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conda_env_v_1_0_0.yml

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+# Conda environment specification. The dependencies defined in this file will
+# be automatically provisioned for runs with userManagedDependencies=False.
+
+# Details about the Conda environment file format:
+# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually
+
+name: project_environment
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.8 and later.
+- python=3.9.19
+
+- pip:
+  - azureml-train-automl-runtime==1.57.0
+  - inference-schema
+  - xgboost<=1.5.2
+  - azureml-interpret==1.57.0
+  - azureml-defaults==1.57.0
+- numpy==1.23.5
+- pandas==1.3.5
+- scikit-learn==1.5.1
+- prophet==1.1.4
+- holidays==0.54
+- psutil==5.9.3
+channels:
+- anaconda
+- conda-forge

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e9a93cc6f8d9cdbfa6304f0579c8b323a51c9be06c69620bd3ea1bca5b8faa1
+size 268686

scoring_file_v_2_0_0.py

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+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+import json
+import logging
+import os
+import pickle
+import numpy as np
+import pandas as pd
+import joblib
+
+import azureml.automl.core
+from azureml.automl.core.shared import logging_utilities, log_server
+from azureml.telemetry import INSTRUMENTATION_KEY
+
+from inference_schema.schema_decorators import input_schema, output_schema
+from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
+from inference_schema.parameter_types.pandas_parameter_type import PandasParameterType
+from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType
+
+data_sample = PandasParameterType(pd.DataFrame({"day": pd.Series([0], dtype="int8"), "mnth": pd.Series([0], dtype="int8"), "year": pd.Series([0], dtype="int16"), "season": pd.Series([0], dtype="int8"), "holiday": pd.Series([False], dtype="bool"), "weekday": pd.Series([0], dtype="int8"), "workingday": pd.Series([False], dtype="bool"), "weathersit": pd.Series([0], dtype="int8"), "temp": pd.Series([0.0], dtype="float32"), "atemp": pd.Series([0.0], dtype="float32"), "hum": pd.Series([0.0], dtype="float32"), "windspeed": pd.Series([0.0], dtype="float32")}))
+input_sample = StandardPythonParameterType({'data': data_sample})
+
+result_sample = NumpyParameterType(np.array([0]))
+output_sample = StandardPythonParameterType({'Results':result_sample})
+sample_global_parameters = StandardPythonParameterType(1.0)
+
+try:
+    log_server.enable_telemetry(INSTRUMENTATION_KEY)
+    log_server.set_verbosity('INFO')
+    logger = logging.getLogger('azureml.automl.core.scoring_script_v2')
+except:
+    pass
+
+
+def init():
+    global model
+    # This name is model.id of model that we want to deploy deserialize the model file back
+    # into a sklearn model
+    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.pkl')
+    path = os.path.normpath(model_path)
+    path_split = path.split(os.sep)
+    log_server.update_custom_dimensions({'model_name': path_split[-3], 'model_version': path_split[-2]})
+    try:
+        logger.info("Loading model from path.")
+        model = joblib.load(model_path)
+        logger.info("Loading successful.")
+    except Exception as e:
+        logging_utilities.log_traceback(e, logger)
+        raise
+
+@input_schema('Inputs', input_sample)
+@input_schema('GlobalParameters', sample_global_parameters, convert_to_provided_type=False)
+@output_schema(output_sample)
+def run(Inputs, GlobalParameters=1.0):
+    data = Inputs['data']
+    result = model.predict(data)
+    return {'Results':result.tolist()}