MLFlow logging

jenkins/training.Jenkinsfile

@@ -36,6 +36,7 @@ pipeline {
             steps {
                 sh "chmod +x -R ${env.WORKSPACE}"
                 sh 'python3 scripts/sacred_train.py -e $epochs -s $step $save_model'
+                sh 'python3 scripts/mlflow_train.py -e $epochs -s $step $save_model'
             }
         }
         stage('Archive artifacts') {

scripts/mlflow_train.py

@@ -0,0 +1,211 @@
+from urllib.parse import urlparse
+import mlflow
+import mlflow.pytorch as model_logger
+
+import argparse
+import pandas as pd
+import numpy as np
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+import torch
+from torch import nn
+from torch.utils import data as t_u_data
+
+
+mlflow.set_tracking_uri("http://localhost:5000")
+mlflow.set_experiment("s478841")
+
+
+# * Customized Dataset class (base provided by PyTorch)
+class AvocadoDataset(t_u_data.Dataset):
+    def __init__(self, path: str, target: str = 'AveragePrice'):
+        data = pd.read_csv(path)
+        y = data[target].values.astype('float32')
+        self.y = y.reshape((len(y), 1))
+        self.x_data = data.drop(
+            [target], axis=1).values.astype('float32')
+        self.x_shape = data.drop([target], axis=1).shape
+        # print("Data shape is: ", self.x_data.shape)
+
+    def __len__(self):
+        return len(self.x_data)
+
+    def __getitem__(self, idx):
+        return [self.x_data[idx], self.y[idx]]
+
+    def get_shape(self):
+        return self.x_shape
+
+    def get_splits(self, n_test=0.33):
+        test_size = round(n_test * len(self.x_data))
+        train_size = len(self.x_data) - test_size
+        return t_u_data.random_split(self, [train_size, test_size])
+
+
+class AvocadoRegressor(nn.Module):
+    def __init__(self, input_dim):
+        super(AvocadoRegressor, self).__init__()
+        self.hidden1 = nn.Linear(input_dim, 32)
+        nn.init.xavier_uniform_(self.hidden1.weight)
+        self.act1 = nn.ReLU()
+        self.hidden2 = nn.Linear(32, 8)
+        nn.init.xavier_uniform_(self.hidden2.weight)
+        self.act2 = nn.ReLU()
+        self.hidden3 = nn.Linear(8, 1)
+        nn.init.xavier_uniform_(self.hidden3.weight)
+
+    def forward(self, x):
+        x = self.hidden1(x)
+        x = self.act1(x)
+        x = self.hidden2(x)
+        x = self.act2(x)
+        x = self.hidden3(x)
+        return x
+
+
+def prepare_data(paths):
+    train_dl = t_u_data.DataLoader(AvocadoDataset(
+        paths[0]), batch_size=32, shuffle=True)
+    validate_dl = t_u_data.DataLoader(AvocadoDataset(
+        paths[1]), batch_size=128, shuffle=True)
+    test_dl = t_u_data.DataLoader(AvocadoDataset(
+        paths[2]), batch_size=1, shuffle=False)
+    return train_dl, validate_dl, test_dl
+
+
+def train_model(train_dl, model, epochs, log_step):
+    criterion = nn.MSELoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
+    to_compare = None
+    metrics = None
+    for epoch in range(1, epochs+1):
+        for _, (inputs, targets) in enumerate(train_dl):
+            optimizer.zero_grad()
+            yhat = model(inputs)
+            # * For loss value inspection
+            to_compare = (yhat, targets)
+            loss = criterion(yhat, targets)
+            loss.backward()
+            optimizer.step()
+        if epoch == 1 or (epoch) % log_step == 0:
+            result, target = to_compare[0].detach(
+            ).numpy(), to_compare[1].detach().numpy()
+            metrics = {'train.mse': mean_squared_error(target, result),
+                       'train.mae': mean_absolute_error(target, result),
+                       'train.rmse': mean_squared_error(target, result, squared=False)}
+            # _run.log_scalar("training.RMSE", np.sqrt(mse), epoch)
+            # _run.log_scalar("training.MAE", mae, epoch)
+            # _run.log_scalar('training.MSE', mse, epoch)
+            print(
+                f"Epoch {epoch}\t→\tMSE: {metrics['train.mse']},\tRMSE: {metrics['train.rmse']},\tMAE: {metrics['train.mae']}")
+    return metrics
+
+
+def evaluate_model(test_dl, model):
+    predictions, actuals = list(), list()
+    for _, (inputs, targets) in enumerate(test_dl):
+        yhat = model(inputs)
+        # * retrieve numpy array
+        yhat = yhat.detach().numpy()
+        actual = targets.numpy()
+        actual = actual.reshape((len(actual), 1))
+        # * store predictions
+        predictions.append(yhat)
+        actuals.append(actual)
+    predictions, actuals = np.vstack(predictions), np.vstack(actuals)
+    # * return MSE value
+    mse = mean_squared_error(actuals, predictions)
+    rmse = mean_squared_error(actuals, predictions, squared=False)
+    mae = mean_absolute_error(actuals, predictions)
+    return mse, rmse, mae
+
+
+def predict(row, model):
+    row = row[0].flatten()
+    yhat = model(row)
+    yhat = yhat.detach().numpy()
+    return yhat
+
+
+def main(epochs, save_model, log_step):
+    print(
+        f"Your model will be trained for {epochs} epochs, logging every {log_step} steps. Trained model will {'not ' if save_model else ''}be saved.")
+
+    # * Paths to data
+    avocado_data = ['./data/avocado.data.train',
+                    './data/avocado.data.valid',
+                    './data/avocado.data.test']
+
+    # * Data preparation
+    train_dl, validate_dl, test_dl = prepare_data(paths=avocado_data)
+    print(f"""
+          Train set size: {len(train_dl.dataset)},
+          Validate set size: {len(validate_dl.dataset)}
+          Test set size: {len(test_dl.dataset)}
+          """)
+
+    # * Model definition
+    # ! 66 - in case only regions and type are used (among all the categorical vals)
+    model = AvocadoRegressor(235)
+
+    # * Train model
+    print("Let's start the training, mate!")
+    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))
+        metrics = train_model(train_dl=train_dl, model=model,
+                              epochs=epochs, log_step=log_step)
+        mlflow.log_param('epochs', epochs)
+        mlflow.log_metrics(metrics)
+
+        # * Evaluate model
+        val_metrics = {key: val for key, val in zip(
+            ['validate.mse', 'validate.rmse', 'validate.mae'], evaluate_model(validate_dl, model))}
+        print(
+            f"\nEvaluation on VALIDATION set\t→\tMSE: {val_metrics['validate.mse']}, RMSE: {val_metrics['validate.rmse']}, MAE: {val_metrics['validate.mae']}")
+        mlflow.log_metrics(val_metrics)
+
+        test_loss = {key: val for key, val in zip(
+            ['test.mse', 'test.rmse', 'test.mae'], evaluate_model(test_dl, model))}
+        print(
+            f"\nEvaluation on TEST set\t→\tMSE: {test_loss['test.mse']}, RMSE: {test_loss['test.rmse']}, MAE: {test_loss['test.mae']}")
+        mlflow.log_metrics(test_loss)
+
+        # tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
+
+        # if tracking_url_type_store != 'file':
+        #     print('First option')
+        #     model_logger.log_model(
+        #         model, "avocados-model", registered_model_name="AvocadoModel_478841")
+        # else:
+        #     print('Second option')
+        #     model_logger.log_model(model, "model")
+
+
+    # * Save the trained model
+    if save_model:
+        print("Your model has been saved - have a nice day!")
+        scripted_model = torch.jit.script(model)
+        scripted_model.save('./data/model_scripted.pt')
+        # ex.add_artifact('./data/model_scripted.pt')
+
+
+# ex.run()
+if __name__ == '__main__':
+    # * Model parameters
+    parser = argparse.ArgumentParser(description="Script performing logistic regression model training",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        "-e", "--epochs", default=100, help="Number of epochs the model will be trained for")
+    parser.add_argument(
+        "-s", "--step", default=10, help="Number of steps to repeat logging loss values on")
+    parser.add_argument("--save", action="store_true",
+                        help="Save trained model to file 'trained_model.h5'")
+
+    args = vars(parser.parse_args())
+
+    epochs = int(args['epochs'])
+    save_model = args['save']
+    log_step = int(args['step'])
+
+    main(epochs, save_model, log_step)

scripts/sacred_train.py

@@ -1,7 +1,3 @@
-from audioop import rms
-from cgi import test
-from multiprocessing.spawn import prepare
-from xml.etree.ElementPath import prepare_star
 from sacred import Experiment
 from sacred.observers import FileStorageObserver, MongoObserver