To install tpot and its core dependencies you can use: conda install -c conda-forge tpot We recommend following PyTorch's installation instructions customized for your operating system and Python distribution.įinally to install TPOT itself, run the following command: pip install tpot TPOT-NN will work with either CPU or GPU PyTorch, but we strongly recommend using a GPU version, if possible, as CPU PyTorch models tend to train very slowly. To enable support for PyTorch-based neural networks (TPOT-NN), you will need to install PyTorch. If you plan to use the TPOT-MDR configuration, make sure to install scikit-mdr and scikit-rebate: pip install scikit-mdr skrebate pip install dask dask dask-ml fsspec>=0.3.3 distributed>=2.10.0 It is noted that dask-ml>=1.7 requires distributed>=2.4.0 and scikit-learn>=0.23.0. If you plan to use Dask for parallel training, make sure to install dask and dask and dask_ml. If you have issues installing XGBoost, check the XGBoost installation documentation. Windows users: pip installation may not work on some Windows environments, and it may cause unexpected errors.

NumPy, SciPy, scikit-learn, pandas, joblib, and PyTorch can be installed in Anaconda via the command: conda install numpy scipy scikit-learn pandas joblib pytorchĭEAP, update_checker, tqdm, stopit and xgboost can be installed with pip via the command: pip install deap update_checker tqdm stopit xgboost You can install TPOT using pip or conda-forge. Support for Python 3.4 and below has been officially dropped since version 0.11.0. Most of the necessary Python packages can be installed via the Anaconda Python distribution, which we strongly recommend that you use. TPOT is built on top of several existing Python libraries, including:

Conda install xgboost .9