Turned out I then hit another error which may worth @alfarzan investigating and other R zip file submission people that have recently upgraded to xgboost 1.3 and are getting submission errors also trying.
I upgraded my local version of xgboost to 1.3 which for R users was released on CRAN in the last week. Anyhow after the upgrade I’ve not been successful in making submissions from locally trained zip file, even though they pass all the local tests as per the zip instructions (and I’ve been making successful zip based submissions in previous weeks with xgboost 1.2.)
Anyhow after I downgraded my local version of xgboost back to 1.2, retrained, re-ran tests which again passed and then reloaded my submission then worked.
Could it be that AICrowd is loading xgboost 1.2 for R users and the models trained with the new 1.3 version have incompatibilities causing submission errors? In which case guess I need to specify my version of xgboost in install packages.
My latest submissions (e.g. 114780) have encountered a problem with the same error message, which this thread is reffering to: “Inference failed. View the submission for more details.”.
In the log I can find following detailed error message:
“Error in load_model(model_output_path) :
unused argument (model_output_path)”
I really can’t find any solution. Can you please take a look at my submission?
been mostly using the colab colab seems to run fine but can’t seem to get submission working
worked with zip submission after adding in initialising library statements in predict.R
i thought it should automatically initialise packages in the install.R?
In colab any initialisation you need should be done inside of global_imports which is sourced at the top of every file including in predict.R. In zip you are to do that inside of model.R which gets sourced at the top of predict.R.
Can I have your submission ID to make sure things are working fine from our end?
For notebook submissions the only difference would be that the the hard-coded path of the saved model has to be in a directory called saved_objects. Otherwise it should work very similarly with the exception of having to use the install_packages and global_imports functions. h2o.init() would fit into the latter.