update: lines 57 and 95 in CircleCI config

.circleci/config.yml

@@ -54,7 +54,7 @@ jobs:
       - run:
           name: Deploy to Railway App (You must set RAILWAY_TOKEN env var)
           command: |
-            cd section-07-ci-and-publishing/house-prices-api && railway up --detach
+            cd section-07-ci-and-publishing/house-prices-api && railway up --detach -s MLDevOps-Beginner -e production
 
   section_07_test_and_upload_regression_model:
     <<: *defaults
@@ -92,7 +92,7 @@ jobs:
       - run:
           name: Build and run Dockerfile (see https://docs.railway.app/deploy/dockerfiles)
           command: |
-            cd section-08-deploying-with-containers && railway up --detach
+            cd section-08-deploying-with-containers && railway up --detach -s MLDevOps-Beginner -e production
 
   test_regression_model_py37:
     docker:

README.md

@@ -2,3 +2,5 @@
 Accompanying repo for the online course Deployment of Machine Learning Models.
 
 For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO).
+
+update: only for testing CI/ CD pipeline

section-05-production-model-package/regression_model/trained_models/train_pipeline.py

@@ -0,0 +1,30 @@
+import numpy as np
+from config.core import config 
+from pipeline import price_pipe
+from processing.data_manager import load_dataset, save_pipeline
+from sklearn.model_selection import train_test_split
+
+def run_training() -> None:
+    """Train the model"""
+
+    # read training data
+    data = load_dataset(file_name=config.app_config.training_data_file)
+
+    # divide train and test
+    X_train, X_test, y_train, y_test = train_test_split(
+        data[config.model_config.features], # predictors
+        data[config.model_config.target],
+        test_size=config.model_config.test_size,
+        # we are setting the random seed here for reproductivity
+        random_state=config.model_config.random_state,
+    )
+    y_train= np.log(y_train)
+
+    # fit model
+    price_pipe.fit(X_train, y_train)
+
+    # persist trained model
+    save_pipeline(pipeline_to_persist=price_pipe)
+
+if __name__ == "__main__":
+    run_training()

section-05-production-model-package/requirements/requirements.txt

@@ -1,7 +1,7 @@
 # We use compatible release functionality (see PEP 440 here: https://www.python.org/dev/peps/pep-0440/#compatible-release)
 # to specify acceptable version ranges of our project dependencies. This gives us the flexibility to keep up with small
 # updates/fixes, whilst ensuring we don't install a major update which could introduce backwards incompatible changes.
-numpy>=1.21.0,<2.0.0
+numpy>=1.21.0,<1.24.9 #2.0.0
 pandas>=1.3.5,<2.0.0
 pydantic>=1.8.1,<2.0.0
 scikit-learn>=1.1.3,<2.0.0

section-07-ci-and-publishing/model-package/regression_model/config/core.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Dict, List, Sequence
+from typing import Dict, List, Optional, Sequence
 
 from pydantic import BaseModel
 from strictyaml import YAML, load
@@ -69,7 +69,7 @@ def find_config_file() -> Path:
     raise Exception(f"Config not found at {CONFIG_FILE_PATH!r}")
 
 
-def fetch_config_from_yaml(cfg_path: Path = None) -> YAML:
+def fetch_config_from_yaml(cfg_path: Optional[Path] = None) -> YAML:
     """Parse YAML containing the package configuration."""
 
     if not cfg_path: