add options save_checkpoints_secs and keep_checkpoint_every_n_hours

tensor2tensor/utils/trainer_utils.py

@@ -70,6 +70,13 @@
                      "How many recent checkpoints to keep.")
 flags.DEFINE_bool("experimental_optimize_placement", False,
                   "Optimize ops placement with experimental session options.")
+flags.DEFINE_integer("keep_checkpoint_every_n_hours", 10000,
+                     "Number of hours between each checkpoint to be saved. "
+                     "The default value of 10,000 hours effectively disables the feature.")
+flags.DEFINE_integer("save_checkpoints_secs", 0,
+                  "Save checkpoints every this many seconds. "
+                  "Default=0 means let tensorflow.contrib.learn.python.learn decide, "
+                  "which is currently equivalent to 600, i.e. 10 minutes.")
 
 # Distributed training flags
 flags.DEFINE_string("master", "", "Address of TensorFlow master.")
@@ -203,7 +210,9 @@ def create_experiment_components(hparams, output_dir, data_dir, model_name):
           model_dir=output_dir,
           gpu_memory_fraction=FLAGS.worker_gpu_memory_fraction,
           session_config=session_config(),
-          keep_checkpoint_max=FLAGS.keep_checkpoint_max))
+          keep_checkpoint_max=FLAGS.keep_checkpoint_max,
+          keep_checkpoint_every_n_hours=FLAGS.keep_checkpoint_every_n_hours,
+          save_checkpoints_secs=FLAGS.save_checkpoints_secs,))
   # Store the hparams in the estimator as well
   estimator.hparams = hparams
   return estimator, {