Merge branch 'master' of https://github.com/philschmid/sagemaker-python-sdk

Author: philschmid

tests/data/huggingface/run_tf.py

@@ -44,12 +44,8 @@
 
     # Load dataset
     train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
-    train_dataset = train_dataset.shuffle().select(
-        range(5000)
-    )  # smaller the size for train dataset to 5k
-    test_dataset = test_dataset.shuffle().select(
-        range(500)
-    )  # smaller the size for test dataset to 500
+    train_dataset = train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
+    test_dataset = test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500
 
     # Preprocess train dataset
     train_dataset = train_dataset.map(
@@ -82,9 +78,7 @@
     model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
 
     start_train_time = time.time()
-    train_results = model.fit(
-        tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size
-    )
+    train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
     end_train_time = time.time() - start_train_time
 
     logger.info("*** Train ***")

tests/integ/test_huggingface.py

@@ -107,9 +107,7 @@ def test_huggingface_training(
 
 
 @pytest.mark.release
-@pytest.mark.skipif(
-    integ.test_region() in integ.TRAINING_NO_P2_REGIONS, reason="no ml.p2 instances in this region"
-)
+@pytest.mark.skipif(integ.test_region() in integ.TRAINING_NO_P2_REGIONS, reason="no ml.p2 instances in this region")
 def test_huggingface_training_tf(
     sagemaker_session,
     gpu_instance_type,
@@ -172,9 +170,7 @@ def test_huggingface_inference(
         pytorch_version=huggingface_inference_pytorch_latest_version,
     )
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
-        model.deploy(
-            instance_type=gpu_instance_type, initial_instance_count=1, endpoint_name=endpoint_name
-        )
+        model.deploy(instance_type=gpu_instance_type, initial_instance_count=1, endpoint_name=endpoint_name)
 
         predictor = HuggingFacePredictor(endpoint_name=endpoint_name)
         data = {