Tag the experiment runs and model with a link to the build ui (#132)

Author: jotaylo

code/evaluate/evaluate_model.py

@@ -90,6 +90,10 @@
 model_name = args.model_name
 metric_eval = "mse"
 run.tag("BuildId", value=build_id)
+builduri_base = os.environ.get("BUILDURI_BASE")
+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
 # Add golden data set on which all the model performance can be evaluated

code/register/register_model.py

@@ -88,7 +88,11 @@ def main():
         register_aml_model(model_name, exp, run_id)
     else:
         run.tag("BuildId", value=build_id)
-        register_aml_model(model_name, exp, run_id, build_id)
+        builduri_base = os.environ.get("BUILDURI_BASE")
+        if (builduri_base is not None):
+            build_uri = builduri_base + build_id
+            run.tag("BuildUri", value=build_uri)
+        register_aml_model(model_name, exp, run_id, build_id, build_uri)
 
 
 def model_already_registered(model_name, exp, run_id):
@@ -102,14 +106,22 @@ def model_already_registered(model_name, exp, run_id):
         print("Model is not registered for this run.")
 
 
-def register_aml_model(model_name, exp, run_id, build_id: str = 'none'):
+def register_aml_model(
+    model_name,
+    exp,
+    run_id,
+    build_id: str = 'none',
+    build_uri=None
+):
     try:
         if (build_id != 'none'):
             model_already_registered(model_name, exp, run_id)
             run = Run(experiment=exp, run_id=run_id)
             tagsValue = {"area": "diabetes", "type": "regression",
                          "BuildId": build_id, "run_id": run_id,
                          "experiment_name": exp.name}
+            if (build_uri is not None):
+                tagsValue["BuildUri"] = build_uri
         else:
             run = Run(experiment=exp, run_id=run_id)
             if (run is not None):

code/training/train.py

@@ -101,9 +101,16 @@ def main():
     print("Following files are uploaded ")
     print(run.parent.get_file_names())
 
+    run.parent.tag("BuildId", value=build_id)
+
     # Add properties to identify this specific training run
     run.tag("BuildId", value=build_id)
     run.tag("run_type", value="train")
+    builduri_base = os.environ.get("BUILDURI_BASE")
+    if (builduri_base is not None):
+        build_uri = builduri_base + build_id
+        run.tag("BuildUri", value=build_uri)
+        run.parent.tag("BuildUri", value=build_uri)
     print(f"tags now present for run: {run.tags}")
 
     run.complete()

ml_service/pipelines/build_train_pipeline.py

@@ -39,6 +39,13 @@ def main():
     )
     run_config.environment.docker.enabled = True
 
+    config_envvar = {}
+    if (e.collection_uri is not None and e.teamproject_name is not None):
+        builduri_base = e.collection_uri + e.teamproject_name
+        builduri_base = builduri_base + "/_build/results?buildId="
+        config_envvar["BUILDURI_BASE"] = builduri_base
+    run_config.environment.environment_variables = config_envvar
+
     model_name_param = PipelineParameter(
         name="model_name", default_value=e.model_name)
     build_id_param = PipelineParameter(

ml_service/util/env_variables.py

@@ -39,6 +39,8 @@ def __init__(self):
         self._model_path = os.environ.get('MODEL_PATH')
         self._db_cluster_id = os.environ.get("DB_CLUSTER_ID")
         self._score_script = os.environ.get("SCORE_SCRIPT")
+        self._collection_uri = os.environ.get("SYSTEM_COLLECTIONURI")
+        self._teamproject_name = os.environ.get("SYSTEM_TEAMPROJECT")
 
     @property
     def workspace_name(self):
@@ -135,3 +137,11 @@ def model_path(self):
     @property
     def score_script(self):
         return self._score_script
+
+    @property
+    def collection_uri(self):
+        return self._collection_uri
+
+    @property
+    def teamproject_name(self):
+        return self._teamproject_name