clean up conda pack name

Author: ahosler

ads/opctl/operator/__init__.py

@@ -14,7 +14,9 @@ def __registered_operators():
     return [
         f
         for f in os.listdir(target_dir)
-        if os.path.isdir(os.path.join(target_dir, f)) and not f.startswith("__")
+        if os.path.isdir(os.path.join(target_dir, f))
+        and not f.startswith("__")
+        and f != "common"
     ]
 
 

ads/opctl/operator/lowcode/anomaly/MLoperator

@@ -1,12 +1,12 @@
 type: anomaly
 version: v1
-conda_type: published
+conda_type: service
 name: Anomaly Detection Operator
 gpu: no
 keywords:
   - Anomaly Detection
 backends:
-  - job, local
+  - job
 description: |
   Anomaly Detection is the identification of rare items, events, or observations in data that
   differ significantly from the expectation. This can be used for several scenarios like asset

tests/operators/anomaly/test_anomaly_simple.py

@@ -104,7 +104,7 @@ def test_artificial_big(model):
         assert os.path.exists(f"{output_dirname}/report.html"), "Report not generated."
 
 
-@pytest.mark.parametrize("model", MODELS + ["auto"])
+@pytest.mark.parametrize("model", MODELS)  # + ["auto"]
 def test_artificial_small(model):
     # artificial data
     d1 = np.random.multivariate_normal(
@@ -131,7 +131,7 @@ def test_artificial_small(model):
         yaml_i["spec"]["output_directory"]["url"] = output_dirname
         yaml_i["spec"]["contamination"] = 0.3
 
-        # run(yaml_i, backend="operator.local", debug=False)
+        # run(yaml_i, debug=False)
 
         with open(anomaly_yaml_filename, "w") as f:
             f.write(yaml.dump(yaml_i))