Merge branch 'master' into jenns/splitpipeline

Author: j-so

ml_service/util/env_variables.py

@@ -1,174 +1,52 @@
+"""Env dataclass to load and hold all environment variables
+"""
+from dataclasses import dataclass
 import os
-from dotenv import load_dotenv
-
-
-class Singleton(object):
-    _instances = {}
-
-    def __new__(class_, *args, **kwargs):
-        if class_ not in class_._instances:
-            class_._instances[class_] = super(Singleton, class_).__new__(class_, *args, **kwargs)  # noqa E501
-        return class_._instances[class_]
-
-
-class Env(Singleton):
-
-    def __init__(self):
-        load_dotenv()
-        self._workspace_name = os.environ.get("WORKSPACE_NAME")
-        self._resource_group = os.environ.get("RESOURCE_GROUP")
-        self._subscription_id = os.environ.get("SUBSCRIPTION_ID")
-        self._tenant_id = os.environ.get("TENANT_ID")
-        self._app_id = os.environ.get("SP_APP_ID")
-        self._app_secret = os.environ.get("SP_APP_SECRET")
-        self._vm_size = os.environ.get("AML_COMPUTE_CLUSTER_CPU_SKU")
-        self._compute_name = os.environ.get("AML_COMPUTE_CLUSTER_NAME")
-        self._vm_priority = os.environ.get("AML_CLUSTER_PRIORITY", 'lowpriority')  # noqa E501
-        self._min_nodes = int(os.environ.get("AML_CLUSTER_MIN_NODES", 0))
-        self._max_nodes = int(os.environ.get("AML_CLUSTER_MAX_NODES", 4))
-        self._build_id = os.environ.get("BUILD_BUILDID")
-        self._pipeline_name = os.environ.get("TRAINING_PIPELINE_NAME")
-        self._sources_directory_train = os.environ.get("SOURCES_DIR_TRAIN")
-        self._train_script_path = os.environ.get("TRAIN_SCRIPT_PATH")
-        self._evaluate_script_path = os.environ.get("EVALUATE_SCRIPT_PATH")
-        self._register_script_path = os.environ.get("REGISTER_SCRIPT_PATH")
-        self._model_name = os.environ.get("MODEL_NAME")
-        self._experiment_name = os.environ.get("EXPERIMENT_NAME")
-        self._model_version = os.environ.get('MODEL_VERSION')
-        self._image_name = os.environ.get('IMAGE_NAME')
-        self._db_cluster_id = os.environ.get("DB_CLUSTER_ID")
-        self._score_script = os.environ.get("SCORE_SCRIPT")
-        self._build_uri = os.environ.get("BUILD_URI")
-        self._dataset_name = os.environ.get("DATASET_NAME")
-        self._datastore_name = os.environ.get("DATASTORE_NAME")
-        self._dataset_version = os.environ.get("DATASET_VERSION")
-        self._run_evaluation = os.environ.get("RUN_EVALUATION", "true")
-        self._allow_run_cancel = os.environ.get(
-            "ALLOW_RUN_CANCEL", "true")
-        self._aml_env_name = os.environ.get("AML_ENV_NAME")
-        self._rebuild_env = os.environ.get("AML_REBUILD_ENVIRONMENT",
-                                           "false").lower().strip() == "true"
-
-    @property
-    def workspace_name(self):
-        return self._workspace_name
-
-    @property
-    def resource_group(self):
-        return self._resource_group
-
-    @property
-    def subscription_id(self):
-        return self._subscription_id
-
-    @property
-    def tenant_id(self):
-        return self._tenant_id
-
-    @property
-    def app_id(self):
-        return self._app_id
-
-    @property
-    def app_secret(self):
-        return self._app_secret
-
-    @property
-    def vm_size(self):
-        return self._vm_size
-
-    @property
-    def compute_name(self):
-        return self._compute_name
-
-    @property
-    def db_cluster_id(self):
-        return self._db_cluster_id
-
-    @property
-    def build_id(self):
-        return self._build_id
-
-    @property
-    def pipeline_name(self):
-        return self._pipeline_name
+from typing import Optional
 
-    @property
-    def sources_directory_train(self):
-        return self._sources_directory_train
-
-    @property
-    def train_script_path(self):
-        return self._train_script_path
-
-    @property
-    def evaluate_script_path(self):
-        return self._evaluate_script_path
-
-    @property
-    def register_script_path(self):
-        return self._register_script_path
-
-    @property
-    def model_name(self):
-        return self._model_name
-
-    @property
-    def experiment_name(self):
-        return self._experiment_name
-
-    @property
-    def vm_priority(self):
-        return self._vm_priority
-
-    @property
-    def min_nodes(self):
-        return self._min_nodes
-
-    @property
-    def max_nodes(self):
-        return self._max_nodes
-
-    @property
-    def model_version(self):
-        return self._model_version
-
-    @property
-    def image_name(self):
-        return self._image_name
-
-    @property
-    def score_script(self):
-        return self._score_script
-
-    @property
-    def build_uri(self):
-        return self._build_uri
-
-    @property
-    def dataset_name(self):
-        return self._dataset_name
-
-    @property
-    def datastore_name(self):
-        return self._datastore_name
-
-    @property
-    def dataset_version(self):
-        return self._dataset_version
-
-    @property
-    def run_evaluation(self):
-        return self._run_evaluation
-
-    @property
-    def allow_run_cancel(self):
-        return self._allow_run_cancel
+from dotenv import load_dotenv
 
-    @property
-    def aml_env_name(self):
-        return self._aml_env_name
 
-    @property
-    def rebuild_env(self):
-        return self._rebuild_env
+@dataclass(frozen=True)
+class Env:
+    """Loads all environment variables into a predefined set of properties
+    """
+    # to load .env file into environment variables for local execution
+    load_dotenv()
+    workspace_name: Optional[str] = os.environ.get("WORKSPACE_NAME")
+    resource_group: Optional[str] = os.environ.get("RESOURCE_GROUP")
+    subscription_id: Optional[str] = os.environ.get("SUBSCRIPTION_ID")
+    tenant_id: Optional[str] = os.environ.get("TENANT_ID")
+    app_id: Optional[str] = os.environ.get("SP_APP_ID")
+    app_secret: Optional[str] = os.environ.get("SP_APP_SECRET")
+    vm_size: Optional[str] = os.environ.get("AML_COMPUTE_CLUSTER_CPU_SKU")
+    compute_name: Optional[str] = os.environ.get("AML_COMPUTE_CLUSTER_NAME")
+    vm_priority: Optional[str] = os.environ.get("AML_CLUSTER_PRIORITY",
+                                                'lowpriority')
+    min_nodes: int = int(os.environ.get("AML_CLUSTER_MIN_NODES", 0))
+    max_nodes: int = int(os.environ.get("AML_CLUSTER_MAX_NODES", 4))
+    build_id: Optional[str] = os.environ.get("BUILD_BUILDID")
+    pipeline_name: Optional[str] = os.environ.get("TRAINING_PIPELINE_NAME")
+    sources_directory_train: Optional[str] = os.environ.get(
+        "SOURCES_DIR_TRAIN")
+    train_script_path: Optional[str] = os.environ.get("TRAIN_SCRIPT_PATH")
+    evaluate_script_path: Optional[str] = os.environ.get(
+        "EVALUATE_SCRIPT_PATH")
+    register_script_path: Optional[str] = os.environ.get(
+        "REGISTER_SCRIPT_PATH")
+    model_name: Optional[str] = os.environ.get("MODEL_NAME")
+    experiment_name: Optional[str] = os.environ.get("EXPERIMENT_NAME")
+    model_version: Optional[str] = os.environ.get('MODEL_VERSION')
+    image_name: Optional[str] = os.environ.get('IMAGE_NAME')
+    db_cluster_id: Optional[str] = os.environ.get("DB_CLUSTER_ID")
+    score_script: Optional[str] = os.environ.get("SCORE_SCRIPT")
+    build_uri: Optional[str] = os.environ.get("BUILD_URI")
+    dataset_name: Optional[str] = os.environ.get("DATASET_NAME")
+    datastore_name: Optional[str] = os.environ.get("DATASTORE_NAME")
+    dataset_version: Optional[str] = os.environ.get("DATASET_VERSION")
+    run_evaluation: Optional[str] = os.environ.get("RUN_EVALUATION", "true")
+    allow_run_cancel: Optional[str] = os.environ.get("ALLOW_RUN_CANCEL",
+                                                     "true")
+    aml_env_name: Optional[str] = os.environ.get("AML_ENV_NAME")
+    rebuild_env: Optional[bool] = os.environ.get(
+        "AML_REBUILD_ENVIRONMENT", "false").lower().strip() == "true"