experimental_steps_per_execution -> steps_per_execution for TF 2.4 release.

PiperOrigin-RevId: 342566888

official/nlp/bert/run_classifier.py

@@ -229,7 +229,7 @@ def run_keras_compile_fit(model_dir,
         optimizer=optimizer,
         loss=loss_fn,
         metrics=[fn() for fn in metric_fn],
-        experimental_steps_per_execution=steps_per_loop)
+        steps_per_execution=steps_per_loop)
 
     summary_dir = os.path.join(model_dir, 'summaries')
     summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)

official/nlp/nhnet/trainer.py

@@ -144,7 +144,7 @@ def train(params, strategy, dataset=None):
 
     trainer.compile(
         optimizer=opt,
-        experimental_steps_per_execution=FLAGS.steps_per_loop)
+        steps_per_execution=FLAGS.steps_per_loop)
     summary_dir = os.path.join(FLAGS.model_dir, "summaries")
     summary_callback = tf.keras.callbacks.TensorBoard(
         summary_dir, update_freq=max(100, FLAGS.steps_per_loop))

official/vision/image_classification/classifier_trainer.py

@@ -352,7 +352,7 @@ def train_and_eval(
         optimizer=optimizer,
         loss=loss_obj,
         metrics=metrics,
-        experimental_steps_per_execution=steps_per_loop)
+        steps_per_execution=steps_per_loop)
 
     initial_epoch = 0
     if params.train.resume_checkpoint:

official/vision/image_classification/configs/base_configs.py

@@ -75,7 +75,7 @@ class TrainConfig(hyperparams.Config):
     callbacks: An instance of CallbacksConfig.
     metrics: An instance of MetricsConfig.
     tensorboard: An instance of TensorboardConfig.
-    set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to
+    set_epoch_loop: Whether or not to set `steps_per_execution` to
       equal the number of training steps in `model.compile`. This reduces the
       number of callbacks run per epoch which significantly improves end-to-end
       TPU training time.