Deleting model fits from cache in a simulation setting (where each model is given a different seed)

Describe the problem with the documentation

I am working with STAN models in a simulation study, using PyStan, where I implement the same model multiple times with different values for random_seed. I noticed that after fitting, the fit is saved to my cache folder under httpstan/4.4.2/models/“model_name”/fits/“fit_name”.

The problem I run into is that my memory gets cluttered by these files, while I don’t need them. I have tried clearing the folders containing these files manually, but since I am using parallelization, I cannot just delete entire folders on the go.

Is there a way to delete fit-files after I retrieve the posterior samples that I want, or keep Stan from saving these files?
I tried using the delete_fit-function from httpstan.cache, which requires you to specify an identifier for the (e.g. model_name), which is easy to obtain, and an identifier for the fit (e.g. fit_name), which I am not sure how to obtain (there is a calculate_fit_name-function in httpstan.fits, but I cannot get it to work). The documentation on how to use these functions (calculate_fit_name and delete_fit) is not clear to me.

Suggest a potential alternative/fix

Could you provide a use case on how to delete model fits from cache (in a setting where a new model is fitted within each iteration of a for-loop)?

Start by reading the httpstan.cache delete_fit function and the httpstan.fits calculate_fit_name function mentioned in the issue, using the PyStan cache paths and parallel fitting scenario as context. Document a concrete loop-based use case showing how to identify and delete a fit after retrieving posterior samples, or explain whether fits can be disabled; the documentation should make both identifiers clear.