diff --git a/dan/manager/training.py b/dan/manager/training.py
index f5779a14fb220465773c30ea82ebea3a3e1498ac..67b9f6bf1b38c8dce424c94275676e26403c5248 100644
--- a/dan/manager/training.py
+++ b/dan/manager/training.py
@@ -43,17 +43,6 @@ class GenericTrainingManager:
         self.paths = None
         self.latest_step = 0
         self.latest_epoch = -1
-        self.latest_batch = 0
-        self.total_batch = 0
-        self.grad_acc_step = 0
-        self.latest_train_metrics = dict()
-        self.latest_valid_metrics = dict()
-        self.curriculum_info = dict()
-        self.curriculum_info["latest_valid_metrics"] = dict()
-        self.phase = None
-        self.max_mem_usage_by_epoch = list()
-        self.losses = list()
-        self.lr_values = list()
 
         self.scaler = None
 
@@ -512,7 +501,7 @@ class GenericTrainingManager:
     def backward_loss(self, loss, retain_graph=False):
         self.scaler.scale(loss).backward(retain_graph=retain_graph)
 
-    def step_optimizers(self, increment_step=True, names=None):
+    def step_optimizers(self, names=None):
         for model_name in self.optimizers:
             if names and model_name not in names:
                 continue
@@ -559,11 +548,6 @@ class GenericTrainingManager:
             self.init_curriculum()
         # perform epochs
         for num_epoch in range(self.latest_epoch + 1, nb_epochs):
-            self.dataset.train_dataset.training_info = {
-                "epoch": self.latest_epoch,
-                "step": self.latest_step,
-            }
-            self.phase = "train"
             # Check maximum training time stop condition
             if (
                 self.params["training_params"]["max_training_time"]
@@ -588,8 +572,6 @@ class GenericTrainingManager:
                 pbar.set_description("EPOCH {}/{}".format(num_epoch, nb_epochs))
                 # iterates over mini-batch data
                 for ind_batch, batch_data in enumerate(self.dataset.train_loader):
-                    self.latest_batch = ind_batch + 1
-                    self.total_batch += 1
                     # train on batch data and compute metrics
                     batch_values = self.train_batch(batch_data, metric_names)
                     batch_metrics = self.metric_manager["train"].compute_metrics(
@@ -651,7 +633,6 @@ class GenericTrainingManager:
                         display_values[key],
                         num_epoch,
                     )
-            self.latest_train_metrics = display_values
 
             # evaluate and compute metrics for valid sets
             if (
@@ -664,7 +645,6 @@ class GenericTrainingManager:
                     eval_values = self.evaluate(
                         valid_set_name, mlflow_logging=mlflow_logging
                     )
-                    self.latest_valid_metrics = eval_values
                     # log valid metrics in tensorboard file
                     if self.is_master:
                         for key in eval_values.keys():
@@ -716,7 +696,6 @@ class GenericTrainingManager:
         """
         Main loop for validation
         """
-        self.phase = "eval"
         loader = self.dataset.valid_loaders[set_name]
         # Set models in eval mode
         for model_name in self.models.keys():
@@ -733,7 +712,6 @@ class GenericTrainingManager:
             with torch.no_grad():
                 # iterate over batch data
                 for ind_batch, batch_data in enumerate(loader):
-                    self.latest_batch = ind_batch + 1
                     # eval batch data and compute metrics
                     batch_values = self.evaluate_batch(batch_data, metric_names)
                     batch_metrics = self.metric_manager[set_name].compute_metrics(
@@ -767,7 +745,6 @@ class GenericTrainingManager:
         """
         Main loop for evaluation
         """
-        self.phase = "predict"
         metric_names = metric_names.copy()
         self.dataset.generate_test_loader(custom_name, sets_list)
         loader = self.dataset.test_loaders[custom_name]
@@ -785,7 +762,6 @@ class GenericTrainingManager:
             with torch.no_grad():
                 for ind_batch, batch_data in enumerate(loader):
                     # iterates over batch data
-                    self.latest_batch = ind_batch + 1
                     # eval batch data and compute metrics
                     batch_values = self.evaluate_batch(batch_data, metric_names)
                     batch_metrics = self.metric_manager[custom_name].compute_metrics(
@@ -903,10 +879,6 @@ class GenericTrainingManager:
         dist.all_gather(res, tensor)
         return list(torch.cat(res, dim=0).flatten().cpu().numpy())
 
-    @staticmethod
-    def cleanup():
-        dist.destroy_process_group()
-
     def train_batch(self, batch_data, metric_names):
         raise NotImplementedError
 
@@ -916,20 +888,6 @@ class GenericTrainingManager:
     def init_curriculum(self):
         raise NotImplementedError
 
-    def update_curriculum(self):
-        raise NotImplementedError
-
-    def add_checkpoint_info(self, load_mode="last", **kwargs):
-        for filename in os.listdir(self.paths["checkpoints"]):
-            if load_mode in filename:
-                checkpoint_path = os.path.join(self.paths["checkpoints"], filename)
-                checkpoint = torch.load(checkpoint_path)
-                for key in kwargs.keys():
-                    checkpoint[key] = kwargs[key]
-                torch.save(checkpoint, checkpoint_path)
-            return
-        self.save_model(self.latest_epoch, "last")
-
     def load_save_info(self, info_dict):
         """
         Load curriculum info from saved model info