Delete iris-training-pipeline.py

iris-training-pipeline.py

@@ -1,100 +0,0 @@
-import os
-import modal
-
-LOCAL=True
-
-if LOCAL == False:
-   stub = modal.Stub()
-   image = modal.Image.debian_slim().apt_install(["libgomp1"]).pip_install(["hopsworks", "seaborn", "joblib", "scikit-learn"])
-
-   @stub.function(image=image, schedule=modal.Period(days=1), secret=modal.Secret.from_name("jim-hopsworks-ai"))
-   def f():
-       g()
-
-
-def g():
-    import hopsworks
-    import pandas as pd
-    from sklearn.neighbors import KNeighborsClassifier
-    from sklearn.metrics import accuracy_score
-    from sklearn.metrics import confusion_matrix
-    from sklearn.metrics import classification_report
-    import seaborn as sns
-    from matplotlib import pyplot
-    from hsml.schema import Schema
-    from hsml.model_schema import ModelSchema
-    import joblib
-
-    # You have to set the environment variable 'HOPSWORKS_API_KEY' for login to succeed
-    project = hopsworks.login()
-    # fs is a reference to the Hopsworks Feature Store
-    fs = project.get_feature_store()
-
-    # The feature view is the input set of features for your model. The features can come from different feature groups.    
-    # You can select features from different feature groups and join them together to create a feature view
-    try: 
-        feature_view = fs.get_feature_view(name="iris_modal", version=1)
-    except:
-        iris_fg = fs.get_feature_group(name="iris_modal", version=1)
-        query = iris_fg.select_all()
-        feature_view = fs.create_feature_view(name="iris_modal",
-                                          version=1,
-                                          description="Read from Iris flower dataset",
-                                          labels=["variety"],
-                                          query=query)    
-
-    # You can read training data, randomly split into train/test sets of features (X) and labels (y)        
-    X_train, X_test, y_train, y_test = feature_view.train_test_split(0.2)
-
-    # Train our model with the Scikit-learn K-nearest-neighbors algorithm using our features (X_train) and labels (y_train)
-    model = KNeighborsClassifier(n_neighbors=2)
-    model.fit(X_train, y_train.values.ravel())
-
-    # Evaluate model performance using the features from the test set (X_test)
-    y_pred = model.predict(X_test)
-
-    # Compare predictions (y_pred) with the labels in the test set (y_test)
-    metrics = classification_report(y_test, y_pred, output_dict=True)
-    results = confusion_matrix(y_test, y_pred)
-
-    # Create the confusion matrix as a figure, we will later store it as a PNG image file
-    df_cm = pd.DataFrame(results, ['True Setosa', 'True Versicolor', 'True Virginica'],
-                         ['Pred Setosa', 'Pred Versicolor', 'Pred Virginica'])
-    cm = sns.heatmap(df_cm, annot=True)
-    fig = cm.get_figure()
-
-    # We will now upload our model to the Hopsworks Model Registry. First get an object for the model registry.
-    mr = project.get_model_registry()
-    
-    # The contents of the 'iris_model' directory will be saved to the model registry. Create the dir, first.
-    model_dir="iris_model"
-    if os.path.isdir(model_dir) == False:
-        os.mkdir(model_dir)
-
-    # Save both our model and the confusion matrix to 'model_dir', whose contents will be uploaded to the model registry
-    joblib.dump(model, model_dir + "/iris_model.pkl")
-    fig.savefig(model_dir + "/confusion_matrix.png")    
-
-
-    # Specify the schema of the model's input/output using the features (X_train) and labels (y_train)
-    input_schema = Schema(X_train)
-    output_schema = Schema(y_train)
-    model_schema = ModelSchema(input_schema, output_schema)
-
-    # Create an entry in the model registry that includes the model's name, desc, metrics
-    iris_model = mr.python.create_model(
-        name="iris_modal", 
-        metrics={"accuracy" : metrics['accuracy']},
-        model_schema=model_schema,
-        description="Iris Flower Predictor"
-    )
-    
-    # Upload the model to the model registry, including all files in 'model_dir'
-    iris_model.save(model_dir)
-    
-if __name__ == "__main__":
-    if LOCAL == True :
-        g()
-    else:
-        with stub.run():
-            f()