remove alpha from pipeline parameters

.pipelines/diabetes_regression-ci-build-train.yml

@@ -62,30 +62,20 @@ stages:
           echo "##vso[task.setvariable variable=AMLPIPELINEID;isOutput=true]$AMLPIPELINEID"
       name: 'getpipelineid'
       displayName: 'Get Pipeline ID'
-    - bash: |
-          # Generate a hyperparameter value as a random number between 0 and 1.
-          # A random value is used here to make the Azure ML dashboards "interesting" when testing
-          # the solution sample.
-          alpha=$(printf "0.%03d\n" $((($RANDOM*1000)/32767)))
-          echo "Alpha: $alpha"
-          echo "##vso[task.setvariable variable=ALPHA;isOutput=true]$alpha"
-      name: 'getalpha'
-      displayName: 'Generate random value for hyperparameter alpha'
   - job: "Run_ML_Pipeline"
     dependsOn: "Get_Pipeline_ID"
     displayName: "Trigger ML Training Pipeline"
     pool: server
     variables:
       AMLPIPELINE_ID: $[ dependencies.Get_Pipeline_ID.outputs['getpipelineid.AMLPIPELINEID'] ]
-      ALPHA: $[ dependencies.Get_Pipeline_ID.outputs['getalpha.ALPHA'] ]
     steps:
     - task: ms-air-aiagility.vss-services-azureml.azureml-restApi-task.MLPublishedPipelineRestAPITask@0
       displayName: 'Invoke ML pipeline'
       inputs:
         azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
         PipelineId: '$(AMLPIPELINE_ID)'
         ExperimentName: '$(EXPERIMENT_NAME)'
-        PipelineParameters: '"ParameterAssignments": {"model_name": "$(MODEL_NAME)", "hyperparameter_alpha": "$(ALPHA)"}'
+        PipelineParameters: '"ParameterAssignments": {"model_name": "$(MODEL_NAME)"}'
   - job: "Training_Run_Report"
     dependsOn: "Run_ML_Pipeline"
     condition: always()

diabetes_regression/training/train.json

@@ -0,0 +1,3 @@
+{
+    "alpha": 0.4
+}

diabetes_regression/training/train.py

@@ -32,6 +32,7 @@
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 from sklearn.externals import joblib
+import json
 
 
 def train_model(run, data, alpha):
@@ -47,6 +48,12 @@ def train_model(run, data, alpha):
     return reg
 
 
+def get_model_parameters():
+    with open("train.json") as f:
+        data = json.load(f)
+    return data
+
+
 def main():
     print("Running train.py")
 
@@ -62,13 +69,6 @@ def main():
         help="Name of the Model",
         default="sklearn_regression_model.pkl",
     )
-    parser.add_argument(
-        "--alpha",
-        type=float,
-        default=0.5,
-        help=("Ridge regression regularization strength hyperparameter; "
-              "must be a positive float.")
-    )
 
     parser.add_argument(
         "--dataset_name",
@@ -79,14 +79,21 @@ def main():
 
     print("Argument [build_id]: %s" % args.build_id)
     print("Argument [model_name]: %s" % args.model_name)
-    print("Argument [alpha]: %s" % args.alpha)
     print("Argument [dataset_name]: %s" % args.dataset_name)
 
     model_name = args.model_name
     build_id = args.build_id
-    alpha = args.alpha
     dataset_name = args.dataset_name
 
+    print("Getting training parameters")
+
+    pars = get_model_parameters()
+    alpha = pars.get("alpha")
+    if alpha is None:
+        alpha = 0.5
+
+    print("Parameter alpha: %s" % alpha)
+
     run = Run.get_context()
     ws = run.experiment.workspace
 

ml_service/pipelines/diabetes_regression_build_train_pipeline.py

@@ -48,8 +48,6 @@ def main():
         name="model_name", default_value=e.model_name)
     build_id_param = PipelineParameter(
         name="build_id", default_value=e.build_id)
-    hyperparameter_alpha_param = PipelineParameter(
-        name="hyperparameter_alpha", default_value=0.5)
 
     dataset_name = ""
     if (e.datastore_name is not None and e.datafile_name is not None):
@@ -70,7 +68,6 @@ def main():
         arguments=[
             "--build_id", build_id_param,
             "--model_name", model_name_param,
-            "--alpha", hyperparameter_alpha_param,
             "--dataset_name", dataset_name,
         ],
         runconfig=run_config,