Internal change

PiperOrigin-RevId: 331861386

official/core/train_lib.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 """TFM common training driver library."""
-
+# pytype: disable=attribute-error
 import copy
 import json
 import os
@@ -219,9 +219,14 @@ def run_experiment(distribution_strategy: tf.distribute.Strategy,
     elif mode == 'eval':
       controller.evaluate(steps=params.trainer.validation_steps)
     elif mode == 'continuous_eval':
+      def timeout_fn():
+        if trainer.global_step.numpy() >= params.trainer.train_steps:
+          return True
+        return False
       controller.evaluate_continuously(
           steps=params.trainer.validation_steps,
-          timeout=params.trainer.continuous_eval_timeout)
+          timeout=params.trainer.continuous_eval_timeout,
+          timeout_fn=timeout_fn)
     else:
       raise NotImplementedError('The mode is not implemented: %s' % mode)
 

official/core/train_lib_test.py

@@ -49,6 +49,7 @@ class TrainTest(tf.test.TestCase, parameterized.TestCase):
             'train_steps': 10,
             'validation_steps': 5,
             'validation_interval': 10,
+            'continuous_eval_timeout': 1,
             'optimizer_config': {
                 'optimizer': {
                     'type': 'sgd',
@@ -97,9 +98,19 @@ class TrainTest(tf.test.TestCase, parameterized.TestCase):
       self.assertEmpty(logs)
     self.assertNotEmpty(
         tf.io.gfile.glob(os.path.join(model_dir, 'params.yaml')))
-    if flag_mode != 'eval':
+    if flag_mode == 'eval':
+      return
     self.assertNotEmpty(
         tf.io.gfile.glob(os.path.join(model_dir, 'checkpoint')))
+    # Tests continuous evaluation.
+    _, logs = train_lib.run_experiment(
+        distribution_strategy=distribution_strategy,
+        task=task,
+        mode='continuous_eval',
+        params=params,
+        model_dir=model_dir,
+        run_post_eval=run_post_eval)
+    print(logs)
 
 
 if __name__ == '__main__':

official/core/train_utils.py

@@ -38,7 +38,7 @@ def create_trainer(
     model_dir: str,
     train: bool,
     evaluate: bool,
-    checkpoint_exporter: Any = None):
+    checkpoint_exporter: Any = None) -> base_trainer.Trainer:
   """Create trainer."""
   del model_dir
   logging.info('Running default trainer.')

official/modeling/hyperparams/config_definitions.py

@@ -189,7 +189,7 @@ class TrainerConfig(base_config.Config):
     continuous_eval_timeout: maximum number of seconds to wait between
       checkpoints, if set to None, continuous eval will wait indefinitely. This
       is only used continuous_train_and_eval and continuous_eval modes. Default
-      value is 24 hrs.
+      value is 1 hrs.
     train_steps: number of train steps.
     validation_steps: number of eval steps. If `None`, the entire eval dataset
       is used.
@@ -218,7 +218,7 @@ class TrainerConfig(base_config.Config):
   checkpoint_interval: int = 1000
   # Checkpoint manager.
   max_to_keep: int = 5
-  continuous_eval_timeout: int = 24 * 60 * 60
+  continuous_eval_timeout: int = 60 * 60
   # Train/Eval routines.
   train_steps: int = 0
   validation_steps: Optional[int] = None