycaret

Run PyCaret on a Docker Container

A Docker container runs in a virtual environment and is the easiest way to deploy applications using PyCaret. Dockerfile from base image python:3.7 and python:3.7-slim is tested for PyCaret >= 2.0.

FROM python:3.7-slim


ADD . /app

RUN apt-get update && apt-get install -y libgomp1

RUN pip install --trusted-host -r requirements.txt

CMD pytest #replace it with your entry point.