I'm trying to run an Azure ML Batch endpoint job, but the job always ends with an error because of the input (see below). I used a model created and trained in the Azure designer as described on the page: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-designer?view=azureml-api-1

Error from directory "logs/azureml/stderrlogs.txt" is like:

TypeError: the JSON object must be str, bytes or bytearray, not MiniBatch
My scoring script (auto-generated for model):
import os
import json
from typing import List
from azureml.studio.core.io.model_directory import ModelDirectory
from pathlib import Path
from azureml.studio.modules.ml.score.score_generic_module.score_generic_module import ScoreModelModule
from azureml.designer.serving.dagengine.converter import create_dfd_from_dict
from collections import defaultdict
from azureml.designer.serving.dagengine.utils import decode_nan
from azureml.studio.common.datatable.data_table import DataTable
model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'trained_model_outputs')
schema_file_path = Path(model_path) / '_schema.json'
with open(schema_file_path) as fp:
    schema_data = json.load(fp)
def init():
    global model
    model = ModelDirectory.load(model_path).model
def run(data):
    data = json.loads(data)
    input_entry = defaultdict(list)
    for row in data:
        for key, val in row.items():
            input_entry[key].append(decode_nan(val))
    data_frame_directory = create_dfd_from_dict(input_entry, schema_data)
    score_module = ScoreModelModule()
    result, = score_module.run(
        learner=model,
        test_data=DataTable.from_dfd(data_frame_directory),
        append_or_result_only=True)
    return json.dumps({"result": result.data_frame.values.tolist()})
definition of input:
input = Input(type=AssetTypes.URI_FILE, path="azureml://subscriptions/$$$$$$$$/resourcegroups/$$$$$$$$$/workspaces/$$$$$/datastores/workspaceblobstore/paths/UI/2023-08-24_193934_UTC/samples.json")
definition of job:
job = ml_client.batch_endpoints.invoke(
   endpoint_name=endpoint.name,
   input=input,
I've read/watched various tutorials/documentation and tried solutions from them, but nothing helped and I've been stuck with this error for several hours, so I'm asking for help.
						
Hello @jestemtym777  
Welcome to Microsoft QnA!
based on the error message you provided, the function is being provided with a MiniBatch
The run function in your scoring script is expecting a JSON string
Please try :
 Modify the Scoring Script to Accept MiniBatch: If the input is always going to be a MiniBatch object, you can modify the scoring script's run function to handle this type of input directly.
 Modify the Input to the Endpoint: Ensure that the input you're providing to the endpoint matches what the run function in your scoring script expects.
I hope this helps!
Kindly mark the answer as Accepted and Upvote in case it helped!
Regards
						
The batch endpoint expects a json file but for some reason Azure adds a hidden file ".amlignore" to the URI_FOLDER where the minibatches were imported from which azure couldn't process and therefore threw errors - see below content of my input folder:
"minibatch": [".amlignore", "samples.json", "samples1.json"]