In a real-world scenario, cloud computing and machine learning go hand-in-hand to build, transform and scale predictive modelling projects. See the next section to learn how to correctly set both credentials.Objective: To set up a fully operational machine learning Server on Google Cloud Compute Engine’s virtual machine instance. However, they will help you to create a seamless connection between R and Earth Engine. On the other hand, the credentials dependencies are only needed to move data from Google Drive and Google Cloud Storage to your local environment.

Take into account that the Python Environment you set must have installed the Earth Engine Python API and Numpy. If you are an Rstudio v.1.4 > user, this tutorial will help you to properly set a Python Environment with your R session without rgee::ee_install(). However, the use of rgee::ee_install() is not mandatory you can instead use your own custom installation. Install rgee Python dependencies: Earth Engine Python API and numpy.Īsk to restart the R session in order to see changes. These variables will be used to define the reticulate environmental variable RETICULATE_PYTHON when rgee is loaded. Set the environmental variables EARTHENGINE_PYTHON and EARTHENGINE_ENV. Remove the previous Python environment defined with the same name if it exists. If you do not have a Python environment, it will display an interactive menu to install Miniconda (a free minimal installer for conda).

This function will perform the following six tasks: Library ( rgee ) ee_install (py_env = 'rgee' ) # It is just necessary once!

Google cloud rstudio