IUM_08

mlflow_project/MLproject

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+name: MLflow_s464979
+
+conda_env: conda.yaml
+
+entry_points:
+  optimal_parameters:
+    parameters:
+      epochs: { type: int, default: 20 }
+      batch_size: { type: int, default: 32 }
+    command: 'python mlflow_training_model.py {epochs} {batch_size}'

mlflow_project/conda.yaml

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+name: MLflow_s464979
+channels:
+  - defaults
+dependencies:
+  - python=3.10
+  - pip
+  - pip:
+      - mlflow
+      - tensorflow
+      - pandas
+      - scikit-learn

mlflow_project/mlflow_training_model.py

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+import pandas as pd
+import tensorflow as tf
+import sys
+import mlflow
+from sklearn.metrics import accuracy_score
+
+mlflow.set_tracking_uri("http://localhost:5000")
+
+def main():
+    train_data = pd.read_csv('./beer_reviews_train.csv')
+    X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
+    y_train = train_data['review_overall']
+
+    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
+    tokenizer.fit_on_texts(X_train)
+    X_train_seq = tokenizer.texts_to_sequences(X_train)
+
+    X_train_pad = tf.keras.preprocessing.sequence.pad_sequences(X_train_seq, maxlen=100)
+
+    with mlflow.start_run() as run:
+        print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
+        print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
+        model = tf.keras.Sequential([
+            tf.keras.layers.Embedding(input_dim=10000, output_dim=16, input_length=100),
+            tf.keras.layers.GlobalAveragePooling1D(),
+            tf.keras.layers.Dense(16, activation='relu'),
+            tf.keras.layers.Dense(1, activation='sigmoid')
+        ])
+
+        model.compile(optimizer='adam',
+                      loss='binary_crossentropy',
+                      metrics=['accuracy'])
+
+        print(sys.argv[1])
+        print(sys.argv[2])
+        model.fit(X_train_pad, y_train, epochs=int(sys.argv[1]), batch_size=int(sys.argv[2]), validation_split=0.1)
+
+        mlflow.log_param("epochs", int(sys.argv[1]))
+        mlflow.log_param("batch_size", int(sys.argv[2]))
+
+        test_data = pd.read_csv('./beer_reviews_test.csv')
+        X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
+        y_test = test_data['review_overall']
+
+        predictions = model.predict(X_test).flatten()
+
+        y_test_binary = (y_test >= 3).astype(int)
+
+        accuracy = accuracy_score(y_test_binary, predictions.round())
+        mlflow.log_metric("accuracy", accuracy)
+
+if __name__ == '__main__':
+    main()