Handle missing tensorflow for integration tests

python-client/tests/fixtures/imdb.py

@@ -1,14 +1,16 @@
 from pathlib import Path
 
 import pytest
-from keras.utils import text_dataset_from_directory, get_file
 
 batch_size = 32
 seed = 42
 
 
 @pytest.fixture()
 def imdb_data():
+    # Import put here to avoid failure when resolving tests
+    from keras.utils import text_dataset_from_directory, get_file
+
     dataset = get_file(
         "aclImdb",
         "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",

python-client/tests/models/tensorflow/test_mnist.py

@@ -1,18 +1,19 @@
-import tensorflow as tf
-from tensorflow import keras
 import pytest
-from giskard import Dataset
+
 import tests.utils
+from giskard import Dataset
 from giskard.models.tensorflow import TensorFlowModel
 
+tf = pytest.importorskip("tensorflow")
+
 
 # Define a simple sequential model
 def create_model():
     model = tf.keras.Sequential(
         [
-            keras.layers.Dense(512, activation="relu", input_shape=(784,)),
-            keras.layers.Dropout(0.2),
-            keras.layers.Dense(10, activation="softmax"),
+            tf.keras.layers.Dense(512, activation="relu", input_shape=(784,)),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.Dense(10, activation="softmax"),
         ]
     )
 

python-client/tests/models/tensorflow/test_text_classification.py

@@ -1,15 +1,15 @@
 import pandas as pd
 import pytest
-from tensorflow.keras import layers
 
-import tensorflow as tf
 import tests.utils
 from giskard import Dataset
 from giskard.models.tensorflow import TensorFlowModel
 
+tf = pytest.importorskip("tensorflow")
 
 @pytest.mark.slow
 def test_text_classification_1d_output(imdb_data):
+
     raw_train_ds, raw_test_ds = imdb_data
 
     test_dataset = {"Review": [], "Label": []}
@@ -36,11 +36,11 @@ def test_text_classification_1d_output(imdb_data):
 
     model = tf.keras.Sequential(
         [
-            layers.Embedding(max_features + 1, embedding_dim),
-            layers.Dropout(0.2),
-            layers.GlobalAveragePooling1D(),
-            layers.Dropout(0.2),
-            layers.Dense(1),
+            tf.keras.layers.Embedding(max_features + 1, embedding_dim),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.GlobalAveragePooling1D(),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.Dense(1),
         ]
     )
 
@@ -101,11 +101,11 @@ def test_text_classification_2d_output(imdb_data):
 
     model = tf.keras.Sequential(
         [
-            layers.Embedding(max_features + 1, embedding_dim),
-            layers.Dropout(0.2),
-            layers.GlobalAveragePooling1D(),
-            layers.Dropout(0.2),
-            layers.Dense(2),
+            tf.keras.layers.Embedding(max_features + 1, embedding_dim),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.GlobalAveragePooling1D(),
+            tf.keras.layers.Dropout(0.2),
+            tf.keras.layers.Dense(2),
             tf.keras.layers.Softmax(),
         ]
     )

python-client/tests/models/tensorflow/test_text_classification_tfhub.py

@@ -2,13 +2,13 @@
 
 import pandas as pd
 import pytest
-import tensorflow as tf
 from sklearn import model_selection
 
 import tests.utils
 from giskard import Dataset
 from giskard.models.tensorflow import TensorFlowModel
 
+tf = pytest.importorskip("tensorflow")
 
 def test_text_classification_tfhub():
     hub = pytest.importorskip("tensorflow_hub")

python-client/tests/models/tensorflow/test_tf_auto_model_as_embedding_layer.py

@@ -1,16 +1,16 @@
-from transformers import AutoTokenizer, TFAutoModel
-
-from giskard import Dataset
+import logging
 
-import pandas as pd
-import tensorflow as tf
 import numpy as np
-import logging
+import pandas as pd
 import pytest
+from transformers import AutoTokenizer, TFAutoModel
 
 import tests.utils
+from giskard import Dataset
 from giskard.models.tensorflow import TensorFlowModel
 
+tf = pytest.importorskip("tensorflow")
+
 logging.basicConfig(level=logging.INFO)
 
 
@@ -96,16 +96,6 @@ def get_inputs(tokenizer, sentences, max_length):
     return inputs
 
 
-pd.set_option("display.max_colwidth", None)
-
-models = {"complaints": "comp_debiased_10"}
-special_tokens = []
-max_length = {"complaints": 64}
-intent = "complaints"
-tokenizer, transformer_model = load_transformer_models("distilbert-base-multilingual-cased", special_tokens)
-model = get_model(max_length.get(intent), transformer_model, num_labels=1, name_model=models.get(intent))
-
-
 @pytest.mark.skip(
     reason="Loading must be customised to take care of loading the TF and embedded huggingFace model correctly"
 )
@@ -126,6 +116,15 @@ def test_tf_auto_model_as_embedding_layer():
         "The app developers are homosexual": 0,
     }
 
+    pd.set_option("display.max_colwidth", None)
+
+    models = {"complaints": "comp_debiased_10"}
+    special_tokens = []
+    max_length = {"complaints": 64}
+    intent = "complaints"
+    tokenizer, transformer_model = load_transformer_models("distilbert-base-multilingual-cased", special_tokens)
+    model = get_model(max_length.get(intent), transformer_model, num_labels=1, name_model=models.get(intent))
+
     data = pd.DataFrame(columns=["text", "label"])
     data.loc[:, "text"] = data_dict.keys()
     data.loc[:, "label"] = data_dict.values()