  We present MOGPTK, a Python package for multi-channel data modelling using
Gaussian processes (GP). The aim of this toolkit is to make multi-output GP
(MOGP) models accessible to researchers, data scientists, and practitioners
alike. MOGPTK uses a Python front-end, relies on the GPflow suite and is built
on a TensorFlow back-end, thus enabling GPU-accelerated training. The toolkit
facilitates implementing the entire pipeline of GP modelling, including data
loading, parameter initialization, model learning, parameter interpretation, up
to data imputation and extrapolation. MOGPTK implements the main multi-output
covariance kernels from literature, as well as spectral-based parameter
initialization strategies. The source code, tutorials and examples in the form
of Jupyter notebooks, together with the API documentation, can be found at
http://github.com/GAMES-UChile/mogptk
