Create cml.yaml

.github/workflows/cml.yaml

@@ -0,0 +1,26 @@
+name: mlops-linear-regression
+on: [push]
+jobs:
+  run:
+    runs-on: [ubuntu-latest]
+    container: docker://dvcorg/cml-py3:latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: 'Train my linear model'
+        env:
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          # Your ML workflow goes here
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          python tensorflow_linear_regression_model.py
+
+          echo "## Model Metrics" > report.md
+          cat metrics.txt >> report.md
+
+          echo "\n## Model Performance" >> report.md
+          echo "Model performance metrics are on the plot below." >> report.md
+
+          cml-publish model_results.png --md >> report.md
+
+          cml-send-comment report.md

requirements.txt

@@ -1,3 +1,3 @@
-mlflow
-cloudpickle==2.2.0
-scikit-learn==1.0.2
+tensorflow
+numpy
+matplotlib

tensorflow_linear_regression_model.py

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+# Import modules and packages
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Functions and procedures
+def plot_predictions(train_data, train_labels,  test_data, test_labels,  predictions):
+  """
+  Plots training data, test data and compares predictions.
+  """
+  plt.figure(figsize=(6, 5))
+  # Plot training data in blue
+  plt.scatter(train_data, train_labels, c="b", label="Training data")
+  # Plot test data in green
+  plt.scatter(test_data, test_labels, c="g", label="Testing data")
+  # Plot the predictions in red (predictions were made on the test data)
+  plt.scatter(test_data, predictions, c="r", label="Predictions")
+  # Show the legend
+  plt.legend(shadow='True')
+  # Set grids
+  plt.grid(which='major', c='#cccccc', linestyle='--', alpha=0.5)
+  # Some text
+  plt.title('Model Results', family='Arial', fontsize=14)
+  plt.xlabel('X axis values', family='Arial', fontsize=11)
+  plt.ylabel('Y axis values', family='Arial', fontsize=11)
+  # Show
+  plt.savefig('model_results.png', dpi=120)
+
+
+
+def mae(y_test, y_pred):
+  """
+  Calculuates mean absolute error between y_test and y_preds.
+  """
+  return tf.metrics.mean_absolute_error(y_test, y_pred)
+
+
+def mse(y_test, y_pred):
+  """
+  Calculates mean squared error between y_test and y_preds.
+  """
+  return tf.metrics.mean_squared_error(y_test, y_pred)
+
+
+# Check Tensorflow version
+print(tf.__version__)
+
+
+# Create features
+X = np.arange(-100, 100, 4)
+
+# Create labels
+y = np.arange(-90, 110, 4)
+
+
+# Split data into train and test sets
+X_train = X[:40] # first 40 examples (80% of data)
+y_train = y[:40]
+
+X_test = X[40:] # last 10 examples (20% of data)
+y_test = y[40:]
+
+
+# Take a single example of X
+input_shape = X[0].shape 
+
+# Take a single example of y
+output_shape = y[0].shape
+
+
+# Set random seed
+tf.random.set_seed(42)
+
+# Create a model using the Sequential API
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(1), 
+    tf.keras.layers.Dense(1)
+    ])
+
+# Compile the model
+model.compile(loss = tf.keras.losses.mae,
+              optimizer = tf.keras.optimizers.SGD(),
+              metrics = ['mae'])
+
+# Fit the model
+model.fit(X_train, y_train, epochs=100)
+
+
+# Make and plot predictions for model_1
+y_preds = model.predict(X_test)
+plot_predictions(train_data=X_train, train_labels=y_train,  test_data=X_test, test_labels=y_test,  predictions=y_preds)
+
+
+# Calculate model_1 metrics
+mae_1 = np.round(float(mae(y_test, y_preds.squeeze()).numpy()), 2)
+mse_1 = np.round(float(mse(y_test, y_preds.squeeze()).numpy()), 2)
+print(f'\nMean Absolute Error = {mae_1}, Mean Squared Error = {mse_1}.')
+
+# Write metrics to file
+with open('metrics.txt', 'w') as outfile:
+    outfile.write(f'\nMean Absolute Error = {mae_1}, Mean Squared Error = {mse_1}.')