Check if there are GPUs instead of if TF is built with CUDA support.

The TF pip packages are always built with CUDA support, so tf.test.is_built_with_cuda() would return True even if the user had no GPU.

PiperOrigin-RevId: 302928378

official/benchmark/models/resnet_cifar_main.py

@@ -138,8 +138,8 @@ def run(flags_obj):
 
   data_format = flags_obj.data_format
   if data_format is None:
-    data_format = ('channels_first'
-                   if tf.test.is_built_with_cuda() else 'channels_last')
+    data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                   else 'channels_last')
   tf.keras.backend.set_image_data_format(data_format)
 
   strategy = distribution_utils.get_distribution_strategy(

official/modeling/model_training_utils.py

@@ -415,7 +415,7 @@ def run_customized_training_loop(
       # Runs several steps in the host while loop.
       steps = steps_to_run(current_step, steps_per_epoch, steps_per_loop)
 
-      if tf.test.is_built_with_cuda():
+      if tf.config.list_physical_devices('GPU'):
         # TODO(zongweiz): merge with train_steps once tf.while_loop
         # GPU performance bugs are fixed.
         for _ in range(steps):

official/r1/mnist/mnist.py

@@ -183,8 +183,8 @@ def run_mnist(flags_obj):
 
   data_format = flags_obj.data_format
   if data_format is None:
-    data_format = ('channels_first'
-                   if tf.test.is_built_with_cuda() else 'channels_last')
+    data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                   else 'channels_last')
   mnist_classifier = tf.estimator.Estimator(
       model_fn=model_function,
       model_dir=flags_obj.model_dir,

official/r1/resnet/resnet_model.py

@@ -391,8 +391,8 @@ class Model(object):
     self.resnet_size = resnet_size
 
     if not data_format:
-      data_format = (
-          'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
+      data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                     else 'channels_last')
 
     self.resnet_version = resnet_version
     if resnet_version not in (1, 2):

official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py

@@ -119,8 +119,8 @@ def run(flags_obj):
   # TODO(anj-s): Set data_format without using Keras.
   data_format = flags_obj.data_format
   if data_format is None:
-    data_format = ('channels_first'
-                   if tf.test.is_built_with_cuda() else 'channels_last')
+    data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                   else 'channels_last')
   tf.keras.backend.set_image_data_format(data_format)
 
   strategy = distribution_utils.get_distribution_strategy(

official/vision/image_classification/resnet/resnet_imagenet_main.py

@@ -71,8 +71,8 @@ def run(flags_obj):
 
   data_format = flags_obj.data_format
   if data_format is None:
-    data_format = ('channels_first'
-                   if tf.test.is_built_with_cuda() else 'channels_last')
+    data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                   else 'channels_last')
   tf.keras.backend.set_image_data_format(data_format)
 
   # Configures cluster spec for distribution strategy.