Is it possible to train Core without specifying model?

Rasa Open Source
3

Hi @Tanja,

Okay. i am currently building a service which would request model/train API to train the current data.So before training the Model i would like to store the cross validation score for my training data

cross_validate(data = training_data, n_folds = fold, nlu_config = config_path)

Which i was able to achieve.Now my next task is to test the core accuracy for which we need model path (Sorry i made that silly mistake :stuck_out_tongue: of asking the above question )

So coming to my question the /train API would return header + byte object i was able to extract out the filename thanks to HTTP API: how to load recently trained model - #5 by Anand_Menon

I have used to gzip library to convert the result returned by the API

    temp_dir = tempfile.mkdtemp()
    model_path = os.path.join(temp_dir,f"{result_obj.headers['filename']}")
    with gzip.open(model_path,'wb') as _zip:
         _zip.write(result_obj.body)

I dont know if the above implementation is correct since i am not able to load this tar.gz file or test the stories using it. It would give me the below error

raise ReadError("file could not be opened successfully")
tarfile.ReadError: file could not be opened successfully

So my question is how can i convert the byte object returned by the train API back to tar.gz file? An implementation of how to convert byte to .tar.gz would be really helpful

I hope it is clear