![]() ![]() use_python("/usr/local/bin/python") Creating a virtual environment (Optional) Then you need to define the location of your python environment which is to be used. ![]() Because we faced difficulties with various ambiguous errors as our R versions were not updated.įirst you need to install and load the reticulate package. Note: However to avoid any difficulties beforehand ensure that your R version is up to date. R has the ‘Reticulate’ package which is a life saver. ![]() Once you have the relevant python object or model exported, next step would be to call it in R. However it is clearly stated that this is not safe, and it is advised to unpickle data which you are sure of (using either pickle or joblib). In some cases it would be unsupported and you would get various errors until you have the same environment (python versions including scikit) in dump and load. Note: If you are trying to save and load models and other objects using either of the mechanisms, it is always advisable to have the same environment in both scenarios. This article provides you examples utilizing both those packages if you want a visual perspective. This allows to save computations and reproduce on various datasets. It keeps the underlying algorithm unchanged.
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