update service

.gitignore

@@ -1,2 +1,2 @@
 .DS_Store
-fortune_predict_model
+model

README.md

@@ -27,6 +27,7 @@ Try to use [MLflow](https://mlflow.org) platform to log PyTorch model training,
   * a sample code to call registered model to predict testing data and save model to local file system
 * **get_registered_model_via_rest_api.py**
   * a script to test MLflow REST api
-
+* **log_unsupported_model.py**
+  * a sample script to apply mlflow.pyfunc to package unsupported ml model which can be logged and registered by mlflow
 
 ###### tags: `MLOps`

log_unsupported_model.py

@@ -0,0 +1,77 @@
+# log_unsupported_model.py
+#
+# author: deng
+# date  : 20230309
+
+import mlflow
+import pandas as pd
+
+
+class CustomModel(mlflow.pyfunc.PythonModel):
+    """ A mlflow wrapper to package unsupported model """
+
+    def __init__(self):
+        self.model = None
+
+    def load_model(self):
+        # load your custom model here
+        self.model = lambda value: value * 2
+
+    def predict(self,
+                model_input: pd.DataFrame) -> pd.DataFrame:
+
+        if self.model is None:
+            self.load_model()
+
+        output = model_input.apply(self.model)
+
+        return output
+
+
+def log_model(server_uri:str,
+              exp_name: str,
+              registered_model_name: str) -> None:
+
+    # init mlflow
+    mlflow.set_tracking_uri(server_uri)
+    mlflow.set_experiment(exp_name)
+
+    # register custom model
+    model = CustomModel()
+    mlflow.pyfunc.log_model(
+        artifact_path='model',
+        python_model=model,
+        registered_model_name=registered_model_name
+    )
+
+
+def pull_model(server_uri: str,
+               exp_name: str,
+               registered_model_name: str) -> None:
+
+    # init mlflow
+    mlflow.set_tracking_uri(server_uri)
+    mlflow.set_experiment(exp_name)
+
+    # pull model from registry
+    model = mlflow.pyfunc.load_model(f'models:/{registered_model_name}/latest')
+    model = model.unwrap_python_model()  # get CustomModel object
+    print(f'Model loaded. model type: {type(model)}')
+
+    # test model availability
+    fake_data = pd.DataFrame([1, 3, 5])
+    output = model.predict(fake_data)
+    print(f'input data: {fake_data}, predictions: {output}')
+
+    # save it to local file system
+    mlflow.pyfunc.save_model(path='./model', python_model=model)
+
+
+if __name__ == '__main__':
+
+    server_uri = 'http://127.0.0.1:5001'
+    exp_name = 'custom_model'
+    registered_model_name = 'custom_model'
+
+    log_model(server_uri, exp_name, registered_model_name)
+    pull_model(server_uri, exp_name, registered_model_name)

predict.py

@@ -7,7 +7,7 @@ import torch
 import mlflow
 
 
-if __name__ == '__main__':
+def main():
 
     # set MLflow server
     mlflow.set_tracking_uri('http://127.0.0.1:5001')
@@ -21,4 +21,8 @@ if __name__ == '__main__':
     print(my_fortune)
 
     # save model and env to local file system
-    mlflow.pytorch.save_model(model, './fortune_predict_model')
+    mlflow.pytorch.save_model(model, './model')
+
+
+if __name__ == '__main__':
+    main()