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Merged
j-so merged 2 commits into from
May 20, 2020
Merged

Simplify Env implementation with python dataclass #277

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e52dec0
Merge pull request #1 from microsoft/master
fujikosu May 19, 2020
e8161d4
Replaced Env class with dataclass
fujikosu May 19, 2020
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218 changes: 48 additions & 170 deletions ml_service/util/env_variables.py
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Original file line number Diff line number Diff line change
@@ -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"