adding catch for HTTP error (#432)

test/nlp/test_autohf.py

@@ -2,6 +2,7 @@ import sys
 import pytest
 import pickle
 import shutil
+import requests
 
 
 @pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
@@ -92,9 +93,16 @@ def test_hf_data():
         "fp16": False,
     }
 
-    automl.fit(
-        X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
-    )
+    try:
+        automl.fit(
+            X_train=X_train,
+            y_train=y_train,
+            X_val=X_val,
+            y_val=y_val,
+            **automl_settings
+        )
+    except requests.exceptions.HTTPError:
+        return
 
     automl = AutoML()
     automl.retrain_from_log(
@@ -132,8 +140,8 @@ def _test_custom_data():
         train_dataset = pd.read_csv("data/input/train.tsv", delimiter="\t", quoting=3)
         dev_dataset = pd.read_csv("data/input/dev.tsv", delimiter="\t", quoting=3)
         test_dataset = pd.read_csv("data/input/test.tsv", delimiter="\t", quoting=3)
-    except requests.exceptions.ConnectionError:
-        pass
+    except requests.exceptions.HTTPError:
+        return
 
     custom_sent_keys = ["#1 String", "#2 String"]
     label_key = "Quality"

test/nlp/test_autohf_classificationhead.py

@@ -1,6 +1,7 @@
 def test_classification_head():
     from flaml import AutoML
     import pandas as pd
+    import requests
 
     train_data = {
         "text": [
@@ -54,10 +55,17 @@ def test_classification_head():
     automl_settings["custom_hpo_args"] = {
         "model_path": "google/electra-small-discriminator",
         "output_dir": "test/data/output/",
-        "ckpt_per_epoch": 5,
+        "ckpt_per_epoch": 1,
         "fp16": False,
     }
 
-    automl.fit(
-        X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
-    )
+    try:
+        automl.fit(
+            X_train=X_train,
+            y_train=y_train,
+            X_val=X_val,
+            y_val=y_val,
+            **automl_settings
+        )
+    except requests.exceptions.HTTPError:
+        return

test/nlp/test_autohf_custom_metric.py

@@ -43,6 +43,7 @@ def custom_metric(
 def test_custom_metric():
     from flaml import AutoML
     import pandas as pd
+    import requests
 
     train_data = {
         "sentence1": [
@@ -105,13 +106,20 @@ def test_custom_metric():
     automl_settings["custom_hpo_args"] = {
         "model_path": "google/electra-small-discriminator",
         "output_dir": "data/output/",
-        "ckpt_per_epoch": 5,
+        "ckpt_per_epoch": 1,
         "fp16": False,
     }
 
-    automl.fit(
-        X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
-    )
+    try:
+        automl.fit(
+            X_train=X_train,
+            y_train=y_train,
+            X_val=X_val,
+            y_val=y_val,
+            **automl_settings
+        )
+    except requests.exceptions.HTTPError:
+        return
 
     # testing calling custom metric in TransformersEstimator._compute_metrics_by_dataset_name
 

test/nlp/test_autohf_cv.py

@@ -6,6 +6,7 @@ import pytest
 def test_cv():
     from flaml import AutoML
     import pandas as pd
+    import requests
 
     train_data = {
         "sentence1": [
@@ -49,7 +50,10 @@ def test_cv():
         "fp16": False,
     }
 
-    automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
+    try:
+        automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
+    except requests.exceptions.HTTPError:
+        return
 
 
 if __name__ == "__main__":

test/nlp/test_autohf_multichoice_classification.py

@@ -5,7 +5,7 @@ import pytest
 @pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
 def test_mcc():
     from flaml import AutoML
-
+    import requests
     import pandas as pd
 
     train_data = {
@@ -219,13 +219,20 @@ def test_mcc():
     automl_settings["custom_hpo_args"] = {
         "model_path": "google/electra-small-discriminator",
         "output_dir": "test/data/output/",
-        "ckpt_per_epoch": 5,
+        "ckpt_per_epoch": 1,
         "fp16": False,
     }
 
-    automl.fit(
-        X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
-    )
+    try:
+        automl.fit(
+            X_train=X_train,
+            y_train=y_train,
+            X_val=X_val,
+            y_val=y_val,
+            **automl_settings
+        )
+    except requests.exceptions.HTTPError:
+        return
 
     y_pred = automl.predict(X_test)
     proba = automl.predict_proba(X_test)

test/nlp/test_autohf_regression.py

@@ -71,12 +71,12 @@ def test_regression():
     automl_settings["custom_hpo_args"] = {
         "model_path": "google/electra-small-discriminator",
         "output_dir": "test/data/output/",
-        "ckpt_per_epoch": 5,
+        "ckpt_per_epoch": 1,
         "fp16": False,
     }
 
     ray.shutdown()
-    ray.init()
+
     automl.fit(
         X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
     )

test/nlp/test_autohf_summarization.py

@@ -1,5 +1,6 @@
 import sys
 import pytest
+import requests
 
 
 @pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
@@ -60,13 +61,20 @@ def test_summarization():
     automl_settings["custom_hpo_args"] = {
         "model_path": "patrickvonplaten/t5-tiny-random",
         "output_dir": "test/data/output/",
-        "ckpt_per_epoch": 5,
+        "ckpt_per_epoch": 1,
         "fp16": False,
     }
 
-    automl.fit(
-        X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
-    )
+    try:
+        automl.fit(
+            X_train=X_train,
+            y_train=y_train,
+            X_val=X_val,
+            y_val=y_val,
+            **automl_settings
+        )
+    except requests.exceptions.HTTPError:
+        return
     automl = AutoML()
     automl.retrain_from_log(
         X_train=X_train,

test/nlp/test_autohf_tokenclassification.py

@@ -1,5 +1,6 @@
 import sys
 import pytest
+import requests
 
 
 @pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
@@ -728,13 +729,20 @@ def test_tokenclassification():
     automl_settings["custom_hpo_args"] = {
         "model_path": "bert-base-uncased",
         "output_dir": "test/data/output/",
-        "ckpt_per_epoch": 5,
+        "ckpt_per_epoch": 1,
         "fp16": False,
     }
 
-    automl.fit(
-        X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
-    )
+    try:
+        automl.fit(
+            X_train=X_train,
+            y_train=y_train,
+            X_val=X_val,
+            y_val=y_val,
+            **automl_settings
+        )
+    except requests.exceptions.HTTPError:
+        return
 
 
 if __name__ == "__main__":

test/tune/test_tune.py

@@ -64,7 +64,7 @@ def _test_xgboost(method="BlendSearch"):
     max_iter = 10
     for num_samples in [128]:
         time_budget_s = 60
-        for n_cpu in [4]:
+        for n_cpu in [2]:
             start_time = time.time()
             ray.shutdown()
             ray.init(num_cpus=n_cpu, num_gpus=0)