added toggle for evaluation step (#152)

  # These are the default values set in ml_service\util\env_variables.py. Uncomment and override if desired.
RUN_EVALUATION=true
ALLOW_RUN_CANCEL=true

Author: sudivate

.env.example

@@ -39,4 +39,10 @@ DATAFILE_NAME = 'diabetes.csv'
 DB_CLUSTER_ID = ''
 
 # Optional. Container Image name for image creation
-IMAGE_NAME = 'mltrained'
+IMAGE_NAME = 'mltrained'
+
+# Run Evaluation Step in AML pipeline
+RUN_EVALUATION = 'true'
+
+# Set to true cancels the Azure ML pipeline run when evaluation criteria are not met.
+ALLOW_RUN_CANCEL = 'true'

.pipelines/diabetes_regression-variables.yml

@@ -1,44 +1,49 @@
 variables:
   # Azure ML Workspace Variables
-- name: EXPERIMENT_NAME
-  value: mlopspython
-  # AML Compute Cluster Config
-- name: AML_COMPUTE_CLUSTER_CPU_SKU
-  value: STANDARD_DS2_V2
-- name: AML_COMPUTE_CLUSTER_NAME
-  value: train-cluster
-- name: AML_CLUSTER_MIN_NODES
-  value: 0
-- name: AML_CLUSTER_MAX_NODES
-  value: 4
-- name: AML_CLUSTER_PRIORITY
-  value: lowpriority
-  # Training Config
-- name: BUILD_TRAIN_SCRIPT
-  value: diabetes_regression_build_train_pipeline.py
-- name: TRAIN_SCRIPT_PATH
-  value: training/train.py
-- name: MODEL_NAME
-  value: sklearn_regression_model.pkl
-- name: MODEL_VERSION
-  value: '1'
-  # AML Pipeline Config 
-- name: TRAINING_PIPELINE_NAME
-  value: 'diabetes-Training-Pipeline'
-- name: MODEL_PATH
-  value: ''
-- name: EVALUATE_SCRIPT_PATH
-  value: evaluate/evaluate_model.py
-- name: REGISTER_SCRIPT_PATH
-  value: register/register_model.py
-- name: SOURCES_DIR_TRAIN
-  value: diabetes_regression
-- name: IMAGE_NAME
-  value: 'diabetestrained'
-  # Optional. Used by a training pipeline with R on Databricks
-- name: DB_CLUSTER_ID
-  value: ''
-- name: SCORE_SCRIPT
-  value: score.py
-- name: DATASET_NAME
-  value: diabetes_ds
+  - name: EXPERIMENT_NAME
+    value: mlopspython
+    # AML Compute Cluster Config
+  - name: AML_COMPUTE_CLUSTER_CPU_SKU
+    value: STANDARD_DS2_V2
+  - name: AML_COMPUTE_CLUSTER_NAME
+    value: train-cluster
+  - name: AML_CLUSTER_MIN_NODES
+    value: 0
+  - name: AML_CLUSTER_MAX_NODES
+    value: 4
+  - name: AML_CLUSTER_PRIORITY
+    value: lowpriority
+    # Training Config
+  - name: BUILD_TRAIN_SCRIPT
+    value: diabetes_regression_build_train_pipeline.py
+  - name: TRAIN_SCRIPT_PATH
+    value: training/train.py
+  - name: MODEL_NAME
+    value: sklearn_regression_model.pkl
+  - name: MODEL_VERSION
+    value: "1"
+    # AML Pipeline Config
+  - name: TRAINING_PIPELINE_NAME
+    value: "diabetes-Training-Pipeline"
+  - name: MODEL_PATH
+    value: ""
+  - name: EVALUATE_SCRIPT_PATH
+    value: evaluate/evaluate_model.py
+  - name: REGISTER_SCRIPT_PATH
+    value: register/register_model.py
+  - name: SOURCES_DIR_TRAIN
+    value: diabetes_regression
+  - name: IMAGE_NAME
+    value: "diabetestrained"
+    # Optional. Used by a training pipeline with R on Databricks
+  - name: DB_CLUSTER_ID
+    value: ""
+  - name: SCORE_SCRIPT
+    value: score.py
+  - name: DATASET_NAME
+    value: diabetes_ds
+  # These are the default values set in ml_service\util\env_variables.py. Uncomment and override if desired.
+  # - name: RUN_EVALUATION
+  #   value: "true"
+  # - name: ALLOW_RUN_CANCEL
+  #   value: "true"

README.md

@@ -12,7 +12,7 @@ description: "Code which demonstrates how to set up and operationalize an MLOps
 # MLOps with Azure ML
 
 
-[![Build Status](https://aidemos.visualstudio.com/MLOps/_apis/build/status/microsoft.MLOpsPython-CI?branchName=master)](https://aidemos.visualstudio.com/MLOps/_build/latest?definitionId=127&branchName=master)
+[![Build Status](https://aidemos.visualstudio.com/MLOps/_apis/build/status/microsoft.MLOpsPython?branchName=master)](https://aidemos.visualstudio.com/MLOps/_build/latest?definitionId=151&branchName=master)
 
 
 MLOps will help you to understand how to build the Continuous Integration and Continuous Delivery pipeline for a ML/AI project. We will be using the Azure DevOps Project for build and release/deployment pipelines along with Azure ML services for model retraining pipeline, model management and operationalization. 

diabetes_regression/evaluate/evaluate_model.py

@@ -83,6 +83,12 @@
     help="Name of the Model",
     default="sklearn_regression_model.pkl",
 )
+parser.add_argument(
+    "--allow_run_cancel",
+    type=str,
+    help="Set this to false to avoid evaluation step from cancelling run after an unsuccessful evaluation",  # NOQA: E501
+    default="true",
+)
 
 args = parser.parse_args()
 if (args.build_id is not None):
@@ -98,8 +104,8 @@
 if (builduri_base is not None):
     build_uri = builduri_base + build_id
     run.tag("BuildUri", value=build_uri)
-
-# Paramaterize the matrices on which the models should be compared
+allow_run_cancel = args.allow_run_cancel
+# Parameterize the matrices on which the models should be compared
 # Add golden data set on which all the model performance can be evaluated
 try:
     firstRegistration = False
@@ -124,7 +130,8 @@
         if (production_model_mse is None or new_model_mse is None):
             print("Unable to find", metric_eval, "metrics, "
                   "exiting evaluation")
-            run.parent.cancel()
+            if((allow_run_cancel).lower() == 'true'):
+                run.parent.cancel()
         else:
             print(
                 "Current Production model mse: {}, "
@@ -139,7 +146,8 @@
         else:
             print("New trained model metric is less than or equal to "
                   "production model so skipping model registration.")
-            run.parent.cancel()
+            if((allow_run_cancel).lower() == 'true'):
+                run.parent.cancel()
     else:
         print("This is the first model, "
               "thus it should be registered")

ml_service/pipelines/diabetes_regression_build_train_pipeline.py

@@ -89,6 +89,7 @@ def main():
         arguments=[
             "--build_id", build_id_param,
             "--model_name", model_name_param,
+            "--allow_run_cancel", e.allow_run_cancel,
         ],
         runconfig=run_config,
         allow_reuse=False,
@@ -108,10 +109,16 @@ def main():
         allow_reuse=False,
     )
     print("Step Register created")
-
-    evaluate_step.run_after(train_step)
-    register_step.run_after(evaluate_step)
-    steps = [train_step, evaluate_step, register_step]
+    # Check run_evaluation flag to include or exclude evaluation step.
+    if ((e.run_evaluation).lower() == 'true'):
+        print("Include evaluation step before register step.")
+        evaluate_step.run_after(train_step)
+        register_step.run_after(evaluate_step)
+        steps = [train_step, evaluate_step, register_step]
+    else:
+        print("Exclude evaluation step and directly run register step.")
+        register_step.run_after(train_step)
+        steps = [train_step, register_step]
 
     train_pipeline = Pipeline(workspace=aml_workspace, steps=steps)
     train_pipeline._set_experiment_name

ml_service/util/env_variables.py

@@ -44,6 +44,9 @@ def __init__(self):
         self._datastore_name = os.environ.get("DATASTORE_NAME")
         self._datafile_name = os.environ.get("DATAFILE_NAME")
         self._dataset_name = os.environ.get("DATASET_NAME")
+        self._run_evaluation = os.environ.get("RUN_EVALUATION", "true")
+        self._allow_run_cancel = os.environ.get(
+            "ALLOW_RUN_CANCEL", "true")
 
     @property
     def workspace_name(self):
@@ -160,3 +163,11 @@ def datafile_name(self):
     @property
     def dataset_name(self):
         return self._dataset_name
+
+    @property
+    def run_evaluation(self):
+        return self._run_evaluation
+
+    @property
+    def allow_run_cancel(self):
+        return self._allow_run_cancel