New feature

.github/workflows/assess_new_production_model.yml

@@ -14,7 +14,7 @@ jobs:
     - name: Configure AWS Credentials
       uses: aws-actions/configure-aws-credentials@v1
       with:
-        role-to-assume: <YOUR SERVICE PRINCIPAL ROLE ARN>
+        role-to-assume: arn:aws:iam::436283604051:role/OuterboundsServicePrincipalsRole
         aws-region: us-west-2
     - run: aws sts get-caller-identity
     - name: Set up Python 3.x
@@ -28,5 +28,5 @@ jobs:
       env:
         METAFLOW_HOME: /tmp/.metaflowconfig
       run: |
-        <YOUR OB CONFIGURE COMMAND FOR SERVICE PRINCIPALS>
+        outerbounds configure awssm-arn:eJxszc2KgzAUxfF3ydoLZnL9mOwykhEXo0MUhlmFmCbShVqMxUXpuxepdNXlgT/ndyPir9WtLJTsWv0jalFKpZUsq6YmnFwDOBNWoCR6Gz63FmqPzTJxswUenF3cGkYzmcEt/PXBM4oJSz+QIcOj4nN/gdHbefLnAdLPnKG3FHLXI+DJMTAs9dBTlvvE+jjOkESk+dJFU39Xpe7+f+UubwEOFQ6W3B8BAAD//95nQ6k=
         python evaluate_new_model_flow.py run --with card

constants.py

@@ -2,7 +2,7 @@
 EVALUATE_DEPLOYMENT_CANDIDATES_COMMAND = ["python", "evaluate_deployment_candidates.py"]
 
 # This is the threshold that determines whether a model is a candidate for deployment.
-# In practice, you might define this by comparing the result against a baseline model's performance.
+# In practice, you might define this by comparing the esult against a baseline model's performance.
 PERFORMANCE_THRESHOLDS = {
     'accuracy': 90
 }

evaluate_new_model_flow.py

@@ -21,7 +21,7 @@ class EvaluateNewModel(FlowSpec):
     def start(self):
         "Train and evaluate a model defined in my_data_science_module.py."
 
-        # Import my organization's custom modules.
+        # Import my organization's custom .
         from my_data_science_module import MyDataLoader, MyModel
 
         # Load some data.
@@ -30,7 +30,7 @@ def start(self):
         # In practice this may return a tabular dataframe or a DataLoader object for images or text.
 
         # Simulate scores measured on your model's performance.
-        self.model = MyModel() # When this flow passes your CI/CD criteria, this artifact will be used in production to produce predictions.
+        self.model = MyModel() # When this flow passes your CI/CD criteria, this artifacin production to produce predictions.
         self.eval_metrics = self.model.score(data=self.train_data)
         # In this toy example, the "model evaluation" will just add 1 to the "self.train_data" integer.
 

predict_flow.py

@@ -4,7 +4,7 @@
 
 def fetch_default_run_id():
     """
-    Return the run id of the latest successful upstream flow's deployment_candidate.
+    Return the run id of the latest successful 's deployment_candidate.
     In practice, you will want far more rigorous conditions. 
     For example, you might want to smoke test the model rather than just assert is not None.
     """