Remove use of is_container in tensorflow_type.py example

Signed-off-by: Eduardo Apolinario <[email protected]>

examples/data_types_and_io/data_types_and_io/tensorflow_type.py

@@ -9,48 +9,47 @@
     registry="ghcr.io/flyteorg",
 )
 
-if custom_image.is_container():
-    import tensorflow as tf
-
-    # TensorFlow Model
-    @task
-    def train_model() -> tf.keras.Model:
-        model = tf.keras.Sequential(
-            [tf.keras.layers.Dense(128, activation="relu"), tf.keras.layers.Dense(10, activation="softmax")]
-        )
-        model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
-        return model
-
-    @task
-    def evaluate_model(model: tf.keras.Model, x: tf.Tensor, y: tf.Tensor) -> float:
-        loss, accuracy = model.evaluate(x, y)
-        return accuracy
-
-    @workflow
-    def training_workflow(x: tf.Tensor, y: tf.Tensor) -> float:
-        model = train_model()
-        return evaluate_model(model=model, x=x, y=y)
-
-    # TFRecord Files
-    @task
-    def process_tfrecord(file: TFRecordFile) -> int:
-        count = 0
-        for record in tf.data.TFRecordDataset(file):
-            count += 1
-        return count
-
-    @workflow
-    def tfrecord_workflow(file: TFRecordFile) -> int:
-        return process_tfrecord(file=file)
-
-    # TFRecord Directories
-    @task
-    def process_tfrecords_dir(dir: TFRecordsDirectory) -> int:
-        count = 0
-        for record in tf.data.TFRecordDataset(dir.path):
-            count += 1
-        return count
-
-    @workflow
-    def tfrecords_dir_workflow(dir: TFRecordsDirectory) -> int:
-        return process_tfrecords_dir(dir=dir)
+import tensorflow as tf
+
+# TensorFlow Model
+@task
+def train_model() -> tf.keras.Model:
+    model = tf.keras.Sequential(
+        [tf.keras.layers.Dense(128, activation="relu"), tf.keras.layers.Dense(10, activation="softmax")]
+    )
+    model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
+    return model
+
+@task
+def evaluate_model(model: tf.keras.Model, x: tf.Tensor, y: tf.Tensor) -> float:
+    loss, accuracy = model.evaluate(x, y)
+    return accuracy
+
+@workflow
+def training_workflow(x: tf.Tensor, y: tf.Tensor) -> float:
+    model = train_model()
+    return evaluate_model(model=model, x=x, y=y)
+
+# TFRecord Files
+@task
+def process_tfrecord(file: TFRecordFile) -> int:
+    count = 0
+    for record in tf.data.TFRecordDataset(file):
+        count += 1
+    return count
+
+@workflow
+def tfrecord_workflow(file: TFRecordFile) -> int:
+    return process_tfrecord(file=file)
+
+# TFRecord Directories
+@task
+def process_tfrecords_dir(dir: TFRecordsDirectory) -> int:
+    count = 0
+    for record in tf.data.TFRecordDataset(dir.path):
+        count += 1
+    return count
+
+@workflow
+def tfrecords_dir_workflow(dir: TFRecordsDirectory) -> int:
+    return process_tfrecords_dir(dir=dir)

examples/data_types_and_io/requirements.in

@@ -1,4 +1,5 @@
 pandas
 torch
 tabulate
+tensorflow
 pyarrow