begin boto3 chagne

model_data/simulation_system/Dockerfiles/Dockerfile.prediction

@@ -34,4 +34,4 @@ USER ${USER}
 WORKDIR /home/simulation_system
 
 # Run the python command
-CMD ["python3", "predictions.py", "--data-path", "./model_build_data/change_data/rdsap_full/test_data.parquet"]
+CMD ["python3", "predictions.py", "--data-path", "s3://retrofit-data-dev/model_build_data/change_data/rdsap_full/test_data_with_id.parquet"]

model_data/simulation_system/Dockerfiles/Dockerfile.training

@@ -34,4 +34,4 @@ USER ${USER}
 WORKDIR /home/simulation_system
 
 # Run the python command
-CMD ["python3", "training.py", "--train-filepath", "./model_build_data/change_data/rdsap_full/train_validation_data.parquet", "--test-filepath", "./model_build_data/change_data/rdsap_full/test_data.parquet"]
+CMD ["python3", "training.py", "--train-filepath", "s3://retrofit-data-dev/model_build_data/change_data/rdsap_full/train_validation_data.parquet", "--test-filepath", "s3://retrofit-data-dev/model_build_data/change_data/rdsap_full/test_data.parquet"]

model_data/simulation_system/core/CloudClient.py

@@ -4,6 +4,7 @@ Set up the client to be used for downloading and uploading model files
 
 import os
 import s3fs
+import boto3
 from core.Logger import logger
 
 
@@ -86,10 +87,68 @@ class S3FSClient:
                 filename = file.split(filepath)[-1]
 
                 # Define the local path where you want to save the file
-                local_path = os.path.join("local_model", filename)
+                local_path = os.path.join(model_folder, filename)
 
                 # Download the file from S3 to the local directory
                 self.client.get(file, local_path)
                 print(f"Downloaded {filename} to {local_path}")
 
             print("Download completed.")
+
+
+class BotoClient:
+    """
+    Using boto3 to access the different aws storage configurations
+    """
+
+    def __init__(self, runtime_environment: str = "local") -> None:
+        self.client = None
+        self.model_bucket: str
+
+        self.client_factory(runtime_environment)
+        self.determine_model_bucket(runtime_environment)
+
+    def client_factory(self, runtime_environment: str = "local"):
+        """
+        Select the correct s3 client to use
+        """
+
+        if runtime_environment == "local":
+            logger.info("No S3 client setup required")
+        elif runtime_environment == "local-mock":
+            logger.info(f"S3 settings for {runtime_environment}")
+            session = boto3.Session()
+            self.client = session.client(
+                service_name="s3",
+                aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID", "admin"),
+                aws_secret_access_key=os.environ.get(
+                    "AWS_SECRET_ACCESS_KEY", "password"
+                ),
+                endpoint_url=os.environ.get("ENDPOINT_URL", "http://localhost:9000"),
+            )
+        elif runtime_environment in ["dev", "staging", "prod"]:
+            logger.info(f"S3 settings for {runtime_environment}")
+            # Key/ token should be in session/lambda for this
+            self.client = boto3.client("s3")
+        else:
+            raise NotImplementedError("No correspnding runtime environment")
+
+    def determine_model_bucket(self, runtime_environment: str) -> None:
+        """
+        For the given environment, return the correct bucket for models
+        """
+        if runtime_environment == "local":
+            logger.info("In local development - no need for s3")
+        elif runtime_environment in ["local-mock", "dev"]:
+            # TODO: get from enironment
+            self.model_bucket = "retrofit-model-directory-dev"
+        elif runtime_environment in ["staging", "prod"]:
+            self.model_bucket = f"retrofit-model-directory-{runtime_environment}"
+        else:
+            raise NotImplementedError("No corresponding runtime environment")
+
+    def download_model(self, filepath: str, model_folder: str):
+        """
+        For the file path, download the model locally so that we can load the model
+        """
+        pass

model_data/simulation_system/core/DataLoader.py

@@ -3,6 +3,7 @@ import os
 from typing import Protocol
 import boto3
 from io import BytesIO, StringIO
+from core.CloudClient import BotoClient
 
 
 def read_parquet_from_s3(bucket_name, file_key):
@@ -57,7 +58,9 @@ class DataLoader(Protocol):
     """
 
     @staticmethod
-    def load(filepath: str, index_col: str | None = None) -> pd.DataFrame | None:
+    def load(
+        client: BotoClient, filepath: str, index_col: str | None = None
+    ) -> pd.DataFrame | None:
         """
         Loading data from the relevant source
         """
@@ -92,7 +95,9 @@ class S3MockDataLoader:
     """
 
     @staticmethod
-    def load(filepath: str, index_col: str | None = None) -> pd.DataFrame:
+    def load(
+        client: BotoClient, filepath: str, index_col: str | None = None
+    ) -> pd.DataFrame:
 
         # TODO: Ingest these as environment variables in the docker compose file
         storage_options = {
@@ -126,7 +131,9 @@ class S3DataLoader:
     """
 
     @staticmethod
-    def load(filepath: str, index_col: str | None = None) -> pd.DataFrame:
+    def load(
+        client: BotoClient, filepath: str, index_col: str | None = None
+    ) -> pd.DataFrame:
 
         filepath_split = filepath.split("s3://")[-1].split("/", 1)
         bucket = filepath_split[0]

model_data/simulation_system/docker-compose.yml

@@ -24,6 +24,7 @@ services:
   #     dockerfile: ./Dockerfiles/Dockerfile.training
   #   image: simulation_system_training
   #   environment:
+  #     RUNTIME_ENVIRONMENT: local-mock
   #     ENDPOINT_URL: http://minio:9000/
   #     AWS_ACCESS_KEY_ID: *MINIO_USER
   #     AWS_SECRET_ACCESS_KEY: *MINIO_PASS
@@ -34,19 +35,19 @@ services:
     # command:
     #   ["bash"]
 
-  # simulation_system_prediction:
-  #   build:
-  #     context: ./
-  #     dockerfile: ./Dockerfiles/Dockerfile.prediction
-  #   image: simulation_system_prediction
-  #   environment:
-  #     ENDPOINT_URL: http://minio:9000/
-  #     AWS_ACCESS_KEY_ID: *MINIO_USER
-  #     AWS_SECRET_ACCESS_KEY: *MINIO_PASS
-  #   tty: true
-  #   depends_on:
-  #     simulation_system_training:
-  #       condition: service_completed_successfully
+  simulation_system_prediction:
+    build:
+      context: ./
+      dockerfile: ./Dockerfiles/Dockerfile.prediction
+    image: simulation_system_prediction
+    environment:
+      ENDPOINT_URL: http://minio:9000/
+      AWS_ACCESS_KEY_ID: *MINIO_USER
+      AWS_SECRET_ACCESS_KEY: *MINIO_PASS
+    tty: true
+    # depends_on:
+    #   simulation_system_training:
+    #     condition: service_completed_successfully
   #   command:
   #     ["bash"]
 

model_data/simulation_system/predictions.py

@@ -5,6 +5,7 @@ Script to load MLModel class and generate predictions
 import os
 import json
 import argparse
+from pathlib import Path
 import pandas as pd
 from typing import Optional
 from datetime import datetime
@@ -12,6 +13,8 @@ from MLModel.Models import AutogluonModel
 from core.Logger import logger
 from core.DataLoader import dataloader_factory
 from core.CloudClient import S3FSClient
+from core.Metrics import Metrics
+from core.RegistryHandler import RegistryHandler
 from core.Settings import (
     BASE_REGISTRY_PATH,
     REGISTRY_FILE,
@@ -19,10 +22,11 @@ from core.Settings import (
     PREDICTION_FILE,
     METADATA_FILE,
     TIMESTAMP_FORMAT,
+    MODEL_DIRECTORY,
 )
 
 TIMESTAMP = datetime.now().strftime(TIMESTAMP_FORMAT)
-RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "dev")
+RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local-mock")
 
 CLIENT = S3FSClient(runtime_environment=RUNTIME_ENVIRONMENT)
 
@@ -82,7 +86,7 @@ def prediction(
         if registry_path is None or not registry_path.exists():
             logger.error("No registry path provided or registry doesn't exist")
             exit(1)
-    elif RUNTIME_ENVIRONMENT == "dev":
+    elif RUNTIME_ENVIRONMENT in ["local-mock", "dev"]:
         registry_path = "s3://retrofit-model-directory-dev/model_directory/RDSAP_CHANGE/model_registry.csv"
     else:
         raise NotImplemented("TO be implemented")
@@ -95,7 +99,17 @@ def prediction(
     else:
         # TODO: Think about where registry will sit/ type
         logger.info("Loading best model from registry")
-        registry_df = pd.read_csv(registry_path)
+
+        metrics = Metrics()
+        registry_handler = RegistryHandler()
+
+        registry_path = Path(MODEL_DIRECTORY) / target_column / REGISTRY_FILE
+
+        registry_df = registry_handler.load_registry(
+            registry_path=registry_path, s3fs_client=CLIENT, metrics=metrics
+        )
+
+        # registry_df = pd.read_csv(registry_path)
         best_model_df = registry_df[registry_df["best_model"]]
 
         model_location = best_model_df["model_location"].values[0]
@@ -120,7 +134,7 @@ def prediction(
             raise ValueError("No data loaded")
 
         # # TODO: DOWNSAMPLING DOWN TO JUST USE ONE FOR PREDICTION
-        # data = data.sample(1)
+        data = data.sample(1)
     else:
         logger.info("Using data provided")
         data = json.loads(str(data))

model_data/simulation_system/training.py

@@ -10,7 +10,7 @@ from core.Logger import logger
 from core.Metrics import Metrics, sort_by_metric
 from core.DataLoader import dataloader_factory
 from core.FeatureProcessor import FeatureProcessor
-from core.CloudClient import S3FSClient
+from core.CloudClient import S3FSClient, BotoClient
 from core.RegistryHandler import RegistryHandler
 from core.Settings import (
     MODEL_DIRECTORY,
@@ -28,9 +28,10 @@ from core.Settings import (
 
 TIMESTAMP = datetime.now().strftime(TIMESTAMP_FORMAT)
 
-RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
+RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local-mock")
 
-CLIENT = S3FSClient(runtime_environment=RUNTIME_ENVIRONMENT)
+CLIENT = BotoClient(runtime_environment=RUNTIME_ENVIRONMENT)
+# CLIENT = S3FSClient(runtime_environment=RUNTIME_ENVIRONMENT)
 
 
 # FOR TESTING