changed model

country_vaccination/MLmodel

@@ -0,0 +1,20 @@
+flavors:
+  keras:
+    data: data
+    keras_module: tensorflow.keras
+    keras_version: 2.5.0
+    save_format: tf
+  python_function:
+    data: data
+    env: conda.yaml
+    loader_module: mlflow.keras
+    python_version: 3.9.4
+saved_input_example_info:
+  artifact_path: input_example.json
+  format: tf-serving
+  type: ndarray
+signature:
+  inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 8]}}]'
+  outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float32", "shape": [-1,
+    1]}}]'
+utc_time_created: '2021-05-23 10:44:42.850073'

country_vaccination/conda.yaml

@@ -0,0 +1,10 @@
+channels:
+- defaults
+- conda-forge
+dependencies:
+- python=3.9.4
+- pip
+- pip:
+  - mlflow
+  - tensorflow==2.5.0-rc1
+name: mlflow-env

country_vaccination/data/keras_module.txt

@@ -0,0 +1 @@
+tensorflow.keras

country_vaccination/data/save_format.txt

@@ -0,0 +1 @@
+tf

country_vaccination/input_example.json

@@ -0,0 +1 @@
+{"inputs": [503810.0, 385506.0, 118304.0, 31121.0, 307.01, 234.94, 72.08000000000001, 158695.0]}

mlflow_model.py

@@ -1,9 +1,9 @@
 import sys
 import pandas as pd
+import mlflow as mlf
 from tensorflow import keras 
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import mean_squared_error as rmse
-import mlflow as mlf
 
 
 def create_model(test_size, epochs, batch_size):
@@ -28,28 +28,28 @@ def create_model(test_size, epochs, batch_size):
 
     model.fit(X_train, y_train, epochs = epochs, validation_split = 0.3, batch_size = batch_size)
 
+    signature = mlf.models.signature.infer_signature(X_train.values, model.predict(X_train.values))
+    input_example = X_test.values[10]
     prediction = model.predict(X_test)
     rmse_result = rmse(y_test, prediction, squared = False)
-    print(prediction)
     model.save('vaccines_model')
-    return model, rmse_result
+    return model, rmse_result, signature, input_example
 
 if __name__ == "__main__":
     test_size = float(sys.argv[1]) if len(sys.argv) > 1 else 0.2
     epochs = int(sys.argv[2]) if len(sys.argv) > 1 else 100
     batch_size = int(sys.argv[3]) if len(sys.argv) > 1 else 32
-
     with mlf.start_run():
         mlf.log_param("Test size", test_size)
         mlf.log_param("Epochs", epochs)
         mlf.log_param("Batch size", batch_size)
 
-        model, rmse_result = create_model(
+        model, rmse_result, signature, input_example = create_model(
             test_size=test_size,
             epochs=epochs,
             batch_size=batch_size,
         )
 
         mlf.log_metric("RMSE", rmse_result)
-
-        mlf.keras.log_model(model, "country_vaccination")
+        # mlf.keras.log_model(model, "country_vaccination")
+        mlf.keras.save_model(model, "country_vaccination", input_example=input_example, signature=signature)