how to create score.py file for azure ml

Question

I am new to Azure ML and trying to deploy my model into Azure. My trained model is of classification in which text data is being first processed, then encoded using BERT model and then trained using catBoost. I have already registered my model; however, I am bit confused with the scoring.py script. This is what I using, but not working:

import json
import joblib
import numpy as np
import os

# Called when the service is loaded
def init():
    global model
    # Get the path to the registered model file and load it
    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'nlp_cla.pkl')
    model = joblib.load(model_path)

# Called when a request is received
def run(raw_data):
    # Get the input data as a numpy array
    data = np.array(json.loads(raw_data)['data'])
    # Get a prediction from the model
    predictions = model.predict(data)
    # Return the predictions as any JSON serializable format
    return predictions.tolist()

How I configure my this script so I could deploy on azure?

Answer 1

You can start debugging from the visual studio code as shown here and deployment sample with score.py.

%%writefile source_directory/x/y/score.py
import joblib
import json
import numpy as np
import os

from inference_schema.schema_decorators import input_schema, output_schema
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType

def init():
    global model
    # AZUREML_MODEL_DIR is an environment variable created during deployment. Join this path with the filename of the model file.
    # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)
    # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)
    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')
    # Deserialize the model file back into a sklearn model.
    model = joblib.load(model_path)

    global name
    # Note here, the entire source directory from inference config gets added into image.
    # Below is an example of how you can use any extra files in image.
    with open('./source_directory/extradata.json') as json_file:
        data = json.load(json_file)
        name = data["people"][0]["name"]

input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])
output_sample = np.array([3726.995])

@input_schema('data', NumpyParameterType(input_sample))
@output_schema(NumpyParameterType(output_sample))
def run(data):
    try:
        result = model.predict(data)
        # You can return any JSON-serializable object.
        return "Hello " + name + " here is your result = " + str(result)
    except Exception as e:
        error = str(e)
        return error