diff --git a/foundations/hparams.py b/foundations/hparams.py
index e513c533..6014cc95 100644
--- a/foundations/hparams.py
+++ b/foundations/hparams.py
@@ -160,6 +160,7 @@ class TrainingHparams(Hparams):
     warmup_steps: str = None
     weight_decay: float = None
     apex_fp16: bool = False
+    save_every_n_epochs: int = None
 
     _name: str = 'Training Hyperparameters'
     _description: str = 'Hyperparameters that determine how the model is trained.'
@@ -175,6 +176,7 @@ class TrainingHparams(Hparams):
     _warmup_steps: str = "Steps of linear lr warmup at the start of training as epochs ('20ep') or iterations ('800it')"
     _weight_decay: str = 'The L2 penalty to apply to the weights.'
     _apex_fp16: bool = 'Whether to train the model in float16 using the NVIDIA Apex library.'
+    _save_every_n_epochs: int = 'Save weights epN_it0 every time epoch N is divisible by this value (default None)'
 
 
 @dataclass
diff --git a/training/runner.py b/training/runner.py
index 7698f747..5ee69456 100644
--- a/training/runner.py
+++ b/training/runner.py
@@ -20,6 +20,7 @@ class TrainingRunner(Runner):
     desc: TrainingDesc
     verbose: bool = True
     evaluate_every_epoch: bool = True
+    save_every_n_epochs: int = None
 
     @staticmethod
     def description():
@@ -33,7 +34,7 @@ def add_args(parser: argparse.ArgumentParser) -> None:
     @staticmethod
     def create_from_args(args: argparse.Namespace) -> 'TrainingRunner':
         return TrainingRunner(args.replicate, TrainingDesc.create_from_args(args),
-                              not args.quiet, not args.evaluate_only_at_end)
+                              not args.quiet, not args.evaluate_only_at_end, args.save_every_n_epochs)
 
     def display_output_location(self):
         print(self.desc.run_path(self.replicate))
@@ -46,4 +47,5 @@ def run(self):
         self.desc.save(self.desc.run_path(self.replicate))
         train.standard_train(
             models.registry.get(self.desc.model_hparams), self.desc.run_path(self.replicate),
-            self.desc.dataset_hparams, self.desc.training_hparams, evaluate_every_epoch=self.evaluate_every_epoch)
+            self.desc.dataset_hparams, self.desc.training_hparams, evaluate_every_epoch=self.evaluate_every_epoch,
+            save_every_n_epochs=self.save_every_n_epochs)
diff --git a/training/standard_callbacks.py b/training/standard_callbacks.py
index 3657d6b5..4633d37e 100644
--- a/training/standard_callbacks.py
+++ b/training/standard_callbacks.py
@@ -110,10 +110,19 @@ def modified_callback(output_location, step, model, optimizer, logger):
     return modified_callback
 
 
+def run_every_n_epochs(callback, n, offset=0):
+    def modified_callback(output_location, step, model, optimizer, logger):
+        if step.it != 0 or (step.ep - offset) % n != 0:
+            return
+        callback(output_location, step, model, optimizer, logger)
+    return modified_callback
+
+
 # The standard set of callbacks that should be used for a normal training run.
 def standard_callbacks(training_hparams: hparams.TrainingHparams, train_set_loader: DataLoader,
                        test_set_loader: DataLoader, eval_on_train: bool = False, verbose: bool = True,
-                       start_step: Step = None, evaluate_every_epoch: bool = True):
+                       start_step: Step = None, evaluate_every_epoch: bool = True,
+                       save_every_n_epochs: int = None):
     start = start_step or Step.zero(train_set_loader.iterations_per_epoch)
     end = Step.from_str(training_hparams.training_steps, train_set_loader.iterations_per_epoch)
     test_eval_callback = create_eval_callback('test', test_set_loader, verbose=verbose)
@@ -131,6 +140,9 @@ def standard_callbacks(training_hparams: hparams.TrainingHparams, train_set_load
     if evaluate_every_epoch: result = [run_every_epoch(test_eval_callback)] + result
     elif verbose: result.append(run_every_epoch(create_timekeeper_callback()))
 
+    # Save model weights every N epochs if requested
+    if save_every_n_epochs is not None: result.append(run_every_n_epochs(save_model, n=save_every_n_epochs, offset=0))
+
     # Ensure that testing occurs at least at the beginning and end of training.
     if start.it != 0 or not evaluate_every_epoch: result = [run_at_step(start, test_eval_callback)] + result
     if end.it != 0 or not evaluate_every_epoch: result = [run_at_step(end, test_eval_callback)] + result
diff --git a/training/train.py b/training/train.py
index 99951b46..5b92ffda 100644
--- a/training/train.py
+++ b/training/train.py
@@ -138,7 +138,8 @@ def standard_train(
   training_hparams: hparams.TrainingHparams,
   start_step: Step = None,
   verbose: bool = True,
-  evaluate_every_epoch: bool = True
+  evaluate_every_epoch: bool = True,
+  save_every_n_epochs=None,
 ):
     """Train using the standard callbacks according to the provided hparams."""
 
@@ -152,5 +153,6 @@ def standard_train(
     test_loader = datasets.registry.get(dataset_hparams, train=False)
     callbacks = standard_callbacks.standard_callbacks(
         training_hparams, train_loader, test_loader, start_step=start_step,
-        verbose=verbose, evaluate_every_epoch=evaluate_every_epoch)
+        verbose=verbose, evaluate_every_epoch=evaluate_every_epoch,
+        save_every_n_epochs=save_every_n_epochs)
     train(training_hparams, model, train_loader, output_location, callbacks, start_step=start_step)