Uncomment a test.
@@ -37,29 +37,29 @@ def test_entailment_multinli_bert(notebooks, tmp):
assert pytest.approx(result["mismatched_recall"], 0.76, abs=ABS_TOL)
assert pytest.approx(result["mismatched_f1"], 0.76, abs=ABS_TOL)
-# @pytest.mark.integration
-# @pytest.mark.azureml
-# def test_entailment_xnli_bert_azureml(
-# notebooks, subscription_id, resource_group, workspace_name, workspace_region, cluster_name
-# ):
-# notebook_path = notebooks["entailment_xnli_bert_azureml"]
-# pm.execute_notebook(
-# notebook_path,
-# OUTPUT_NOTEBOOK,
-# parameters={
-# "DATA_PERCENT_USED": 0.0025,
-# "subscription_id": subscription_id,
-# "resource_group": resource_group,
-# "workspace_name": workspace_name,
-# "workspace_region": workspace_region,
-# "cluster_name": cluster_name,
-# },
-# kernel_name=KERNEL_NAME,
-# )
+@pytest.mark.integration
+@pytest.mark.azureml
+def test_entailment_xnli_bert_azureml(
+ notebooks, subscription_id, resource_group, workspace_name, workspace_region, cluster_name
+):
+ notebook_path = notebooks["entailment_xnli_bert_azureml"]
+ pm.execute_notebook(
+ notebook_path,
+ OUTPUT_NOTEBOOK,
+ parameters={
+ "DATA_PERCENT_USED": 0.0025,
+ "subscription_id": subscription_id,
+ "resource_group": resource_group,
+ "workspace_name": workspace_name,
+ "workspace_region": workspace_region,
+ "cluster_name": cluster_name,
+ },
+ kernel_name=KERNEL_NAME,
+ )
-# with open("outputs/results.json", "r") as handle:
-# result_dict = json.load(handle)
-# assert result_dict["weighted avg"]["f1-score"] == pytest.approx(0.2, abs=ABS_TOL)
+ with open("outputs/results.json", "r") as handle:
+ result_dict = json.load(handle)
+ assert result_dict["weighted avg"]["f1-score"] == pytest.approx(0.2, abs=ABS_TOL)
-# if os.path.exists("outputs"):
-# shutil.rmtree("outputs")
+ if os.path.exists("outputs"):
+ shutil.rmtree("outputs")
