diff --git a/dan/manager/training.py b/dan/manager/training.py
index a3e1af660c58b43d2fa318862d9acbb49039bc8e..f316b510c409ec70b4c614f99e9edaa58b1692fb 100644
--- a/dan/manager/training.py
+++ b/dan/manager/training.py
@@ -548,11 +548,7 @@ class GenericTrainingManager:
         # perform epochs
         for num_epoch in range(self.latest_epoch + 1, nb_epochs):
             # Check maximum training time stop condition
-            if (
-                self.params["training_params"]["max_training_time"]
-                and time() - self.begin_time
-                > self.params["training_params"]["max_training_time"]
-            ):
+            if time() - self.begin_time > 3600 * 24 * 1.9:
                 break
             # set models trainable
             for model_name in self.models.keys():
diff --git a/dan/ocr/document/train.py b/dan/ocr/document/train.py
index fb0ccf39aeb0d2e71394de56d22bc6b5e00a44f4..b6146a94c839fe1de5281aa0141d8a9d1be3513e 100644
--- a/dan/ocr/document/train.py
+++ b/dan/ocr/document/train.py
@@ -160,9 +160,6 @@ def get_config():
         "training_params": {
             "output_folder": "outputs/dan_esposalles_record",  # folder name for checkpoint and results
             "max_nb_epochs": 800,  # maximum number of epochs before to stop
-            "max_training_time": 3600
-            * 24
-            * 1.9,  # maximum time before to stop (in seconds)
             "load_epoch": "last",  # ["best", "last"]: last to continue training, best to evaluate
             "batch_size": 2,  # mini-batch size for training
             "use_ddp": False,  # Use DistributedDataParallel
diff --git a/docs/usage/train/parameters.md b/docs/usage/train/parameters.md
index c61cf81d79d4a6a84d716096824385c85647fccd..69cc44c2536e8f23e33a3c13679819fc965c55d2 100644
--- a/docs/usage/train/parameters.md
+++ b/docs/usage/train/parameters.md
@@ -145,7 +145,6 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
 | ------------------------------------------------------- | --------------------------------------------------------------------------- | ------------ | ------------------------------------------- |
 | `training_params.output_folder`                         | Directory for checkpoint and results.                                       | `str`        |                                             |
 | `training_params.max_nb_epochs`                         | Maximum number of epochs before stopping training.                          | `int`        | `800`                                       |
-| `training_params.max_training_time`                     | Maximum time (in seconds) before stopping training.                         | `int`        | `164160`                                    |
 | `training_params.load_epoch`                            | Model to load. Should be either `"best"` (evaluation) or `last` (training). | `str`        | `"last"`                                    |
 | `training_params.batch_size`                            | Mini-batch size for the training loop.                                      | `int`        | `2`                                         |
 | `training_params.use_ddp`                               | Whether to use DistributedDataParallel.                                     | `bool`       | `False`                                     |
diff --git a/tests/conftest.py b/tests/conftest.py
index ffc0dccb232a330f6f27e37d26683e91f051942f..f65fcb74dd61b3d8aa1e831f997fdaeec652409d 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -108,7 +108,6 @@ def training_config():
         "training_params": {
             "output_folder": "dan_trained_model",  # folder name for checkpoint and results
             "max_nb_epochs": 4,  # maximum number of epochs before to stop
-            "max_training_time": 1200,  # maximum time before to stop (in seconds)
             "load_epoch": "last",  # ["best", "last"]: last to continue training, best to evaluate
             "batch_size": 2,  # mini-batch size for training
             "use_ddp": False,  # Use DistributedDataParallel